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The steroid hormone ecdysone and its receptor ( EcR ) play critical roles in orchestrating developmental transitions in arthropods . However , the mechanism by which EcR integrates nutritional and developmental cues to correctly activate transcription remains poorly understood . Here , we show that EcR-dependent transcription , and thus , developmental timing in Drosophila , is regulated by CDK8 and its regulatory partner Cyclin C ( CycC ) , and the level of CDK8 is affected by nutrient availability . We observed that cdk8 and cycC mutants resemble EcR mutants and EcR-target genes are systematically down-regulated in both mutants . Indeed , the ability of the EcR-Ultraspiracle ( USP ) heterodimer to bind to polytene chromosomes and the promoters of EcR target genes is also diminished . Mass spectrometry analysis of proteins that co-immunoprecipitate with EcR and USP identified multiple Mediator subunits , including CDK8 and CycC . Consistently , CDK8-CycC interacts with EcR-USP in vivo; in particular , CDK8 and Med14 can directly interact with the AF1 domain of EcR . These results suggest that CDK8-CycC may serve as transcriptional cofactors for EcR-dependent transcription . During the larval–pupal transition , the levels of CDK8 protein positively correlate with EcR and USP levels , but inversely correlate with the activity of sterol regulatory element binding protein ( SREBP ) , the master regulator of intracellular lipid homeostasis . Likewise , starvation of early third instar larvae precociously increases the levels of CDK8 , EcR and USP , yet down-regulates SREBP activity . Conversely , refeeding the starved larvae strongly reduces CDK8 levels but increases SREBP activity . Importantly , these changes correlate with the timing for the larval–pupal transition . Taken together , these results suggest that CDK8-CycC links nutrient intake to developmental transitions ( EcR activity ) and fat metabolism ( SREBP activity ) during the larval–pupal transition .
In animals , the amount of juvenile growth is controlled by the coordinated timing of maturation and growth rate , which are strongly influenced by the environmental factors such as nutrient availability [1 , 2] . This is particularly evident in arthropods , such as insects , arachnids and crustaceans , which account for over 80% of all described animal species on earth . Characterized by their jointed limbs and exoskeletons , juvenile arthropods have to replace their rigid cuticles periodically by molting . In insects , the larval–larval and larval–pupal transitions are controlled by the interplay between juvenile hormone ( JH ) and steroid hormone ecdysone [3–7] . Drosophila has been a powerful system for deciphering the conserved mechanisms that regulate hormone signaling , sugar and lipid homeostasis , and the molecular mechanisms underlying the nutritional regulation of development [1 , 2 , 8–11] . In Drosophila , all growth occurs during the larval stage when larvae constantly feed , and as a result their body mass increases approximately 200-fold within 4 d , largely due to de novo lipogenesis [12] . At the end of the third instar , pulses of ecdysone , combined with a low level of JH , trigger the larval–pupal transition and metamorphosis [3 , 6 , 13] . During this transition , feeding is inhibited , and after pupariation , feeding is impossible , thus the larval–pupal transition marks when energy metabolism is switched from energy storage by lipogenesis in larvae to energy utilization by lipolysis in pupae . The molecular mechanisms of ecdysone-regulated metamorphosis and developmental timing have been studied extensively in Drosophila [3 , 5 , 14 , 15] . Ecdysone binds to the Ecdysone Receptor ( EcR ) , which heterodimerizes with Ultraspiracle ( USP ) , an ortholog of the vertebrate Retinoid X Receptor ( RXR ) [16–21] . By activating the expression of genes whose products are required for metamorphosis , ecdysone and EcR-USP are essential for the reorganization of flies’ body plans before emerging from pupal cases as adults . Despite the tremendous progress in our understanding of the physiological and developmental effects of EcR-USP signaling , the molecular mechanism of how the EcR-USP transcription factor interacts with the general transcription machinery of RNA polymerase II ( Pol II ) and stimulates its target gene expression remains mysterious . EcR is colocalized with Pol II in Bradysia hygida and Chironomus tentans [22 , 23] . Although a number of proteins , such as Alien , Bonus , Diabetes and Obesity Regulated ( dDOR ) , dDEK , Hsc70 , Hsp90 , Rigor mortis ( Rig ) , Smrter ( Smr ) , Taiman , and Trithorax-related ( TRR ) , have been identified as regulators or cofactors of EcR-mediated gene expression [13 , 24–32] , it is unknown how these proteins communicate with the general transcription machinery and whether additional cofactors are involved in EcR-mediated gene expression . In addition , it remains poorly understood how EcR activates transcription correctly after integrating nutritional and developmental cues . The multisubunit Mediator complex serves as a molecular bridge between transcriptional factors and the core transcriptional machinery , and is thought to regulate most ( if not all ) of Pol II-dependent transcription [33–40] . Biochemical analyses have identified two major forms of the Mediator complexes: the large and the small Mediator complexes . In addition to a separable “CDK8 submodule” , the large Mediator complex contains all but one ( MED26 ) of the subunits of the small Mediator complex [36 , 38 , 41] . The CDK8 submodule is composed of MED12 , MED13 , CDK8 , and CycC . CDK8 is the only enzymatic subunit of the Mediator complex , and CDK8 can both activate and repress transcription depending on the transcription factors with which it interacts [37 , 42] . Amplification and mutation of genes encoding CDK8 , CycC , and other subunits of Mediator complex have been identified in a variety of human cancers [43 , 44] , however , the function and regulation of CDK8-CycC in non-disease conditions remain poorly understood . CDK8 and CycC are highly conserved in eukaryotes [45] , thus analysis of the functional regulation of CDK8-CycC in Drosophila is a viable approach to understand their activities . Previously , we have shown that CDK8-CycC negatively regulates the stability of sterol regulatory element-binding proteins ( SREBPs ) by directly phosphorylating a conserved threonine residue [46] . We now report that CDK8-CycC also regulates developmental timing in Drosophila by linking nutrient intake with EcR-activated gene expression . We show that homozygous cdk8 or cycC mutants resemble EcR mutants in both pupal morphology and retarded developmental transitions . Despite the elevation of both EcR and USP proteins in cdk8 or cycC mutants , genome-wide gene expression profiling analyses reveal systematic down-regulation of EcR-target genes , suggesting the CDK8-CycC defect lies between the receptor complex and transcriptional activation . CDK8-CycC is required for EcR-USP transcription factor binding to EcR target genes . Mass spectrometry analysis for proteins that co-immunoprecipitate with EcR and USP has identified multiple Mediator subunits , including CDK8 and CycC , and our yeast two-hybrid assays have revealed that CDK8 and Med14 can directly interact with the EcR-AF1 domain . Furthermore , the dynamic changes of CDK8 , EcR , USP , and SREBP correlated with the fundamental roles of SREBP in regulating lipogenesis and EcR-USP in regulating metamorphosis during the larval–pupal transition . Importantly , we show that starving the early third instar larvae causes precocious increase of CDK8 , EcR and USP proteins , as well as premature inactivation of SREBP; whereas refeeding of the starved larvae reduces CDK8 , EcR , and USP proteins , but potently stimulates SREBP activity . These results suggest a dual role of CDK8-CycC , linking nutrient intake to de novo lipogenesis ( by inhibiting SREBP ) and developmental signaling ( by regulating EcR-dependent transcription ) during the larval–pupal transition .
The Drosophila cdk8 and cycC genes were originally identified based on the function and sequence conservation to their yeast and human orthologs [47–49] . cdk8K185 and cycCY5 are null alleles that delete part of cdk8 ( 882 bp ) and all of cycC ( 2 , 733 bp ) , respectively , and the homozygous mutants are both prepupal lethal [50] . Mutant animals are able to develop to prepupae , likely due to maternally loaded CDK8 and CycC mRNAs and proteins , because embryos derived from the cycCY5 germline clones are smaller and are embryonic lethal without proper denticle formation ( S1 Fig ) . In contrast to the wild-type pupae ( Fig 1A ) , 96% of cdk8K185 ( Fig 1B ) and 97% of cycCY5 ( Fig 1C ) homozygous mutants fail to evert their anterior spiracles ( quantified in S2A Fig ) , and prepupae of both mutants are partially separated from their pupal cases ( arrows in Fig 1B and 1C ) . In addition , pupariation is delayed by about 2 to 3 d in the cdk8 and cycC mutants ( Fig 1G and 1H ) . To investigate the effects of CDK8-CycC on developmental timing , we first analyzed the cdk8-cycC double mutant animals by genetically combining the cdk8K185 and cycCY5 null alleles in the same organism . The phenotypes in the cdk8-cycC double mutant animals were similar to cdk8 or cycC single mutants , including pupal morphology ( Figs 1D and S2A ) , delayed pupariation ( Fig 1G and 1H ) , and prepupal lethality . The levels of cdk8 and cycC mRNA ( S2B Fig ) and their protein products ( S2C Fig ) are diminished in cdk8 or cycC single and double mutant larvae when assayed at the third instar larval stage ( L3 ) . The protein level of CycC is significantly reduced in cdk8 and cycC mutants , but the level of CDK8 is not affected in cycC mutants ( S2C Fig ) , thus the stability of CycC is dependent on CDK8 but not vice versa . To validate that the loss of CDK8-CycC causes the defects in pupal morphology and development , we tested whether the mutant phenotypes could be rescued by expression of wild-type CDK8 or CycC . Since CDK8 and CycC form the CDK8 submodule with MED12 and MED13 in a 1:1:1:1 stoichiometry [51] , proper dosage of these four subunits is critical for the formation and function of a viable CDK8 sub-module . To ensure proper expression levels and patterns , we generated transgenic flies using genomic fragments of cdk8 and cycC loci with EGFP tags at their C-termini ( S3 Fig ) . The X-ray crystal structure of human CDK8-CycC complex demonstrates that the C-termini of CDK8 and CycC are not involved in their interaction [52] , thus epitope tags fused to C-termini were expected to avoid functional disruption of the CDK8-CycC complex . These constructs were transposed to chromosome 2; the transgenic flies are referred to as “cdk8+-EGFP” or “cycC+-EGFP” for simplicity . We genetically combined these transgenes with cdk8 or cycC null alleles , thus CDK8 or CycC proteins were tagged with EGFP in the rescued animals ( “w1118; cdk8+-EGFP; cdk8K185” for cdk8-rescued animals , and “w1118; cycC+-EGFP; cycCY5” for cycC-rescued animals ) . The genotypes of the rescued adult animals were validated by PCR analysis ( S3 Fig ) . Importantly , these transgenic lines rescue both the pupal morphology ( Figs 1E and 1F , and S2A ) and developmental timing ( Fig 1G and 1H ) . The rescued animals are no longer prepupal lethal , and they emerge as adult flies . These observations indicate that CDK8 and CycC are required for proper developmental transitions in Drosophila . The phenotypes of cdk8 and cycC mutants ( Fig 1 ) are reminiscent of loss-of-function alleles of EcR-B1 , the major EcR isoform that controls the larval-to-pupal transition [53] . The EcR gene encodes three isoforms ( EcR-A , -B1 , and-B2 ) that are expressed in tissue- and developmental stage-specific manners [21 , 53 , 54] . To test the possibility that CDK8-CycC and EcR-B1 regulate similar molecular events that control the developmental transitions , we first examined whether the expression of EcR target genes was affected in cdk8 or cycC mutants . By mining the microarray data that we published previously [46] , we analyzed the mRNA levels of 67 genes whose products are related to the ecdysone and JH activities as reported in the literature ( S1 Table ) [3 , 6] . In cdk8 or cycC mutants , the mRNA levels of 33 of these genes are significantly decreased whereas mRNA levels of 10 of these genes are increased more than 1 . 5-fold compared to the control . Most of the down-regulated genes are EcR-activated genes , while most of the up-regulated genes respond to JH activity ( Fig 2A and S1 Table ) . We used qRT-PCR assays to verify the levels of several well-characterized direct target genes of EcR , such as broad , E74 , E75 , E78 , Hsp27 ( Heat shock protein 27 ) , ImpE2 ( Ecdysone-inducible gene E2 ) , Sgs1 ( Salivary gland secretion 1 ) , and Sgs5 [3 , 6] . As shown in Fig 2B , the expression of these EcR-activated genes was significantly reduced in L3 wandering cdk8 and cycC mutants . There was a small reduction of EcR mRNA levels , but a mild increase of usp mRNA levels , in the cdk8 and cycC mutants ( Fig 2C ) . EcR normally represses the expression of the mid-prepupal gene βFtz-F1 during the larval stage [15 , 55]; however , the expression of βFtz-F1 was dramatically increased in the cdk8 and cycC mutant larvae ( Fig 2C ) , suggesting that the function of EcR is disrupted in the cdk8 and cycC mutants . Likewise , the levels of jheh1 ( JH-epoxide hydrolase ) and JhI-26 ( JH-inducible protein 26 ) were significantly increased in cdk8 and cycC mutants ( Fig 2C ) . JHEH1 is involved in the catabolic processing of JH , while the expression of JhI-26 is induced by either methoprene or JH III [56] . Therefore , the expression of both EcR and JH-regulated genes was deregulated in cdk8 and cycC mutants , consistent with the developmental retardation phenotype ( Fig 1G and 1H ) . To test whether ecdysone-induced EcR target gene expression was generally compromised in cdk8 or cycC mutants , we analyzed the effect of cdk8 or cycC mutation on the expression of the multimerized hsp27 EcRE ( ecdysone response element ) -lacZ reporter [21 , 54] . In response to the treatment of 20-hydroxyecdysone ( 20E ) , the most biologically potent EcR ligand , β-galactosidase activity was induced in the control salivary gland cells as expected ( Fig 2D versus 2D’ ) . However , this response was significantly compromised in salivary glands from the cdk8 ( Fig 2E’ ) and cycC ( Fig 2F’ ) homozygous mutants; the glands from the same animals ( Fig 2E and 2F , respectively ) were used as the controls . These results were consistent with reduced expression of EcR target genes in cdk8 or cycC mutants ( Fig 2A and 2B ) . Both the ecdysone ligand and the EcR-USP transcription factor complex are required for the expression of EcR target genes . 20E directly binds to the ligand-binding domain ( LBD ) of EcR , which then activates EcR target gene expression [3 , 6 , 13] . Therefore , down-regulated expression of EcR target genes in cdk8 and cycC mutants may be due to defective biosynthesis of 20E , or defects in EcR-activated transcription . To test whether the biosynthesis of 20E is defective in cdk8 and cycC mutants , we analyzed the expression of enzymes that are required for the biosynthesis of 20E , such as nvd ( neverland , encoding an oxygenase-like protein ) and a family of cytochrome P450 enzymes including dib ( disembodied ) , phm ( phantom ) , sad ( shadow ) , shd ( shade ) , spo ( spook ) , and spok ( spookier ) , collectively known as the Halloween genes [57–59] . The expressions of sad and spok were decreased in the cdk8 and cycC mutants , but the expression of nvd and other Halloween genes were not significantly affected ( Fig 3A ) . In addition , we analyzed the mRNA levels of cyp18a , which encodes a cytochrome P450 enzyme involved in degradation of 20E [60] , and a few genes encoding factors involved in regulating the expression of the Halloween genes , such as mld , kni , and vvl [61] . As shown in Fig 3B , no obvious changes of these genes were observed in both cdk8 and cycC mutants . Nevertheless , reduction of sad and spok in cdk8 and cycC mutants indicates that the biosynthesis of ecdysone may be defective in the cdk8 and cycC mutants . Next , we measured the levels of ecdysteroids in cdk8 and cycC mutants from early L3 larval stage to white prepupal ( WPP ) stage . Compared to the control , the levels of ecdysteroids are significantly lower in cdk8 mutant animals during the wandering L3 and WPP stages than the control , while the levels of ecdysteroids are lower in cycC mutants only during the late wandering stage ( Fig 3C ) . Nevertheless , the levels of ecdysteroids are continuously increased from early L3 to WPP stage in both cdk8 and cycC mutants , indicating that the biosynthesis of ecdysteroids is compromised , but not completely abolished , in these mutants . To further determine whether the developmental retardation in cdk8 and cycC mutants is caused by impaired 20E biosynthesis , we fed the homozygous mutants with fly food supplemented with 200 μM of 20E , which is an established approach used to examine whether developmental defects are caused by mutations that disrupt biosynthesis of ecdysteroid [62–64] . However , the defective prepupal morphology of the cdk8 and cycC mutants was not rescued ( Fig 3D ) . In contrast , food supplement of 20E rescued animals with prothoracic gland ( PG ) cells ablated by PG-specific expression of reaper gene ( Fig 3E ) , which triggers apoptosis , or animals with spok specifically depleted in the PG ( Fig 3F ) using a PG-specific driver ( phm-Gal4 ) . Therefore , supplement of 20E to larvae depleted in factors required for ecdysone biosynthesis rescues their developmental delay [61] . Consistent with reduced ecdysteroids level in cdk8 mutants ( Fig 3C ) , feeding cdk8 mutant with 200 μM of 20E in food had a mild effect on the time from egg deposition to pupariation compared to the control ( Fig 3G ) . However , feeding cycC mutants with 20E had no effect on their developmental delay , also consistent with the weaker effect of cycC mutation on ecdysteroids levels ( Fig 3C and 3G ) . Importantly , the larval–pupal transition is still significantly retarded in cdk8 or cycC mutants , even when fed with 20E ( Fig 3G ) . Other concentrations of 20E in food , ranging from 2 μM to 2mM , also failed to rescue the developmental defects of the cdk8 and cycC mutants ( S4 Fig ) . These results suggest that defective biosynthesis of 20E alone is not sufficient to explain the developmental defects of cdk8 or cycC mutants , which is consistent with the strongly compromised effects of 20E on EcRE-lacZ expression in salivary gland cells in cdk8 and cycC mutants ( Fig 2E and 2E’ and 2F and 2F’ ) . Considering that CDK8 and CycC function as subunits of the transcription cofactor Mediator complex , which is known to regulate the transcriptional activity of several nuclear hormone receptors in mammals [65 , 66] , the most likely scenario is that the cdk8 and cycC mutants are defective in the regulation of EcR-dependent gene expression in peripheral tissues , in addition to impairing ecdysone biosynthesis in the PG . To understand how loss of CDK8 or CycC reduced EcR-target gene expression , we tested whether the protein levels of EcR and USP were affected in cdk8 and cycC mutants . Since the major defects occurred during the larval–pupal transition , we first analyzed the expression of EcR , USP , CDK8 , and CycC in wild-type larval and pupal extracts from the early L3 larvae ( 84hr AEL [after egg laying] ) , the late L3 wandering larvae ( 112hr AEL ) , at pupariation ( 0 hr APF [after puparium formation] ) , and pupae ( 72 hr APF ) . The level of EcR-B1 ( 105 kDa ) was low in the early L3 stage , but was significantly increased from the wandering to 72 hr APF stage ( Fig 4A ) , which lags the temporal expression profile of EcR-B mRNA [54 , 67] . The monoclonal antibody against USP ( AB11 ) recognizes two forms of USP: the 54 kDa full-length USP protein and the 48 kDa truncated USP that lacks the most N-terminal portion [68–70] . The truncated USP is proposed to derive from alternative usage of translation start sites or protease cleavage [68 , 69 , 71] . We detected both isoforms of USP , and observed that the levels of both isoforms , particularly the full-length USP , are significantly increased during the pupal stages ( Fig 4A ) . To facilitate biochemical analyses of USP ( see below ) , we generated a polyclonal USP antibody in guinea pig . Similar to the USP monoclonal antibody [72 , 73] , this new polyclonal antibody also specifically recognizes the two isoforms of USP ( S5A Fig ) , and reveals a similar expression pattern of USP during development ( Fig 4A ) . Interestingly , the protein levels of CDK8 and CycC are increased after the L3 wandering stage ( Fig 4A ) . Because the major changes in EcR , USP , CDK8 , and CycC levels occurred during the L3 larval to pupal transition ( Fig 4A ) , we performed our subsequent analyses of cdk8 and cycC mutants at the L3 wandering stage and the white prepupal stage . During the L3 wandering stage , the level of the full-length USP protein was significantly increased in cdk8 and cycC mutants ( Fig 4B ) . The level of EcR-B1 was increased in the mutants , particularly in the cdk8 mutant larvae ( Fig 4B ) . In white prepupae , the level of EcR-B1 was also significantly increased in cdk8 and cycC mutants , but the level of full-length USP was similar to the control ( Fig 4C ) . Thus , the total protein levels of EcR are higher in cdk8 and cycC mutants than in controls during the L3 wandering stage and the white prepupal stage , while the total protein levels of USP are higher in cdk8 and cycC mutants during the L3 wandering stage . Since the expression of the EcR-USP target genes is reduced in cdk8 and cycC mutants ( Fig 2 ) , we did not expect that protein levels of EcR and USP would be increased in mutants at the same stage ( Fig 4B and 4C ) . Thus we examined whether the subcellular distribution of EcR or USP were affected in cdk8 or cycC mutants by performing immunostaining of the salivary glands from the L3 wandering larvae . Both EcR ( S6A Fig ) and USP ( S6A’ Fig ) were localized in the nuclei in wild-type salivary glands . In cdk8 and cycC mutant glands , the levels of EcR ( S6B and S6C Fig ) and USP ( S6B’ and S6C’ Fig ) in both nucleus and cytoplasm appear to be slightly elevated compared to the control ( S6A and S6A’ Fig , respectively ) , which is supported by quantification of these images using ImageJ ( S6D and S6E Fig ) . These results suggest that the cytoplasmic levels of EcR or USP and nuclear levels of USP were increased in cdk8 and cycC mutants . Since immunostaining is not a robust quantitative approach , we fractionated nuclear soluble and cytoplasmic fractions of total proteins and analyzed the levels of EcR and USP by Western blot . The full-length USP protein was significantly increased in the nuclear soluble fraction of samples from cdk8 and cycC mutants during the late L3 wandering and WPP stages ( Fig 4D , left panel ) . In addition , the nuclear EcR levels were higher in cdk8 , and to a lesser extent , cycC mutants , than the control at the late L3 and WPP stages ( Fig 4D ) . In contrast , when analyzing the cytoplasmic fraction from the early L3 to WPP stage , we observed that USP level was a bit higher in cdk8 and cycC mutants than the control , but there was no obvious difference in cytoplasmic EcR levels ( Fig 4D , right panel ) . Nevertheless , these analyses show that the increased EcR and USP proteins in cdk8 or cycC mutants are predominantly localized in the nuclei during early and late L3 stage , suggesting that the subcellular localization of EcR and USP are not affected in the cdk8 or cycC mutants . Interestingly , the salivary gland cells in the cdk8 and cycC mutants are smaller than the control of the same stage ( compare S6B and S6C with S6A Fig ) , in addition to weaker DAPI staining ( S6B” and S6C” Fig , compared to the control in S6A” Fig ) . The sizes of salivary gland cells positively correlate to the DNA content [74] . The giant polytene chromosomes are produced from successive rounds of DNA endoreduplication . At the molecular level , DNA endoreduplication is controlled by periodical E2F1-activated expression of cyclin E ( cycE ) gene followed by transient degradation of E2F1 protein , which is mediated by the CRL4 ( CDT2 ) ubiquitin ligase [75] . Previously , we have reported that CDK8-CycC negatively regulates E2F1 activity in Drosophila [76] . As measured by qRT-PCR , the levels of E2F1 targets genes , such as CG7670 , cycE , MCM5 , mus209 ( encoding PCNA ) , Orc5 , rnrL , and stg , are indeed significantly increased in the cdk8 or cycC mutant salivary glands ( S7 Fig ) . These results suggest that the smaller salivary glands in the cdk8 and cycC mutants are likely caused by dysregulated E2F1 activity and endoreduplication . An alternative model to explain the apparent discrepancy between the increased protein levels of EcR-USP and the decreased EcR target gene expression in cdk8 or cycC mutants is that the CDK8-CycC complex is required for EcR-USP binding to the promoters of EcR target genes . In this model , the accumulated EcR-USP in nuclei may not effectively stimulate the target gene expression in the absence of CDK8 or CycC . To test this hypothesis , we used the antibodies against EcR or USP to ascertain protein localization on polytene chromosomes , which provide a straightforward method for rapid detection of the genome-wide localization of chromatin-binding proteins [77 , 78] . In polytene chromosome spreads from wild-type larvae , EcR ( Fig 5A ) and USP ( Fig 5A’ ) antibodies stain distinct bands that largely overlap with each other . However , we could hardly detect any signal of anti-USP staining on polytene chromosome spreads from cdk8 and cycC mutants that were prepared and imaged under the same conditions ( Fig 5B’ and 5C’ ) . Similarly , the signal of the anti-EcR antibody staining was significantly reduced on the polytene chromosome from the cdk8 and cycC mutants ( Fig 5B and 5C ) , compared to the control ( Fig 5A ) . To validate the consequence of this reduction of EcR-USP binding to polytene chromosomes , we examined the expression of EcR-target genes in the cdk8 and cycC mutant salivary glands at the L3 wandering stage using qRT-PCR . Similar to the data from whole-body analysis ( Fig 2B ) , the levels of EcR activated genes were significantly reduced in cdk8 and cycC mutant salivary glands than the control ( Fig 5D ) . These observations suggest that the recruitment of EcR and USP to their target promoters is defective in cdk8 and cycC mutants . To further validate these observations , we performed chromatin immunoprecipitation ( ChIP ) assay to examine whether the presence of USP at EcR target gene promoters , such as E74 , E75 , E78 , and Hsp27 , was affected in cdk8 and cycC mutant larvae . As shown in Fig 5E , the binding of USP to the promoters of these EcR-USP target genes was diminished in cdk8 and cycC mutants . These data are consistent with the reduced binding of EcR and USP to the polytene spreads in cdk8 and cycC mutants ( Fig 5B and 5B’ and 5C and 5C’ ) . Taken together , these observations suggest that the CDK8-CycC complex is required for the recruitment of EcR and USP to their target genes . To test the possibility that CDK8-CycC interacts with EcR or USP in vivo , we analyzed whether EcR or USP could co-immunoprecipitate with CDK8 in white prepupae . As shown in Fig 6A , EcR-B1 co-immunoprecipitated endogenous CDK8 . Similarly , CDK8 was co-immunoprecipitated with USP ( Fig 6B ) . These results suggest that CDK8 can interact with the EcR-USP complex in vivo . To test whether other Mediator subunits can co-immunoprecipitate with EcR and USP , we performed mass spectrometry analysis for proteins that immunoprecipitated with either EcR or USP in wild-type white prepupae . As shown in Fig 6C , multiple Mediator subunits , including the subunits of the CDK8 submodule , Med12 ( encoded by kohtalo or kto ) , Med13 ( encoded by skuld or skd [79–81] ) , CDK8 , and CycC , co-immunoprecipitated with EcR and USP . This assay also identified several known cofactors for EcR-USP , such as Hsp70 , Taiman , Smr , Rig , dDOR , and Utx ( S2 Table ) . These results validated and significantly expanded our co-immunoprecipitation data ( Fig 6A and 6B ) , suggesting that the Mediator complexes may function as transcriptional cofactors for EcR-USP . To address whether the interaction between EcR and USP is affected by CDK8-CycC , we tested whether USP could co-immunoprecipitate EcR in cdk8 and cycC mutants as efficient as control during the white prepupal stage . As expected , USP co-immunoprecipitated with EcR-B1 in the control; however , despite the elevated levels of EcR and USP in the mutants ( Fig 6D’ ) , much less EcR-B1 could be co-immunoprecipitated with USP in cdk8 or cycC mutants ( Fig 6D ) , consistent with the reduced USP binding to EcR targets in the mutants ( Fig 5 ) . This result suggests that CDK8-CycC normally functions to enhance the EcR-USP interaction , which is required for EcR-USP binding to the promoters of EcR target genes . Many transcriptional cofactors of nuclear receptors are known to possess a conserved signature amino acid motif LXXLL ( where L is leucine and X is any amino acid ) , as the interaction surfaces [65 , 66] . For example , via this LXXLL motif , the Mediator subunits ( MED1 and MED14 ) and other cofactors interact with mammalian nuclear receptors , such as androgen receptor , estrogen receptor , glucocorticoid receptor , thyroid hormone receptor and RXR [65 , 66 , 82 , 83] . This LXXLL motif is also found in several transcription coactivators for EcR [13] , such as Taiman , TRR , Rig , dDOR , dDEK , Hsp90 , and Hsc70 [24–29 , 84] . Interestingly , we have found that CDK8 , but not CycC , has a LXXLL motif that is highly conserved from yeasts to human ( Fig 6E ) . The X-ray crystal structure of the human CDK8-CycC complex shows that this leucine-rich motif is localized on the surface of the CDK8 protein [52] . In addition , the two LXXLL motifs in MED1 are vertebrate-specific and they are not present in flies or worms ( S8A Fig ) , while Drosophila Med14 has one LXXLL motif that is conserved from flies to humans but not in worms ( Fig 6F ) . To test whether EcR or USP may directly interact with CDK8 and Med14 , we performed yeast two-hybrid assays . We focused on the EcR-B1 isoform , because cdk8 and cycC mutants resemble EcR-B1 mutants ( Fig 1 ) and EcR-B1 is the major isoform that controls the larval–pupal transition [53] . Similar to other nuclear receptors , EcR and USP contain a ligand-independent activation function ( AF1 ) domain at their N-termini , followed by a DNA-binding domain ( DBD ) and a ligand-binding domain ( LBD ) that contains the ligand-dependent activation function ( AF2 ) ( Fig 6G ) [6 , 13] . We observed that EcR-AF1 , but not EcR-AF2 , could directly bind to CDK8 ( Fig 6H ) . In contrast , CDK8 did not bind to either AF1 or AF2 of USP ( Fig 6H ) . Similarly , EcR-AF1 , but not EcR-AF2 , directly interacted with the fragment of Med14 that contains the LXXLL motif ( Fig 6H ) . One caveat of these analyses is that the ligand 20E is not present in this assay , thus it is possible that the ligand may be required for EcR-AF2 to interact with CDK8 , Med14 , or other Mediator subunits . Nevertheless , these data suggest that the Mediator complexes are involved in regulating EcR-dependent gene expression through direct interactions between EcR and CDK8 or Med14 ( S8B Fig ) . Recently , we have reported that CDK8-CycC plays a key role in regulating lipogenesis in Drosophila and mammals by directly inhibiting the transcriptional activity of SREBPs [46] . Since the wandering behavior triggered by a pulse of 20E may mark a fundamental transition in energy metabolism from SREBP-dependent lipogenesis in feeding larvae to lipolysis in nonfeeding pupae , our data showing that CDK8 regulates EcR- and SREBP-dependent transcription prompt us to hypothesize that CDK8-CycC may integrate feeding-stimulated lipogenesis and ecdysone-regulated metamorphosis during the larval–pupal transition ( Fig 7A ) . To assess the plausibility of this hypothesis , we first analyzed the protein levels of CDK8 , CycC , SREBP , EcR and USP from mid-L3 larval stage ( 92 hr AEL ) to WPP stage ( 120 hr AEL ) by Western blot . In our experiments , the larvae started moving out of food approximately104 hr AEL , wandering stage occurred between 108 and 116 hr AEL , and then they reached WPP stage at approximately120 hr AEL . As shown in Fig 7B , the level of CDK8 is significantly increased during the wandering stage , which coincides with the abrupt increase of EcR and USP proteins . In contrast , the protein levels of CycC and SREBP were not significantly altered . To test whether the levels of EcR-USP and SREBP correlate with the expression of their target genes , we analyzed the expression of their target genes using qRT-PCR . We observed that the mRNA levels of cdk8 and cycC are gradually increased during L3 ( Fig 7C and 7D ) , which is supported by our measurement of their levels from early L3 ( 84 hr AEL ) to pupal stage ( 72hr APF ) ( S9A Fig ) . Although the expression of usp is not significantly increased , the mRNA levels of EcR and EcR-target genes , such as E74 , E75 , and E78 , are significantly increased during the wandering stage ( Figs 7E–7H and S9B ) . In contrast to EcR and EcR target genes , the mRNA levels of SREBP , and particularly SREBP-target genes , such as dFAS , dACC and dACS , are significantly decreased during the wandering and WPP stages ( Figs 7I–7K and S9C ) . Importantly , the patterns of change for SREBP target genes and EcR target genes appear opposite , and the transition occurs during the wandering stage , suggesting that the onset of wandering stage may represent a turning point for the increase of CDK8 and EcR-USP but the opposite trend of SREBP activity during the late L3 stage . The wandering behavior is accompanied by the cessation of feeding , thus the wandering stage may mark the major shift from lipogenesis in feeding larvae to EcR-regulated pupariation . These changes are suggestive and correlative , thus we performed additional experiments to test the relationship between nutrient intake and activities of SREBP and EcR as described below . Because CDK8-CycC directly regulates the transcriptional activity of both SREBP and EcR-USP , we sought to examine whether CDK8-CycC was actively involved in coordinating lipogenesis and metamorphosis in response to changes in nutrient intake triggered by wandering behavior . Since starvation of feeding larvae prematurely turns off nutrient intake , we asked whether starvation of the feeding larvae could precociously regulate the CDK8-SREBP/EcR network outlined in Fig 7A . Drosophila larvae reach critical weight between 80 hr and 82 hr AEL , and continue feeding for about 20 hr before the onset of wandering stage [85] . Therefore , we starved larvae during the first half of the post-critical weight feeding stage ( 84–100 hr AEL ) , and then analyzed levels of CDK8 , CycC , SREBP , EcR , and USP by Western blot . As shown in Fig 8A , the levels of CDK8 , EcR-B1 , and the full-length USP are barely detectable in normal feeding larvae during 84–100 hr AEL , but all of them are significantly increased after 4–8 hr of starvation ( 88 or 92 hr AEL ) . In contrast , the level of nuclear SREBP was decreased after 12 hr of starvation ( 96 hr AEL ) , while CycC was not significantly affected by starvation ( Fig 8A ) . These data show that starvation indeed leads to precocious reduction of mature form of SREBP and up-regulation of CDK8 , EcR , and USP . Interestingly , the mRNA levels of these factors are not significantly affected by starvation ( Figs 8B , 8C and 8E , S10A and S10B ) , suggesting post-transcriptional regulation of CDK8 , EcR , USP , and SREBP by starvation . Next , we examined whether the expression of EcR and SREBP target genes was affected by starvation using qRT-PCR . Consistent to reduced level of mature SREBP protein and increased CDK8 , SREBP target genes such as dFAS and dACS are strongly reduced after 8 hr of starvation ( Figs 8F and 8G ) . Although EcR and USP levels are significantly increased after starvation , expression of EcR target genes , such as E74 , E75 and E78 , is not significantly affected by starvation ( Figs 8D , S10C and S10D ) , suggesting that increase of EcR-USP alone is not sufficient to induce EcR target gene expression . To test whether the ecdysone biosynthesis is affected by starvation , we measured ecdysteroid titer and found no significant effect of starvation on ecdysone biosynthesis during the 84–100 hr AEL ( S10E Fig ) . Although it is unclear whether ecdysone biosynthesis is accelerated by starvation between 100 and 120 hr AEL ( see below , Fig 8H ) , this observation ( S10E Fig ) may explain why EcR target genes are not induced by elevated EcR-USP in starved larvae during the 16-hr period that we analyzed . Together , these results suggest that starvation precociously up-regulates CDK8-CycC and EcR-USP , but down-regulates SREBP and SREBP activity , all post-transcriptionally . Furthermore , we analyzed the effect of starvation on the timing of the larval–pupal transition . We observed that starvation of the wild-type larvae after they reached critical weight led to approximately 6 hr earlier onset of pupariation ( Fig 8H , red line ) and formation of smaller pupae than control ( S10F Fig ) . These observations are consistent to the predicted effects on the CDK8-EcR/SREBP network when nutrient intake is stopped early by starvation ( Fig 7A ) . Previously , we reported that refeeding of the starved larvae strongly activated the expression of lipogenic genes such as dFAS , while over-expression of CycC in fat body significantly hampered the refeeding-induced dFAS expression [46] . Therefore , to further analyze the effect of nutrition and feeding on the CDK8-EcR-SREBP network , we tested whether refeeding of starved larvae could have opposite effects on the CDK8-EcR/SREBP regulatory network to starvation . Specifically , we starved wild-type larvae at 84 hr AEL for 10 hr , and then collected the refeeding larvae after they were transferred back to normal food for 0 , 1 , 2 , 3 , 6 , or 9 hr ( Fig 8H , blue line; Fig 9A ) . We observed that refeeding for 1 to 3 hr potently reduced the protein levels of CDK8 , EcR and USP ( Fig 9B ) . Except EcR , the mRNA levels of cdk8 and usp are not obviously affected by refeeding ( Figs 9D and 9E , and S11B ) , suggesting a post-transcriptional regulation of these factors by refeeding . Similar observations were made after refeeding for 6 or 9 hr ( Fig 9C and S12 ) . Importantly , these changes are opposite to the effect of starvation ( Fig 8A ) , supporting the inhibitory effects of feeding or refeeding on CDK8 ( Fig 7A ) . Although both EcR and USP levels are reduced in refed larvae , expression of EcR-target genes are not obviously affected ( S11C–S11E Fig and S12F and S12G Fig ) . Perhaps , biosynthesis of 20E or other cofactors for EcR-USP dependent transcription are not present in refed larvae in the time window that we analyzed . Indeed , the refed larvae could pupariate , but with approximately 8 hr of delay ( Figs 8H and S10F ) . In addition , we did not observe any obvious changes in the level of mature SREBP proteins , but expression of SREBP and the SREBP target genes were significantly increased in refed larvae ( Figs 9F–9H and S12H–S12K ) , suggesting a potent stimulatory effect of refeeding on SREBP activity . Taken together , these results are largely consistent with the model that CDK8-CycC links the nutrition intake to EcR-USP and the activity of SREBP , suggesting that CDK8-CycC functions as a signaling node for coordinating lipid homeostasis and developmental timing in response to nutrient cues ( Fig 7A ) .
The Mediator complex is composed of up to 30 different subunits , and biochemical analyses of the Mediator have identified the small Mediator complex and the large Mediator complex , with the CDK8 submodule being the major difference between the two complexes [38 , 39 , 86] . Several reports link EcR and certain subunits of the Mediator complex . For example , Med12 and Med24 were shown to be required for ecdysone-triggered apoptosis in Drosophila salivary glands [87–89] . It was recently reported that ecdysone and multiple Mediator subunits could regulate cell-cycle exit in neuronal stem cells by changing energy metabolism in Drosophila , and specifically , EcR was shown to co-immunoprecipitate with Med27 [90] . However , exactly how Mediator complexes are involved in regulating EcR-dependent transcription remains unknown . Our data suggest that CDK8 and CycC are required for EcR-activated gene expression . Loss of either CDK8 or CycC reduced USP binding to EcR target promoters , diminished EcR target gene expression , and delayed developmental transition , which are reminiscent of EcR-B1 mutants [53] . Importantly , our mass spectrometry analysis for proteins that co-immunoprecipitate with EcR or USP has identified multiple Mediator subunits , including all four subunits of the CDK8 submodule . Taken together , previous works and our present work highlight a critical role of the Mediator complexes including CDK8-CycC in regulating EcR-dependent transcription . How does CDK8-CycC regulate EcR-activated gene expression ? Our biochemical analyses show that CDK8 can interact with EcR and USP in vivo and that CDK8 can directly interact with EcR-AF1 . These observations , together with the current understanding of how nuclear receptors and Mediator coordinately regulate transcription , suggest that CDK8-CycC may positively and directly regulate EcR-dependent transcription ( S8B Fig ) . Our yeast two-hybrid analysis indicates that the recruitment of CDK8-CycC to EcR-USP can occur via interactions between CDK8 and the AF1 domain of EcR . Interestingly , this assay also revealed a direct interaction between EcR-AF1 and a fragment of Med14 that contains the LXXLL motif . In future work , it will be interesting to determine whether CDK8 and Med14 compete with each other in binding with the EcR-AF1 , whether they interact with EcR-AF1 sequentially in activating EcR-dependent transcription , and how the Mediator complexes coordinate with other known EcR cofactors in regulating EcR-dependent gene expression . In cdk8 or cycC mutants , the binding of USP to the promoters of the EcR target genes is significantly compromised , even though nuclear protein levels of both EcR and USP are increased . It is unclear how CDK8-CycC positively regulates EcR-USP binding to EcREs near promoters . CDK8 can directly phosphorylate a number of transcription factors , such as Notch intracellular domain , E2F1 , SMADs , SREBP , STAT1 , and p53 [42 , 43 , 46] . Interestingly , the endogenous EcR and USP are phosphorylated at multiple serine residues , and treatment with 20E enhances the phosphorylation of USP [70 , 91 , 92] . Protein kinase C has also been proposed to phosphorylate USP [93 , 94] . It will be interesting to determine whether CDK8 can also directly phosphorylate either EcR or USP , thereby potentiating expression of EcR target genes and integrating signals from multiple signaling pathways . Although we favor a direct role for CDK8-CycC to regulate EcR-USP activated gene expression , we could not exclude the potential contribution of impaired biosynthesis of 20E to the developmental defects in cdk8 or cycC mutants . For example , the expression of genes involved in synthesis of 20E , such as sad and spok , is significantly reduced in cdk8 or cycC mutant larvae . Indeed , the ecdysteroid titer is significantly lower in cdk8 mutants than control from the early L3 to the WPP stages , and feeding the cdk8 mutant larvae with 20E can partially reduce the retardation in pupariation . Nevertheless , impaired ecdysone biosynthesis alone cannot explain developmental defects that we characterized in this report for the following reasons . First , feeding cdk8 or cycC mutants with 20E cannot rescue the defects in pupal morphology , developmental delay , and the onset of pupariation . Second , the expression of EcRE-lacZ reporter in cdk8 or cycC mutant salivary glands cannot be as effectively stimulated by 20E treatment as in control . Third , knocking down of either cdk8 or cycC in PG did not lead to obvious defects in developmental timing . Therefore , the most likely scenario is that the cdk8 or cycC mutants are impaired not only in 20E biosynthesis in the PG , but also in EcR-activated gene expression in peripheral tissues . Defects in either ecdysone biosynthesis or EcR transcriptional activity will generate the same outcome: diminished expression of the EcR target genes , thereby delayed onset of pupariation . How CDK8-CycC regulates biosynthesis of ecdysone in PG remains unknown . Several signaling pathways have been proposed to regulate ecdysone biosynthesis in Drosophila PG , including PTTH and Drosophila insulin-like peptides ( dILPs ) -activated receptor tyrosine kinase pathway and Activins/TGFβ signaling pathway [95 , 96] . Interestingly , CDK8 has been reported to regulate the transcriptional activity of SMADs , transcription factors downstream of the TGFβ signaling pathway , in both Drosophila and mammalian cells [97 , 98] . Thus , it is conceivable that the effect of cdk8 or cycC mutation on ecdysone biosynthesis may due to dysregulated TGFβ signaling in the PG . Our effort to explore the potential role of food consumption and nutrient intake on CDK8-CycC has resulted an unexpected observation that the protein level of CDK8 is strongly influenced by starvation and refeeding: starvation potently increased CDK8 level , while refeeding has opposite effect , and both occur post-transcriptionally ( Figs 8 and 9 ) . The importance of this observation is highlighted in two aspects . First , considering the generally repressive role of CDK8 on Pol II-dependent gene expression , up-regulation of CDK8 may provide an efficient way to quickly tune down most of the Pol II-dependent transcription in response to starvation; while down-regulation of CDK8 in response to refeeding may allow many genes to express when nutrients are abundant . Second , it will be necessary to test whether the effects of nutrient intake on CDK8-CycC is conserved in mammals . If so , considering that both CDK8 and CycC are dysregulated in a variety of human cancers [43] , the effects of nutrient intake on CDK8 may have important implications in not only our understanding of the effects of nutrients on tumorigenesis , but also providing nutritional guidance for patients with cancer . Major dietary components including carbohydrates , lipids , and proteins , can strongly influence the developmental timing in Drosophila [2] . Excessive dietary carbohydrates repress growth and potently retard the onset of pupariation [99–101] . One elegant model proposed to explain how high sugar diet delays developmental timing is that high sugar diet reduces the activity of the Target of Rapamycin ( TOR ) in the PG , thereby reducing the secretion of ecdysone and delaying the developmental transition [102] . Previously , we reported that insulin signaling could down-regulate CDK8-CycC , and that ectopic expression of CycC could antagonize the effect of insulin stimulation in mammalian cells , as well as the effect of refeeding on the expression of dFAS in Drosophila [46] . Although the mRNA levels of TOR and insulin receptor ( InR ) are not significantly affected in cdk8 or cycC mutants ( Fig 3B ) , it is necessary to further study whether and how different dietary components may regulate CDK8-CycC in the future . Our developmental genetic analyses of the cdk8 and cycC mutants have revealed major defects in fat metabolism and developmental timing ( [46]; this work ) . De novo lipogenesis , which is stimulated by insulin signaling , contributes significantly to the rapid increase of body mass during the constant feeding larval stage . This process is terminated by pulses of ecdysone that trigger the wandering behavior at the end of the L3 stage , followed by the onset of the pupariation . Insulin and ecdysone signaling are known to antagonize each other , and together determine body size of Drosophila . The genetic interaction is established , but the detailed molecular mechanisms are not [1 , 25 , 103 , 104] . The SREBP family of transcription factors controls the expression of lipogenic enzymes in metazoans and the expression of cholesterogenic enzymes in vertebrates [105 , 106] . Our previous work shows that CDK8 directly phosphorylates the nuclear SREBP proteins on a conserved threonine residue and promotes the degradation of nuclear SREBP proteins [46] . Consistent with the lipogenic role of SREBP and the inhibitory role of insulin to CDK8-CycC [46] , the transcriptional activity of SREBP is high while the levels of CDK8-CycC and EcR-USP are low prior to the onset of wandering stage . Subsequently during the wandering and non-mobile , non-feeding pupal stage , the transcriptional activity of SREBP is dramatically reduced , accompanied by the significant accumulation of CDK8-CycC and EcR-USP ( Fig 7 ) . The causal relationship of these phenomena was further tested by our starvation and refeeding experiments . On the one hand , we observed that the levels of CDK8 , EcR and USP are potently induced by starvation , while the mature SREBP level and the transcriptional activity of SREBP are reduced by starvation ( Fig 8 ) . Starvation of larvae prior to the two nutritional checkpoints in early L3 , known as minimum viable weight and critical weight , which are reached almost simultaneously in Drosophila , will lead to larval lethality; while starvation after larvae reach the critical weight will lead to early onset of pupariation and formation of small pupae [9 , 85 , 107 , 108] . Thus , this nutritional checkpoint ensures the larvae have accumulated sufficient growth before metamorphosis initiation [2 , 85] . If we regard the status with high CDK8 , EcR , and USP as an older or later stage , these results indicate that starvation shifts the regulatory network precociously , which is consistent with the regulatory network outlined in Fig 7A and the observed premature pupariation ( Fig 8H ) . On the other hand , our analyses of refed larvae show that refeeding potently reduced the levels of CDK8 , EcR and USP ( Fig 9 ) . If we consider the status with low CDK8 , EcR , and USP as a younger or earlier stage , these results indicate that refeeding delays the activation of this network , which is consistent with our model ( Fig 7A ) and delayed pupariation as observed ( Fig 8H ) . Taken together , our results based on starved and refed larvae suggest that CDK8-CycC is a key regulatory node linking nutritional cues with de novo lipogenesis and developmental timing ( Fig 7A ) . The larval–pupal transition is complex and dynamic . Although the expression of SREBP target genes fit well with the predicted effects of starvation and refeeding , the expression of EcR targets during the stage that we analyzed does not reflect the changes in the protein levels of EcR and USP ( Figs 8 and 9 ) . It is reasonable to consider that CDK8-CycC and EcR-USP are necessary , but not sufficient , for the activation of EcR target genes . One possibility is that there is a delay on synthesis of 20E or other cofactors that are required for EcR-activated gene expression in response to starvation . Indeed , we measured the 20E levels during the first 16 hr of starvation and observed no significant difference between fed and starved larvae ( S10E Fig ) . It will be necessary to further analyze the effect of starvation on 20E synthesis at later time points in the future . Taken together , we propose a model whereby CDK8-CycC functions as a regulatory node that coordinates de novo lipogenesis during larval stage and EcR-dependent pupariation in response to nutritional cues ( Fig 7A ) . It is likely that pulses of 20E synthesized in the PG , and subsequent behavioral change from feeding to wandering , ultimately trigger the transition from SREBP-dependent lipogenesis to EcR-dependent pupariation . The opposite effects of CDK8-CycC on SREBP- and EcR-dependent gene expression suggest that the role of CDK8 on transcription is context-dependent . In conclusion , our study illustrates how CDK8-CycC regulates EcR-USP-dependent gene expression , and our results suggest that CDK8-CycC may function as a regulatory node linking fat metabolism and developmental timing with nutritional cues during Drosophila development .
The null alleles of cdk8 ( cdk8K185 ) and cycC ( cycCY5 ) strains were provided by Drs . Muriel Boube and Henri-Marc Bourbon [50] . The EcRE-lacZ reporter and ubi-Gal4 lines were obtained from Dr . Keith Maggert . The P[hs-usp] transgenic line [72 , 73] was obtained from the Bloomington Drosophila stock center . Embryos from cycCY5 germline clones were generated using the Flipase recombinase-mediated dominant female sterile technique [109] . All flies were maintained on standard cornmeal-molasses-yeast medium at 25°C . The anti-USP monoclonal antibody was provided by Dr . Rosa Barrio Olano . The anti-CycC polyclonal antiserum ( peptide antibody in rabbits ) was provided by Dr . Terry Orr-Weaver . Anti-EcR common ( DDA2 . 7 ) and anti-EcR-B1 ( AD4 . 4 ) monoclonal antibodies were obtained from Developmental Studies Hybridoma Bank , and the anti-actin ( MA5-11869 ) monoclonal antibody was purchased from Thermo Scientific ( Rockford , IL ) . The anti-CDK8 polyclonal antibody was generated by immunizing rabbits using peptide AA355~372 ( KREFLTDDDQEDKSDNKR ) as the antigen , anti-SREBP polyclonal antibody was generated using peptide AA360~378 ( KDLLQLGTRPGRASKKRRE ) as the antigen , and both were performed by Thermo Scientific . The antisera were purified by GST-CDK8 ( AA1~372 ) or GST-SREBP ( AA1~451 ) fusion proteins , respectively , using the protocol as described previously [110] . The anti-USP polyclonal antibody was generated by immunizing guinea pigs with GST-USP ( full length ) as the antigen , performed by Covance Research Products ( Denver , PA ) . These fusion proteins were generated using the protocol described previously [111] . We generated the tagged genomic cdk8 or cycC ( approximately 7 . 5-kb ) rescue constructs using backbone of the pVALIUM20 vector , which can be used for site-specific insertion with the PhiC31 integrase system [112] . For subcloning , we first linearized the pVALIUM20-gypsy-MSC10 vector by EcoRI ( NEB ) . The gDNA segments for cdk8 and cycC were PCR amplified from bacterial artificial chromosome ( BAC ) clones ( CH322-104A8 for cdk8 locus and CH321-46N21 for cycC locus ) from the BACPAC Resources Center ( http://bacpac . chori . org/home . htm ) . To ensure the fidelity of these PCR reactions , we used a high-fidelity DNA polymerase PrimeSTAR Max ( Takara , Cat# R045A ) and then purified all segments by gel extraction ( QIAEX II ) . To join four DNA segments ( pVALIUM20 backbone , two gDNA segments and one EGFP segment ) seamlessly in a single reaction , we used In-Fusion HD system developed by Clontech ( 639649 ) . This system requires that the sense and antisense PCR primers contain a 15bp overlap with the neighboring segment and 20–30bp segment specific sequence . The primers with the 15bp overlapping sequence underlined are listed below: Cdk8 IN-1L: 5′-GTGGCTAGCAGAATTCAGGCACCCATTGGCGATG; Cdk8 IN-2: 5′-GTTGAAGCGCTGGAAGTTCTGCT; Cdk8 IN-3 ( EGFP ) : 5′-TTCCAGCGCTTCAACATGGTGAGCAAGGGCGAGGAG; Cdk8 IN-4 ( EGFP ) : 5′-TGTATCAGTCTCTCACTTGTACAGCTCGTCCATGCCG; Cdk8 IN-5: 5′- TGAGAGACTGATACATGCAGCATTTTTTC; Cdk8 IN-6LL: 5′- GGCTCTAGATGAATTATGCTCGCTGATTCCACGATCAG; CycC IN-1L: 5′- GTGGCTAGCAGAATTTCCTTCGAGGATCGCACCTG; CycC IN-2: 5′-ACGCTGAGGCGGTGGTTTC; CycC IN-3 ( EGFP-ATG ) : 5′-ATGCCACCGCCTCAGCGTGTGAGCAAGGGCGAGGAGCTG; CycC IN-4 ( EGFP ) : 5′-TATGAAGCTCTTCTACTTGTACAGCTCGTCCATGCCG; CycC IN-5: 5′-TAGAAGAGCTTCATAATCATTCATCATTAGC; and CycC IN-6L: 5′-GGCTCTAGATGAATTTGCTGGACCTATACAGACGCACG . For the In-Fusion reaction , 100 ng of enzyme-digested , gel-purified vector were mixed with the PCR amplified segments at a molar ratio of 1 vector to 2 of each DNA segment in a total of 10 μl system buffered by In-Fusion HD Enzyme premix and the subsequent steps were carried out following the manufacturer’s instructions . The positive clones were selected and characterized by restriction enzyme digestion and sequencing . The rescue constructs were inserted into the second chromosome ( attP40 site at 25C6 ) with the service provided by Genetic Services , Inc . This design facilitates genetic recombination since the endogenous cdk8 and cycC genes are on the third chromosome . The microarray analyses were described previously [46] , and the data sets can be found in the ArrayExpress database ( http://www . ebi . ac . uk/arrayexpress/; accession number E-MTAB-1066 ) . The RNA isolation , reverse transcription , the qRT-PCR analyses , and primers for the lipogenic enzymes were performed as described previously [46] . The primers used in the qRT-PCR assay are listed in S3 Table , and Rp49 gene was used as the control . Primers for InR , kni , mld , nvd , tor , and vvl are adapted from [61] . Quantification of ecdysteroids in whole larvae was performed as described by [61 , 113] with the following modifications . Briefly , animals were homogenized in 0 . 25 ml 75% methanol , and then the supernatants were collected following centrifugation at 14 , 000 g for 15 min . The pellets were re-extracted in 0 . 1 ml methanol . The supernatants were combined , evaporated using a SpeedVac , and then re-dissolved in 0 . 5 ml ELISA buffer ( Cayman Chemical ) . Ecdysteroids were measured using a commercial ELISA kit ( Cayman Chemical ) that detects 20E equivalents . Standard curves were generated using 20E ( Cayman Chemical ) , and absorbance was measured at 405 nm on a microplate photometer ( Thermo Scientific ) . The rescue experiments were performed as described previously [63] . Briefly , cdk8K185 and cycCY5 homozygous mutants at late L2 and early L3 larvae were collected and placed in groups of 10 individuals in new vials containing food with 20E ( Alexis or Cayman Chemical ) ranging from 2 . 0 μM to 2 . 0 mM ( Figs 3 and S4 ) , and w1118 larvae were treated in parallel as the control . Pupae were collected and photographed under a microscope . The EcRE-lacZ reporter line was recombined with cdk8K185 or cycCY5 mutant to generate the following genotypes: “w1118; EcRE-lacZ; cdk8K185/TM6B” , “w1118; EcRE-lacZ; cycCY5/TM6B” . The “w1118; EcRE-lacZ; +” line was used as the control . To ensure that we compare the salivary glands that are at the same developmental stage , we dissected the salivary gland from mid-L3 homozygous larvae ( non-TM6B ) of all these genotypes , and separated into two halves in Grace’s insect medium ( HiMedia ) . One half was treated in Grace’s medium with 1μM 20E , while the other half from the same larva was cultured in Grace’s insect medium as the control . After 2 . 5 hr incubation at 25 ˚C , salivary glands were stained with X-gal solution ( 3 . 0 mM K4[Fe ( CN ) 6] , 3 . 0 mM K3[Fe ( CN ) 6] in PBS with X-gal stock solution ( 8% in DMSO ) added to a final concentration of 0 . 2% ) at 37°C for 1 hr in the dark . The stained salivary glands were then transferred into 80% glycerol in PBS , mounted and photographed with a Leica DM2500 microscope . The salivary glands from the third-instar wandering larvae were dissected in PBS ( phosphate buffered saline , pH7 . 4 ) and fixed in 5% formaldehyde in PBS for 10 min . After washing in PBT ( PBS with 0 . 2% Triton X-100 , pH7 . 4 ) for 4 times for 1 hour , the salivary glands were blocked in PBTB ( 0 . 2% BSA , 5% normal goat serum in PBT ) for 1 hour at room temperature . The glands were then incubated with the anti-EcR-common DDA2 . 7 antibody ( 1:100 ) and anti-USP antibody ( 1:2 , 000 , polyclonal antibody from guinea pigs ) at 4 ˚C overnight on a nutator . After rinsing with PBT for 4 times , the glands were incubated with secondary antibodies ( BODIPY-conjugated goat anti-mouse antibody 1:500 in PBTB; Alex594-conjugated goat anti-guinea pig antibody , 1:2 , 500 in PBTB ) at room temperature for 2 hr . After standard nuclear counterstaining with DAPI ( 4′ , 6-Diamidino-2-phenylindole dihydrochloride , Sigma ) , the salivary glands were mounted on slides with Vectashield mounting media ( Vector lab ) . For immunostaining of polytene chromosome , we followed the protocol described previously [114] , and the following antibodies were used: anti-EcR-common DDA2 . 7 ( 1:200 in PBTB ) , anti-USP ( Guinea pig , 1:200 ) , BODIPY-conjugated goat anti-mouse antibody ( 1:500 ) , and Alex594-conjugated goat anti-guinea pig antibody ( 1:1 , 000 ) . Confocal images were taken with a Nikon Ti Eclipse microscope , and images were processed by Adobe Photoshop CS6 software . We separated the cytoplasmic , nuclear soluble , and nuclear insoluble fractions of protein extractions by following the protocol as described [115] . Western blot analysis was performed as previously described with minor modifications [46] . For whole cell extracts , homogenized larvae or pupae were lysed in a buffer containing 50 mM Tris HCl ( pH 8 . 0 ) , 0 . 1 mM EDTA , 420 mM NaCl , 0 . 5% NP-40 , 10% glycerol , 1 mM dithiothreitol ( DTT ) , 2 . 5 mM phenylmethanesulfonylfluoride ( PMSF ) , protease and phosphatase inhibitors ( the cOmplete Protease Inhibitor Cocktails and PhosSTOP , Roche Applied Science ) . Supernatants were collected after centrifugation at 2 , 000 g for 15 min at 4°C . Protein concentrations were measured with a Bradford protein assay kit ( Bio-Rad ) . A given amount of whole cell extract was mixed with 4x Laemmli sample buffer ( Bio-Rad ) . After boiling for 5 min , the proteins were resolved by 8% SDS-PAGE gel and transferred to PVDF membrane . The following antibodies were used: anti-EcR-common DDA2 . 7 ( 1:250 ) , anti-USP ( guinea pig , 1:2 , 000 ) , anti-USP ( monoclonal antibody , 1:1 , 000 ) , anti-CDK8 ( polyclonal antibody from rabbit , 1:50 ) , anti-CycC ( rabbit polyclonal antibody , 1:2 , 000 ) , anti-dSREBP ( rabbit polyclonal antibody , 1:100 ) , and anti-actin monoclonal antibody ( 1:4 , 000 , Thermo Scientific ) . The membranes were incubated with the corresponding HRP-conjugated secondary antibodies ( 1:2 , 500–1:10 , 000 , Jackson ImmunoResearch ) for 1 hr at room temperature . After washing , the HRP signals were visualized by the Western Lightening Plus ECL ( PerkinElmer ) according to the manufacturer’s instructions . The co-IP assay was performed as described previously with minor modifications [116] . Briefly , the IP complex was prepared with 35 μL Magnetic Protein G beads ( 28-9670-66 , GE Healthcare Life Sciences ) and 5 μg primary antibody or IgG in 500 μL PBS and put on the rotator for 12–16 hr at 4°C . After incubation , the IP complex was washed with PBS twice and eventually removed all PBS . Lysates of 30 white prepupae per sample were prepared in the lysis buffer ( 150 mM NaCl , 50 mM Tris pH 8 . 0 , 5 mM EDTA , 5 mM DTT , 0 . 1 mM PMSF , 0 . 5% NP-40 , 2 mM Na3VO4 , and protease inhibitor from Roche Applied Science ) . 400 μL of lysates were pre-cleared with 20 μL Magnetic Protein G beads on a rotator for 1 hr at 4°C , then the beads were discarded , and the lysates were mixed with IP complex and put on the rotator for 12–16 hr at 4°C . The IP complex was washed with lysis buffer ( without protease inhibitor ) five times , added 60 μL 2X sample buffer , denatured for 3 min at 95°C , and further analyzed by Western blot . Each IP sample was run as a gel plug and proteins in the gel plug were reduced , carboxymethylated , digested with trypsin using standard protocols . Peptides were solubilized in 0 . 1% trifluoroacetic acid , and analyzed by Nano LC-MS/MS ( Dionex Ultimate 3000 RLSCnano System interfaced with a Velos-LTQ-Orbitrap ( ThermoFisher , San Jose , CA ) . Sample was loaded onto a self-packed 100 μm x 2 cm trap ( Magic C18AQ , 5 μm 200 Å , Michrom Bioresources , Inc . ) and washed with Buffer A ( 0 . 2% formic acid ) for 5 min with a flow rate of 10 μl/min . The trap was brought in-line with the analytical column ( Magic C18AQ , 3 μm 200 Å , 75 μm x 50 cm ) and peptides fractionated at 300 nL/min using a segmented linear gradient: 4%–15% B ( 0 . 2% formic acid in acetonitrile ) in 35 min , 15%–25% B in 65 min , 25%–50% B in 55 min . Mass spectrometry data was acquired using a data-dependent acquisition procedure with a cyclic series of a full scan acquired in Orbitrap with resolution of 60 , 000 followed by MS/MS ( acquired in the linear ion trap ) of the 20 most intense ions with a repeat count of two and a dynamic exclusion duration of 30 sec . Peak lists in the format of MASCOT Generic Format ( MGF ) was generated using the Proteome Discover 1 . 4 ( ThermoFisher ) . Data were searched against latest flybase Drosophila melanogaster protein database ( madmel-all-translation-r6 . 03 . fasta ) using a local version of the Global Proteome Machine ( GPM ) XE Manager version 2 . 2 . 1 ( Beavis Informatics Ltd . , Winnipeg , Canada ) with X ! Tandem SLEDGEHAMMER ( 2013 . 09 . 01 ) to assign spectral data [117 , 118] . Precursor ion mass error tolerance was set to ±10 ppm and fragment mass error tolerance set to ±0 . 4 Da . Cysteine carbamidomethylation was set as a complete modification , methionine oxidation and deamidation at asparagine and glutamine residues were set as variable modifications . All LC-MS data were analyzed together in a MudPit analysis and individual data extracted to ensure that peptides that could be assigned to more than one protein were assigned consistently for all samples . The resulting identifications were filtered by peptide log GPM expectancy score ( log ( e ) <-1 . 5 ) . Whole-animal extracts prepared from L3 wandering larvae or white prepupae of w1118 ( control ) , cdk8K185 , or cycCY5 homozygous mutants were used for ChIP assays according to the protocols described previously [119] . Briefly , the fixed materials were sonicated using an Ultrasonic Processor Cell Disruptor ( Branson S-450D ) at 50% power output for 60 sec ( 2-sec-long pulse with 1 minute interval on ice ) . We prepared triplicate biological samples for each genotype , and for each sample , we used 2 . 0 μg Guinea Pig anti-USP for IP or 2 . 0μg normal serum isolated from the same Guinea Pig before immunization as a control . SYBR Green PCR Master Mix ( Invitrogen ) was used in qPCR reactions . For the qRT-PCR , the following primers were designed using Primer Express ( Applied Biosystems ) based on the EcR ChIP-Seq data ( Kevin White’s lab ) : Hsp27 F 5′-GCAACAAACAAAAGAACGGC-3′ , Hsp27 R 5′-TTTCAGAGTGCAACAGAGCTTG-3′; E74 F 5′-TCGGTCAAAAGCAGAGTTCACA-3′ , R 5′-ATTTCTCTGCAACTGCTCCC-3′; E75 F 5′-AGGCCTGGCTGGCTGTTACTTA-3′ , R 5′-CGGAGAGTTGAAGGCGAGTTT-3′; and E78 F 5′-ATGACGTTGCCCACAAGTCATT-3′ , R 5′-ACAGTTGCCTTGGCTTCTTCG-3′ . Fold enrichment was calculated and normalized using the Guinea Pig normal serum as the negative control . To investigate physically interactions between EcR/USP and CDK8/Med14 in yeast cells , we have cloned EcR/USP into pGBKT7 ( bait vector ) and Drosophila Med14/CDK8 into pGADT7 ( prey vector ) ( Clontech , Mountain View , CA ) using the procedures described previously [120] . The following primers were used: for EcRB1-AF1 BamHIS 5′-CAGGATCCTGAAGCGGCGCTGGTCGAAC-3′ and EcRB1-AF1PstIAS 5′-CACTGCAGACCTGAAGATATAGAATTCACCGAATCGC-3′; for EcRB1-AF2 BamHIS 5′-CAGGATCCCTGATGAAATATTGGCCAAGTGTCAAGC-3′ and EcRB1-AF2 PstIAS 5′-CACTGCAGGATGGCATGAACGTCCCAGATCTC-3′; for USP-AF1 ( 1-100AA ) : dUSP-1EcoRIS 5′-CAGAATTCATGGACAACTGCGACCAGGACGC-3′ and dUSP-100BamHIAS 5′-CAGGATCCGCTGCCGCTCAGCGGATGGTT-3′; for USP-AF2 ( 206-509AA ) : dUSP-206EcoRIS 5′-CAGAATTCAGCTCTCAAGGCGGAGGAGGAGGA-3′ and dUSP-509BamHIAS 5′-CAGGATCCCTACTCCAGTTTCATCGCCAGGCC-3′; for Med14 ( 159-320AA ) : dMed14-159EcoRIS 5′-CAGAATTCCTAATTGTACATACTGTCTACATACGATCGG-3′ and dMed14-320BamHIAS 5′-CAGGATCCGGTAGTCTTTTCCTTGAGTTGAACCAC-3′ . At least three independent biological repeats were included for each genotype , all error bars indicate standard deviation , and t-tests were performed using Microsoft Excel ( S1 Data ) . Statistical significance was shown in figures , and fold changes of 1 . 5 or greater were considered as biologically significant . All biochemical analyses were repeated at least three times and the representative results were shown .
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Arthropods are estimated to account for over 80% of animal species on earth . Characterized by their rigid exoskeletons , juvenile arthropods must periodically shed their thick outer cuticles by molting in order to grow . The steroid hormone ecdysone plays an essential role in regulating the timing of developmental transitions , but exactly how ecdysone and its receptor EcR activates transcription correctly after integrating nutritional and developmental cues remains unknown . Our developmental genetic analyses of two Drosophila mutants , cdk8 and cycC , show that they are lethal during the prepupal stage , with aberrant accumulation of fat and a severely delayed larval–pupal transition . As we have reported previously , CDK8-CycC inhibits fat accumulation by directly inactivating SREBP , a master transcription factor that controls the expression of lipogenic genes , which explains the abnormal fat accumulation in the cdk8 and cycC mutants . We find that CDK8 and CycC are required for EcR to bind to its target genes , serving as transcriptional cofactors for EcR-dependent gene expression . The expression of EcR target genes is compromised in cdk8 and cycC mutants and underpins the retarded pupariation phenotype . Starvation of feeding larvae precociously up-regulates CDK8 and EcR , prematurely down-regulates SREBP activity , and leads to early pupariation , whereas re-feeding starved larvae has opposite effects . Taken together , these results suggest that CDK8 and CycC play important roles in coordinating nutrition intake with fat metabolism by directly inhibiting SREBP-dependent gene expression and regulating developmental timing by activating EcR-dependent transcription in Drosophila .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
CDK8-Cyclin C Mediates Nutritional Regulation of Developmental Transitions through the Ecdysone Receptor in Drosophila
|
Bacterial pathogens have evolved strategies that enable them to invade tissues and spread within the host . Enterococcus faecalis is a leading cause of local and disseminated multidrug-resistant hospital infections , but the molecular mechanisms used by this non-motile bacterium to penetrate surfaces and translocate through tissues remain largely unexplored . Here we present experimental evidence indicating that E . faecalis generates exopolysaccharides containing β-1 , 6-linked poly-N-acetylglucosamine ( polyGlcNAc ) as a mechanism to successfully penetrate semisolid surfaces and translocate through human epithelial cell monolayers . Genetic screening and molecular analyses of mutant strains identified glnA , rpiA and epaX as genes critically required for optimal E . faecalis penetration and translocation . Mechanistically , GlnA and RpiA cooperated to generate uridine diphosphate N-acetylglucosamine ( UDP-GlcNAc ) that was utilized by EpaX to synthesize polyGlcNAc-containing polymers . Notably , exogenous supplementation with polymeric N-acetylglucosamine ( PNAG ) restored surface penetration by E . faecalis mutants devoid of EpaX . Our study uncovers an unexpected mechanism whereby the RpiA-GlnA-EpaX metabolic axis enables production of polyGlcNAc-containing polysaccharides that endow E . faecalis with the ability to penetrate surfaces . Hence , targeting carbohydrate metabolism or inhibiting biosynthesis of polyGlcNAc-containing exopolymers may represent a new strategy to more effectively confront enterococcal infections in the clinic .
Microbes use a variety of strategies to obtain nutrients and ensure survival . While motility could be used as a means for accessing nutrient sources , non-motile bacterial species require unconventional mechanisms to accomplish this goal . Enterococcus faecalis is a non-motile , facultative anaerobic bacterium that inhabits the human gastrointestinal ( GI ) tract [1] . However , hypervirulent E . faecalis strains resistant to multiple antibiotics often cause hospital-acquired urinary tract , wound and abdominal infections , as well as bacteremia and infective endocarditis [1] . Enterococci can adhere to and invade host tissues in order to act as lethal pathogens . Indeed , E . faecalis translocation across the intestinal barrier enables bacterial spread and colonization of distal anatomical sites [2 , 3] . Interestingly , E . faecalis extra-intestinal translocation appears to be promoted by association with epithelial cells in aggregates [3] , a process that is partly mediated by the synthesis of adhesins [3–5] and additional unknown factors . Enterococci produce diverse cell wall-anchored polysaccharides [6–8] , which generally consist of repeating units of oligosaccharides that are associated with bacterial surfaces through linkage to cell membrane , peptidoglycan or other unknown mechanisms [7 , 9] . E . faecalis displays an extensive surface glycome , including wall teichoic acid and lipoteichoic acid polymers , capsular polysaccharides [7 , 10] , and the enterococcal polysaccharide antigen ( EPA ) , which is a rhamnopolysaccharide . EPA , mainly composed of glucose , rhamnose , N-acetylglucosamine ( GlcNAc ) , N-acetylgalactosamine ( GalNAc ) , and galactose that appears to be buried within the cell wall , thus precluding interaction with host cells [7] . In addition to these polysaccharides , a new glycopolymer was recently discovered on the surface of E . faecalis cells by immunofluorescence assays using the human IgG monoclonal antibody ( mAb ) F598 , which specifically binds to β-1 , 6-linked GlcNAc polysaccharides [8 , 11] . While this putative polyGlcNAc-like polymer has not been studied in E . faecalis , other reports have characterized similar polysaccharides that react with mAb F598 [12 , 13] , termed either PIA ( for polysaccharide intercellular adhesin ) in Staphylococcus epidermidis [14 , 15] or PNAG ( for polymeric N-acetyl glucosamine ) in Staphylococcus aureus and other pathogens [16 , 17] . Of note , these extracellular glycopolymers consist of β-1 , 6-linked GlcNAc residues containing 5–10% positively-charged amino groups ( due to partial de-N-acetylation ( GlcNH3 ) ) as well as negative charges ( resulting from O-succinylation ) [8 , 14–17] . E . faecalis has been shown to invade surfaces such as mammalian tissues [3 , 18] and penetrate solid culture media [19] , but the mechanisms driving these processes remain elusive . In the present study , we identified the molecular events and metabolic pathways that endow E . faecalis with remarkable capacity to penetrate semisolid surfaces . We found that E . faecalis produces extracellular polyGlcNAc-containing polymers to form penetrating microcolonies inside semisolid surfaces . Using diverse genetic and biochemical approaches , we determined that biosynthesis of these complex exopolymers occurs through the RpiA-GlnA-EpaX metabolic pathway . Notably , E . faecalis mutants unable to produce polyGlcNAc-containing polymers demonstrated impaired capacity to pass into semisolid surfaces and translocate through human epithelial cell monolayers .
Analysis of semisolid media penetration has been useful for identifying and characterizing virulence traits in human fungal pathogens [20–23] . We sought to exploit this approach to understand the molecular mechanisms that E . faecalis utilizes to enter surfaces . Under our conditions , an indelible bacterial “colony-print” developed inside modified MOLP ( medium optimal for lipopeptide production ) [24] , when six-day-old colonies of the clinical isolate E . faecalis were extensively washed with water to remove adventitiously associated bacterial cells from the surface of the agar . This colony-print , indicative of penetration , was not observed at agar concentrations above 1 . 0% ( Fig 1A ) , and agar degradation was not evidenced once the external ( non-penetrating ) cells were removed . Importantly , we identified this semisolid agar-penetration trait in several clinical isolates as well as in the commensal-like strain E . faecalis OG1RF ( S1A Fig ) . Kinetic analyses revealed that E . faecalis agar entrance was macroscopically evident 48 h post-inoculation , and that it progressed concomitantly with colony expansion ( S1B Fig ) . Of note , the agar penetration ability of all E . faecalis strains tested in this study was not lost upon laboratory domestication . This phenomenon was not only evidenced when E . faecalis colonies were grown on culture media solidified with agarose , but also with the copolymer poloxamer-407 ( MOLP-407 ) ( S1C and S1D Fig; top ) , demonstrating that this process is not specific to the nature of the gelling agent . To quantify the number of penetrating and non-penetrating bacteria , colony forming units ( CFUs ) were determined from both inside and outside E . faecalis populations grown on semisolid MOLP . Some of the penetrating bacteria were able to form colonies , but the total number of CFUs obtained for this population ( ~5x108 ) was reproducibly one order of magnitude lower than that of the external cells ( ~6x109; Fig 1B ) . Similarly , the total CFUs obtained from the penetrating population grown on MOLP-407 were lower ( ~2x108 ) than the CFUs obtained for external cells ( ~1x109; S1D Fig , bottom ) . Flow cytometric analyses of penetrating and non-penetrating bacteria grown on MOLP were performed to determine the viability of these cells after staining with the live/dead dye Brilliant Violet-570 ( BV-570 ) . In contrast to the heat-killed control showing less than 5% viable cells , ~90% of the penetrating and non-penetrating population were viable under our culture conditions ( Fig 1C ) . Together , these data indicate that E . faecalis can pass into semisolid surfaces and that the majority of the penetrating cells remain viable during this process . To further understand the E . faecalis penetration process , we analyzed agar side sections of approximately 2 mm-wide obtained from six-day-old colony-prints produced by penetrating bacteria grown in MOLP . Interestingly , isolated aggregates with varying morphologies and sizes readily formed inside the agar ( Fig 1D; top ) . Penetration was found to decrease proportionally from the center ( S1E Fig; depth of ~128 μm ) to the edge ( S1F Fig; depth of ~6 μm ) of the colony , with several aggregates penetrating deeper at the center ( Fig 1D; top and S1E Fig ) . DAPI staining ( Fig 1D; middle ) and epifluorescence analysis of penetrating bacterial cells constitutively expressing m-Cherry ( Fig 1D; bottom ) confirmed that these internal clumps contained viable and metabolically active E . faecalis cells . Scanning electron microscopy ( SEM ) analyses of E . faecalis colonies were performed to determine the morphological status of external and MOLP-penetrating cells . No major changes in cell morphology were found and only normal diplococcal , clumped or isolated , bacterial cells were observed . Strikingly , however , cells within the aggregates were covered with and connected by an extracellular matrix that appeared to be more abundantly produced by invading cells than surface cells ( Fig 1E ) . Indeed , automated SEM image analysis ( see Material and Methods ) determined that internal cells exhibited significantly higher matrix coverage than external cells ( Fig 1F ) . These data indicate that E . faecalis penetrates the agar surface and that this process is accompanied by the generation of microcolonies formed by matrix-covered cells . Cellular structures , such as pili , have been shown to be involved in mediating Gram-positive bacterial motility [25] . To determine whether pili expression could mediate the penetration process observed , we tested two previously generated pili-deficient E . faecalis mutants ( ΔebpABC and ΔebpA ) , and their parental OG1RF strain [26] , under our conditions . After 6 days of growth on MOLP , we found that Ebp mutants and the wild-type ( WT ) strain exhibited similar penetration capacities , and only a slight change on the shape of the colony-print was evidenced in the absence of pili ( S2A Fig ) . These data suggest that the bacterial migration process is mediated by an Ebp pilus-independent mechanism . To further understand the E . faecalis semisolid surface penetration process , we performed genetic screening of a Mariner transposon insertion library . We sought to identify mutants that developed normal colonies above the agar , but were impaired in their semisolid surface-penetration capacity . Out of approximately 6 , 000 mutants screened , seven were found to be defective in penetration . Five of the seven mutants identified exhibited substantial growth defects and were thus excluded from subsequent studies . Of the remaining two mutants that were unable to generate WT-like colony-prints inside the agar ( Fig 2A ) , we determined they had transposon inserts in either the glnA ( glutamine synthetase ) or rpiA ( ribose-5-phosphate isomerase ) genes . The glnA::TnM , but not the rpiA::TnM strain , exhibited a slight growth defect in liquid MOLP ( S2B Fig ) , but both strains formed external colonies similar in size to those of WT ( Fig 2A ) . The external and internal numbers of bacterial cells were next determined by differential CFU analysis . No significant differences were found in the CFU counts of the glnA::TnM and rpiA::TnM mutants on the agar surface in comparison with their paternal strain ( S2C Fig and S1 Table ) . However , inside the semisolid surface , the rpiA::TnM and glnA::TnM mutants exhibited a significant reduction in the CFU counts in comparison with their parental strain ( 6x109; Fig 2B ) , consistent with the visible decrease in the colony-print generated by these two transposon mutants ( Fig 2A ) . Genetic complementation with plasmids expressing either RpiA or GlnA correspondingly restored the invading phenotype of these mutant strains ( Fig 2A and 2B ) . SEM analysis of external cells of six-day-old colonies revealed that while all strains exhibited diplococcal morphology , rpiA::TnM cells were bigger than both WT and glnA::TnM cells ( Fig 2C ) . Most importantly , the extracellular matrix normally covering and connecting WT E . faecalis cells ( Figs 1E and 2C ) was either decreased or almost absent in glnA::TnM or rpiA::TnM mutants , respectively ( Fig 2C and 2D ) . Together , these results indicate that GlnA and RpiA are necessary for efficient penetration of E . faecalis into semisolid surfaces , and that mutants lacking these genes also failed to produce the extracellular matrix that naturally covers the WT cells . We next determined the molecular mechanisms by which GlnA and RpiA promote E . faecalis semisolid surface penetration . Since both enterococcal mutants unable to pass into agar failed to produce the extracellular matrix evident in the colony-prints of the parental strain ( Figs 1E , 1F , 2C and 2D ) , we hypothesized that extracellular factors produced by WT cells could restore penetration by strains lacking GlnA and RpiA . To test this idea , WT cells expressing m-Cherry were independently mixed with GFP-labeled glnA::TnM or rpiA::TnM mutants , and colony-prints were analyzed after 6 days via fluorescence stereomicroscopy . WT invading cells formed a bright red fluorescent colony-print , whereas monocultures of glnA::TnM or rpiA::TnM showed decreased invasion capacity as evidenced by minimal GFP-derived fluorescence . However , a remarkable increase in GFP-positive invading cells was found when either mutant was co-cultured with WT cells ( Fig 2E ) . These observations were consistent with the higher relative fluorescent intensity ( RFI ) observed with the colony-prints from mutants co-cultured with WT than those obtained from the monoculture area ( Fig 2F ) . Hence , extracellular factors produced by the WT strain can restore the agar penetration defects intrinsic to the glnA::TnM and rpiA::TnM mutant cells . GlnA and RpiA participate in key metabolic pathways ( Fig 2G ) . GlnA plays an essential role in the metabolism of nitrogen by catalyzing the condensation of glutamate and ammonia ( NH3 ) to generate glutamine [27] . RpiA catalyzes the reversible conversion of ribose-5-phosphate to ribulose-5-phosphate , a central enzymatic reaction in the pentose phosphate pathway [28] . We hypothesized that the metabolic functions of GlnA and RpiA may converge in the hexosamine biosynthetic pathway , where the glutamine produced by GlnA , together with fructose-6-phosphate generated from metabolites of the pentose phosphate pathway , could promote the formation of glucosamine-6-phosphate [29 , 30] . We further postulated that decreased ability to penetrate MOLP by glnA::TnM and rpiA::TnM mutants could be a consequence of alterations in the levels of intracellular hexosamine biosynthetic pathway metabolites ( Fig 2G ) . To test these hypotheses , we performed a metabolic complementation assay by supplementing exogenous substrates related to this pathway . Semisolid surface penetration by WT cells was not altered by addition of any of the substrates to the medium ( Fig 2H ) . However , exogenous glutamine , glucosamine and GlcNAc , but not glutamate or fructose , rescued the defective penetration phenotype of glnA::TnM cells . In addition , penetration of rpiA::TnM mutants was restored by fructose , glucosamine or GlcNAc supplementation ( Fig 2H ) . These data suggest that low availability of cellular fructose-6-phosphate ( in rpiA::TnM ) or glutamine ( in glnA::TnM ) compromises the hexosamine biosynthetic pathway and consequently , decreases the cellular levels of products of this pathway such as glucosamine-6P and UDP-GlcNAc that are required for enterococcal migration into semisolid MOLP . We postulated that products derived from UDP-GlcNAc could mediate agar penetration by E . faecalis . Interestingly , we observed that E . faecalis readily produced extracellular GlcNAc-containing products , as evidenced by staining with wheat germ agglutinin ( WGA; Fig 3A ) that binds to GlcNAc residues [31] . S . aureus MN8 , a bacterial strain that has been shown to secrete GlcNAc-derived polymers[8 , 32] , was used in this assay as a positive control ( Fig 3A ) . Since UDP-GlcNAc is a common precursor for the synthesis of bacterial cell walls and some polyGlcNAc exopolysaccharides , such as PNAG ( PIA ) [9] , we hypothesized that polyGlcNAc-containing exopolysaccharides could mediate enterococcal semisolid surface penetration . To test this , we examined the surface of enterococcal colonies grown on MOLP by immunofluorescence using the monoclonal antibody ( mAb ) F598 , which specifically recognizes β-1 , 6-linked polyGlcNAc polymers [8 , 11] . We detected polyGlcNAc-containing polymers on both the WT enterococcal cell surface and the positive control S . aureus ( Fig 3A ) . The staining specificity was confirmed with an S . aureus strain ( Δica ) unable to produce the polyGlcNAc polymer , PNAG ( PIA ) [32] ( S3A Fig ) . Further validating these results , the presence of these polymers was decreased and severely mislocalized in both WT E . faecalis and the positive control S . aureus MN8 upon treatment with Dispersin B ( DspB; Fig 3A ) , an enzyme that specifically cleaves the β-1 , 6 linkage of glucosamine and depolymerizes PNAG ( PIA ) [33] . DspB treatment did not affect the binding of WGA to any WT strain ( Fig 3A ) , suggesting that this lectin may react with additional cellular components different from β-1 , 6-linked GlcNAc polymers . Indeed , WGA has been shown to detect not only GlcNAc residues , but also β-1 , 4-linked GlcNAc oligomers [31] such as the exposed-GlcNAc residues of the peptidoglycan layer of Gram ( + ) bacteria [34] . This observation was further confirmed by the remaining positive WGA signal found in the PNAG ( PIA ) -deficient S . aureus mutant Δica ( S3A Fig ) . In contrast to WT E . faecalis , polyGlcNAc-containing polymers were not detected in glnA::TnM or rpiA::TnM mutants stained with mAb F598 ( Fig 3B ) . Strikingly , metabolic complementation using GlcNAc-supplemented media restored polyGlcNAc-derived polysaccharide production ( Fig 3B ) and semisolid surface penetration ( Fig 2H ) in these two mutant strains . We performed colony immunoblot assays to define the localization of polyGlcNAc-containing polysaccharides . Non-lysed E . faecalis cells from colonies grown on MOLP were transferred onto nitrocellulose membranes and incubated with mAb F598 to detect polymer production . Consistent with our microscopy results , we only observed a strong signal in the WT strain but not in glnA::TnM and rpiA::TnM mutants . Complementation by either addition of exogenous GlcNAc to the media ( S3B and S3C Fig ) or with plasmids expressing either RpiA or GlnA correspondingly ( Fig 3C ) restored extracellular polyGlcNAc-derived polysaccharide production . Similarly , the control S . aureus MN8 also demonstrated positive detection in these analysis ( Fig 3C; and S3C Fig ) . To further characterize the exopolysaccharides produced by E . faecalis grown on semisolid surfaces , we used calcofluor white ( CFW ) , a fluorescent dye known to bind surface fibrillar exopolysaccharides harboring either β-1 , 3 or β-1 , 4 linkages such as cellulose and chitin [35–37] . In contrast to the CFW positive binding observed with Candida albicans colonies , a strain known to synthesize chitin ( a β-1 , 4-linked oligosaccharide ) [38] , E . faecalis colonies did not produce CFW-reactive exopolysaccharide under our conditions . Similarly , the negative control Escherichia coli DH5α [39] , did not exhibit any fluorescence with CFW in the culture media ( S3D Fig ) . Taken together , these data suggest that E . faecalis produces β-1 , 6-linked GlcNAc-containing polysaccharides that are extracellularly localized and necessary for agar penetration capacity . In S . aureus , the synthesis of polyGlcNAc polymers , such as PNAG ( PIA ) , depends on the expression of biosynthetic enzymes encoded by the icaADBC operon [40–42] . IcaA is a glycosyltransferase that uses UDP-GlcNAc as a substrate [40] , and IcaB is responsible for the deacetylation of PNAG ( PIA ) [43] . Since in silico analyses revealed that E . faecalis does not have homologs of these genes , we used Nanostring technology [44 , 45] to identify potential glycosyltransferase genes that could be involved in synthesizing polyGlcNAc-containing polysaccharides that mediate E . faecalis agar penetration . Transcript levels of genes encoding putative glycosyltransferases were determined in cell lysates from E . faecalis colonies undergoing agar penetration , normalizing gene expression to multiple independent housekeeping genes ( S2 Table ) . Several genes demonstrated significant expression changes during the semisolid surface entering process ( S4A Fig ) . We focused on EF2170 ( epaX ) because its transcript levels were markedly elevated in cells that entered the agar , compared with non-penetrating cells on the surface ( S4A Fig ) . To determine the role of EpaX in enterococcal semisolid surface penetration , we tested a strain harboring mutations in the epaX gene ( EF2170 ) [46] . This mutant had no apparent growth defects under our conditions ( Fig 4A and S4B Fig ) , but demonstrated a substantial defect in agar penetration , which was corrected upon genetic complementation with a plasmid expressing EpaX ( Fig 4A ) . Further confirming these results , deletion of epaX in a closely-related E . faecalis strain ( MMH594 ) also resulted in attenuated semisolid surface invasion ( S4C Fig ) . Similarly to glnA::TnM and rpiA::TnM strains , SEM analysis of external E . faecalis cells in six-day-old colonies revealed that ΔepaX showed a profound reduction in the extracellular matrix normally covering and connecting its WT counterpart ( Fig 4B and 4C ) . To further characterize the role of EpaX in the synthesis of polysaccharides under our penetration conditions , we used polyacrylamide gel electrophoresis with subsequent alcian blue and silver nitrate staining to analyze the polysaccharide content of WT and ΔepaX extracted from colonies grown on MOLP . Consistent with previous results [46] , a band disappeared in the ΔepaX strain , which was restored upon genetic complementation ( S4D Fig ) , suggesting drastic changes in the oligosaccharide composition between these strains . Indeed , further analysis using acid methanolysis combined with gas chromatography-mass spectrometry ( GC-MS ) determined that loss of EpaX severely altered the glycosyl composition of E . faecalis ( Fig 4D ) . Specifically , ΔepaX stains demonstrated increased glucose content that was accompanied by a profound reduction in rhamnose , GalNAc and GlcNAc , compared with their parental strain ( Fig 4D ) . These data suggested that EpaX might be required for the synthesis of GlcNAc-containing exopolymers that are necessary for optimal E . faecalis semisolid surface penetration . To test this idea , we performed immunofluorescence analyses using mAb F598 . Notably , E . faecalis ΔepaX was not recognized by the antibody ( Fig 4E and S4E Fig ) , but this defect could be corrected upon genetic complementation with a plasmid encoding EpaX ( Fig 4E ) . The binding of WGA to E . faecalis was unaltered in the absence of EpaX ( S4E Fig ) , suggesting that this putative glycosyl transferase is necessary to generate β-1 , 6-polyGlcNAc-containing polymers , but not to synthesize other GlcNAc-containing cellular components or polysaccharides detected by the lectin . Moreover , colony immunoblot assays further demonstrated that polyGlcNAc-containing exopolysaccharides were detected only in colonies from strains with a functional EpaX ( Fig 4F ) . To define whether EpaX operates upstream or downstream of GlnA and RpiA , we tested if exogenous fructose , glucosamine or GlcNAc could rescue the defective invasive phenotype of ΔepaX , as previously observed in glnA::TnM and rpiA::TnM mutants ( Fig 2H ) . The WT parental strain formed bigger colonies and penetrated more efficiently when fructose , glucosamine and GlcNAc were supplemented . However , none of these substrates rescued penetration in ΔepaX strains ( Fig 4A and S4C Fig ) . Supplementation of exogenous GlcNAc also failed to restore polyGlcNAc-containing polymer production by the ΔepaX strain ( Fig 4E ) . Strikingly , however , exogenous addition of exogenous purified PNAG ( PIA ) from S . aureus MN8 fully restored invasion by ΔepaX strains ( Fig 4G ) . CFUs quantification of invading cells confirmed that S . aureus MN8-derived PNAG ( PIA ) rescued the attenuated invasive phenotype observed in ΔepaX ( Fig 4H ) . No major bacterial growth defects were observed upon PNAG ( PIA ) addition to semisolid media ( S4F and S4G Fig ) . Together , these results demonstrate that EpaX functions downstream of GlnA and RpiA to drive β-1 , 6-linked polyGlcNAc polymer-mediated surface penetration in E . faecalis . E . faecalis has the potential to translocate from the gastrointestinal tract to the blood stream [47] , most likely via a paracellular mechanism that allows the bacterium to move through epithelial cell monolayers [48] . We hypothesized that E . faecalis mutants defective in biosynthesis of polyGlcNAc-containing polymers and semisolid surface penetration would also be altered for translocation through intestinal epithelial barriers . To this end , we used a previously described two-chamber transcytosis system [49 , 50] where translocation is evaluated by determining the bacterial number ( as CFUs ) capable of passing from the apical side through T84 human intestinal epithelial cell monolayers , to the basolateral side of the chamber ( Fig 5A ) . We found that 8 hours post-infection , the integrity of inoculated and non-inoculated monolayers exhibited transepithelial resistance values similar ( ~8 , 900 Ω/cm2 ) to those obtained prior to bacterial inoculation ( ~8 , 300 Ω/cm2 ) , thus indicating that the T84 cell monolayers remained mostly intact throughout the experiment . All strains evaluated reached approximately 108−109 CFUs/mL in the apical side of all the wells tested ( Fig 5B , S5A and S5B Fig ) . Consistent with previous reports [49 , 50] , the negative control E . coli DH5α was not detected in the lower chamber of any of the inserts analyzed ( Fig 5B , S5A and S5B Fig ) , but WT E . faecalis , in sharp contrast , demonstrated a remarkable capacity to translocate in this assay . When E . faecalis mutant strains were evaluated , reduced numbers of rpiA::TnM cells ( ~5x102 CFUs/mL ) were detected in the basolateral section in comparison with its parental WT strain ( ~6x107 CFUs/mL; S5A Fig ) . Interestingly , the glnA::TnM mutant did not show a significant decrease in translocation , likely due to metabolic complementation by exogenous glutamine present in the translocation culture medium ( S5A Fig ) . Indeed , removing glutamine from the system drastically attenuated the translocation capacity of glnA::TnM but not WT cells ( ~4x102 vs . ~1x108 CFUs/mL , respectively; S5B Fig ) . Similarly , while WT E . faecalis moved efficiently through epithelial cell monolayers , ΔepaX demonstrated a significant decrease in translocation ( ~2x109 and ~1x103 CFUs/mL for WT and mutant , respectively; Fig 5B ) . Of note , genetic complementation of rpiA::TnM , glnA::TnM or ΔepaX cells restored the ability of each corresponding mutant strain to translocate ( Fig 5B , S5A and S5B Fig ) . As control in our assays , we used a ΔepaB deletion strain unable to produce the glycosyl transferase EpaB ( Orfde4 ) , a protein previously shown to be necessary for efficient E . faecalis translocation through human epithelial cell monolayers [6 , 49] . Surprisingly , under our conditions , the translocation ability of the ΔepaB mutant was similar to its parental strain ( S6A Fig ) . In addition , ΔepaB was capable of producing polyGlcNAc-containing polymers at similar levels as the WT strain ( S6B and S6C Fig ) , and exhibited normal capacity to penetrate semisolid agar ( S6D Fig ) . These data suggest that , under our experimental conditions , the synthesis of polyGlcNAc-containing polymers is sufficient to enable surface penetration by strains devoid of EpaB . To further characterize E . faecalis translocation , T84 human intestinal epithelial cell monolayers were infected with GFP-labeled E . faecalis parental and mutant strains . Immunofluorescence analyses were performed by reacting each sample with phalloidin , DAPI and mAb F598 to visualize the actin cytoskeleton , nuclei ( and bacterial DNA ) and polyGlcNAc-containing polymers , respectively . Laser scanning confocal microscopic analyses of stained samples were carried out to localize bacteria within the T84 monolayers . After two hours of infection , we observed that enterococci were frequently co-localized with actin-rich areas ( Fig 5C and S5C and S5D Fig ) . Moreover , the orthogonal views showed that bacterial aggregates concentrated on the top of the monolayers where parental strains ( WT ) formed surface invaginations , in comparison with the smooth surface of intact T84 human intestinal epithelial monolayers ( Fig 5C , S5C and S5D Fig ) , hence suggesting that WT strains alter the actin cytoskeleton during the translocation process . These surface perturbations were observed to a lesser extent in monolayers infected with either ΔepaX ( Fig 5C ) , rpiA::TnM ( S5C Fig ) or glnA::TnM mutants ( S5D Fig ) . Importantly , polyGlcNAc-containing polysaccharides were detected around WT cell aggregates , but not in any of the mutants tested ( Fig 5D , S5C and S5D Fig ) . These polysaccharides were frequently found to cover ( or be adjacent to ) bacterial cells , and their presence was visualized even after 6 hours post-infection ( Fig 5D ) . Interestingly , we only observed surface openings of the epithelial barrier upon incubation with WT strains , suggesting that host cell lysis is caused by E . faecalis during the translocation process ( Fig 5C and 5D and S5C and S5D Fig ) . Taken together , our data reveal that E . faecalis utilizes the RpiA-GlnA-EpaX axis to generate β-1 , 6-linked polyGlcNAc-containing exopolysaccharides necessary for optimal migration into semisolid surfaces and for efficient paracellular translocation through human epithelial cell monolayers .
In this study , we uncovered molecular pathways and metabolic mediators that endow E . faecalis with the capacity to move into semisolid surfaces and translocate through human epithelial cell barriers ( proposed model; Fig 6 ) . Exopolysaccharides have been well characterized as prominent components of the extracellular matrices of surface-associated multicellular communities termed biofilms [51–53] . Our study reveals a new role for polyGlcNAc-containing extracellular polysaccharides as key mediators of E . faecalis migration traits . These exopolysaccharides may operate as a “glue” that holds cells together [52 , 54 , 55] while promoting the formation of matrix-encased multicellular aggregates during enterococcal migratory behavior . Indeed , it has been proposed that polyGlcNAc polymers facilitate intercellular adhesion by bridging electrostatic interactions between cells surfaces [56] . Alternatively , or in addition , polyGlcNAc-containing hydrated exopolysaccharides may help E . faecalis to spread in a manner similar to that found in Proteus mirabilis , which secretes polysaccharides that create a fluidic environment promoting movement on surfaces with low moisture [57] . Furthermore , in B . subtilis , a PNAG ( PIA ) -defective strain was shown to lose its hydrophobic or nonwetting surface characteristics [58] , indicating that polyGlcNAc polymers provide a means to shape the external environment in a manner amenable to bacterial movement or penetration . Additional biophysical and chemical analyses are thus warranted to comprehensively understand how these glycopolymers promote surface penetration by E . faecalis . Our study unearths new metabolic factors mediating enterococcal surface penetration . The first one is GlnA , which plays an essential function in the generation of glutamine [27] that is used as a constituent of proteins and a nitrogen donor for many biosynthetic reactions [59 , 60] . The second factor is RpiA , which catalyzes a central enzymatic reaction in the pentose phosphate pathway that is a major route of intermediary carbohydrate metabolism . RpiA is also involved in the generation of lipopolysaccharide components in Gram-negative bacteria [28] . Our results indicate that the metabolic functions of GlnA and RpiA converge in the hexosamine biosynthetic pathway to generate the UDP-GlcNAc necessary to produce polyGlcNAc-containing polymers such as PNAG ( PIA ) [61] . Based on our genetic and metabolic supplementation experiments , we propose that the hexosamine biosynthetic pathway and the pentose phosphate pathway supply metabolic substrates that serve as precursors for synthesizing enterococcal polyGlcNAc-containing polymers ( see proposed model , Fig 6 ) . A link between the pentose phosphate pathway and polysaccharide synthesis was previously described in bacteria . Somerville and colleagues reported that the S . aureus transcriptional regulator RpiR , which is known to control rpiA expression , also acts as a sugar-responsive regulator that modulates polysaccharide synthesis in response to metabolite concentrations [62] . The biosynthesis of polysaccharides first occurs in the cytoplasm , and the repeating units are then assembled and exported to the surface . This process involves several key enzymes including glycosyltransferases that mobilize sugar units [63] . Our study emphasizes a major role for the putative glycosyltransferase EpaX in E . faecalis physiology , as this enzyme was pivotal for semisolid surface penetration and paracellular translocation . Supporting this concept , Rigottier-Gois and colleagues had demonstrated that EpaX is a major determinant of E . faecalis intestinal colonization in mice [46] . Of note , the penetration-defective phenotype of ΔepaX could not be complemented by exogenous GlcNAc , indicating that EpaX is required for synthesis of the polyGlcNAc structure needed for optimal surface migration . Our data also show that EpaX acts downstream of RpiA and GlnA , which explains why only the addition of exogenous PNAG ( a polyGlcNAc polymer ) could rescue the penetration-defective phenotype of ΔepaX strains . Consistent with the notion that GlcNAc-derived polysaccharides are necessary for semisolid surface penetration , we demonstrated that epaX mutants do not produce detectable amounts polyGlcNAc-containing exopolymers . Interestingly , bioinformatic analysis using the Protein Homology/AnalogY Recognition Engine ( Phyre2 ) [64] indicated that EpaX has 100% similarity across its predicted secondary structure to glycosyltransferases such as N-acetylgalactosamyltransferases . Furthermore , analysis using the conserved domain architecture retrieval tool ( CDART ) revealed that EpaX also has similar domain architecture to YdaM , a putative glycosyltransferase shown to be required for exopolysaccharide synthesis in Bacillis subtilis [65] . However , the function of EpaX remains elusive , and its activity might have an epistatic relationship with other factors required for the production or cell surface display of polyGlcNAc-containing polymers . Recent studies proposed that epaX deletion alters the synthesis of the rhamnopolysaccharide EPA in E . faecalis by compromising the decoration of these polymers with galactose and/or GalNAc . Therefore , it was suggested that EpaX functions as a GalNAc transferase [46] . Our findings indicate that , under our conditions , the absence of EpaX in E . faecalis not only dramatically decreases the levels of GalNAc- , but also of rhamnose- and GlcNAc-containing oligosaccharides . Though rhamnose was not detected in the polysaccharides produced by ΔepaB using GC-MS analysis [6] , we found that E . faecalis lacking EpaB was still able to penetrate and generate polyGlcNAc-containing polymers , suggesting that the presence of GlcNAc , but not rhamnose , in E . faecalis exopolymers is necessary for optimal penetration into semisolid surfaces . While the structure of E . faecalis EPA has not been elucidated , similar polysaccharides with branching structures composed by other oligosaccharides bound to GlcNAc or terminal β-linked GlcNAc side chains have been evidenced in other Gram-positive bacteria [66] . Indeed , our results using DspB demonstrated the presence of β-1 , 6 glycosidic bonds within the structure of E . faecalis polyGlcNAc-containing exopolysaccharides . However , the precise nature of the polymer involved in E . faecalis semisolid surface and epithelial barrier penetration has not yet been elucidated by purification and chemical analyses . Indeed , either EPA or another polysaccharide yet to be identified might mediate the penetration process . Future analyses to determine the structure of E . faecalis polyGlcNAc-containing exopolymers , and their link with EPA , will hence be of significant interest . E . faecalis is a leading cause of nosocomial infections world-wide [67] . It has been shown that E . faecalis can translocate across mouse and rat intestinal tracts to reach other body sites [68 , 69] . Most recently , Krueger et al . reported that after feeding mice with antibiotics , E . faecalis could be found in the liver , spleen , and mesenteric lymph nodes [70 , 71] . PolyGlcNAc-like polysaccharides might mediate these processes by promoting enterococcal translocation across the intestinal epithelium . Interestingly , E . faecalis has been shown to form microcolonies surrounded by an extracellular matrix that not only covers the bacterial cells , but also extends into the intestinal space between cell clusters [72] . In line with our observations during semisolid surface invasion and epithelial barrier assays , Peng and collaborators described that E . faecalis formed cellular aggregates that localized with the actin cytoskeleton during the process of translocation [48] . Our findings therefore uncover that production of polyGlcNAc-containing exopolysaccharides is a mechanism that enables non-motile E . faecalis to penetrate semisolid surfaces and cross human intestinal epithelial cell monolayers .
S3 Table describes all strains and plasmids used in this study [6 , 10 , 26 , 32 , 46 , 73–83] E . faecalis was cultured overnight at 37°C in Tryptic Soy Broth ( TSB ) with 0 . 25% Glucose ( Becton Dickinson ) under shaking conditions , unless indicated otherwise . E . coli strains were cultured in Lysogeny Broth ( LB ) . Antibiotics were added to the medium when appropriate as follows: Chloramphenicol 10 μg/mL , spectinomycin 150 μg/mL or ampicillin 100 μg/mL for E . coli . Either tetracycline 15 μg/ml , chloramphenicol 10–15 μg/mL or spectinomycin 750 μg/mL for E . faecalis strains when specified . All chemicals were purchased from Sigma-Aldrich unless stated otherwise . 2 μL of TSB-grown E . faecalis overnight cultures were inoculated onto modified medium optimal for lipopeptide production ( MOLP ) [24] , containing 30 g/L peptone , 7 g/L yeast extract , 1 . 0 mM MgSO4 , 25 μM MnSO4 , 25 μM FeCl2 , 0 . 001 mg/L CuSO4 , 0 . 004 mg/L Na2MoO4 , 0 . 002 mg/L KI , 5 μM ZnSO4 . 7H2O , 0 . 001 mg/L H3BO3 , 25 mM potassium phosphate buffer ( pH 7 ) , 125 mM MOPS ( morpholinepropanesulfonic acid; pH 7 ) and 10 g/L agar ( Becton Dickinson ) . Saline solutions were filter-sterilized independently before mixing the MOLP components . Semisolid MOLP agar was prepared the day before and air-dried ( opened plates inside the biological hood ) for at least 30 minutes prior to bacterial inoculation . E . faecalis MOLP-inoculated plates were incubated upside down in a highly humid environment to avoid dryness at 37°C for 6 days , unless indicated . After this period , semisolid media penetration was determined by removing all cells above the agar with 3 to 4 washes with ~10 mL distilled water and then observing bacterial growth within the agar . When stated , MOLP media was solidified with poloxamer-407 ( Sigma , Aldrich; MOLP-407 ) , a fully autoclavable copolymer based on polyoxyethylene and polypropylene previously used for bacterial media growth development [84] . At low temperature , a poloxamer-407 solution is liquid , but becomes solid upon reaching room temperature ( RT ) . MOLP-407 was prepared by the addition of 10 g of the polymer powder each day into 50 mL distilled water held at 4°C until a concentration of 60% [w/v] was achieved . This solution was then kept at 4°C for an extra 24 hours to ensure complete dissolution , prior to autoclaving . Next , it was cooled to RT and chilled to 4°C to liquefy . Once at low temperature , the poloxamer-407 solution was mixed 1:1 with a cold solution of 2X MOLP to a final volume of 100 mL . Subsequently , 1 . 5 mL of this chilled media were added into each well of 24-well plates ( Falcon , Corning ) and allowed to solidify at RT prior to inoculation with 1 μL of each bacterial strain grown overnight in TSB . Colony forming units ( CFU ) of penetrating and non-penetrating E . faecalis cells grown on MOLP ( for 6 days ) solidified with either 1% ( w/v ) agar or 30% [w/v] poloxamer-407 were measured using two distinct strategies: To determine the number of external/internal cells grown on MOLP-407 , the external cells from the colonies were collected and suspended in 500 μL of saline solution ( 0 . 89% NaCl ) . Subsequently , the surface of the plates was washed 3–4 times with 10 mL of sterile distilled water at RT , and invading bacteria were recovered by transferring the growth from each well into media previously chilled at 4°C to sterile Eppendorf tubes . All bacterial suspensions ( internal and external cells ) were centrifuged at 4°C and washed 3 times with ice-cold saline solution prior to making serial dilutions and plating on TSB agar plates . After 24 hours of incubation at 37°C , the final CFU number was calculated . To quantify invasion of E . faecalis colonies grown on MOLP with 1% agar , the colonies were grown on top of 3 . 0 μm filters ( Whatman ) to separate the external from internal cells . The first ones ( external ) were collected by suspending each filter and suspended them in 500 μL of 1X Dulbecco’s Phosphate Buffered Saline solution ( DPBS; Corning-Cellgro ) and the remaining non-penetrating bacteria was removed by three washes with 10 mL of sterile distilled water and two washes with 70% ethanol ( 10 mL ) . The internal cells were recovered by removing an area of ~1 cm2 from the top layer , that was then suspended in 500 μL of DPBS as previously described above . Penetrating and non-penetrating bacterial suspensions were homogenized with a mortar and pestle followed a passage through a needle ( 27-gauge ) syringe and filtered with a 40 μm nylon filter ( BD Falcon ) . Final saline suspensions ( 1 mL ) were sonicated for 2 minutes ( 30 seconds ON and OFF cycles ) at 30% amplitude ( Sonics Vibra Cell ) to separate cellular clumps and then they were serially diluted and CFUs were determined as described above . Only when mutants exhibited growth differences to their parental strain , the final CFUs/mL was normalized to the absorbance ( OD600 ) of each saline suspension from which serial dilutions were performed ( Normalized CFUs/mL ) . Internal and external cells of MOLP-grown colonies were recovered and processed as described above ( see agar-penetration quantification section ) . Each penetrating and non-penetrating population was then diluted down to 0 . 5 OD600 prior to be stained with Brilliant Violet-570 ( BV-570; LIVE/DEAD staining kit—Life technologies ) for 30 min at RT in the dark , following the manufacturer’s instructions . Samples were washed twice with 1 mL DPBS and subsequently fixed with 4% paraformaldehyde ( BioWorld ) overnight at 4°C . Heat killed ( 100°C for 24 hours ) TSB-grown E . faecalis was used as dead control . Live and dead bacteria were analyzed using a BD LSRII Flow cytometer . E . faecalis colonies were grown on MOLP as described above . SEM samples were prepared as previously described [85] , with some minor modifications: External cells grown on MOLP were carefully transferred to ∼10 mm diameter pieces of 0 . 1% poly-L-lysine ( Sigma , Aldrich ) pre-treated Silicon wafers ( Ted Pella ) . Samples were then fixed in a solution with 2 . 5% glutaraldehyde , 0 . 1% DMSO ( dimethyl sulfoxide ) , 0 . 15% alcian blue and 0 . 15% safranin O [86] , at RT for 18 hours . When stated , a 90 min post-fixation step with 1% Osmium tetroxide was performed . After three 15 min washes with distilled water and dehydration through a graded series of ethanol , the samples , unless specified were infiltrated with hexamethyldisilazane ( HMDS; Sigma , Aldrich ) , through one incubation in 50% HMDS ( in 100% ethanol ) at RT for 1 hour and then two in 100% HMDS for 30 min . At this point the PDMS-bound samples were mounted on pins , dried under vacuum overnight , sputter-coated with gold–palladium alloy , and examined by SEM . For analyzing invading bacteria , small agar sections were placed on the silicon chips after removing the external cells with water; and treated as described above . In order to objectively quantify the fraction of cells covered in matrix , the SEM images were analyzed using automatic image analysis software , Ilastik 1 . 3 . 0 [87] . The software was first trained to recognize different image structures , including background , matrix and cells , based on a single SEM image only . In this training stage , we manually identified regions in the image corresponding to background , matrix and cells , which the software uses to update a machine learning algorithm . After training , the algorithm was used to automatically analyze all remaining SEM images . In those images , pixels corresponding to either matrix or cells are automatically detected , thereby providing an estimate for fraction of cell surface that is covered in matrix . In all cases , we analyzed the SEM images at the same 20 , 000 X magnification . This magnification was chosen such that we could examine as much surface as possible , without compromising on the resolution needed for automatic image analysis . A Mariner transposon insertion library in the multidrug resistant clinical isolate MMH594 [88] was constructed . E . faecalis was transformed with mariner delivery system pLB02 ( kind gift of Dr . Lynn E . Hancock ) , which is identical to progenitor pCAM45 [89] , except that erythromycin and kanamycin resistance markers were swapped for tetracycline and chloramphenicol resistance , respectively . Essentially as described in previous studies [89] , , transformants were initially selected at 30°C and the cure of the delivery vector was done at elevated temperature with selection for only the chloramphenicol resistance harbored by the transposon . A total of >300 , 000 MMH594 colonies possessing mariner insertions were collected as a pool . To find targets necessary for enterococcal semisolid surface penetration , a replica-plating method was used . Approximately 6 , 000 mariner transposon mutants grown on TSB in 96-well plates for 24 hours were replica plated onto MOLP plates to screen for agar penetration capacity . A non-penetrating phenotype was designated as the ability to form WT-like colonies without growth inside the semisolid surface . To determine the genome site insertion of the mariner transposon , we used a previously described modified arbitrary PCR method with few modifications [90] . Amplification of short DNA-fragments was performed by using Platinum PCR High Fidelity SuperMix ( Thermo Fisher Scientific ) as described by the manufacturer . External and internal oligonucleotides specific for the Tn-Mariner ( TnMextF1 , and TnMxtF2; S4 Table ) and the arbitrary primers ( STAPHarb1 , STAPHarb2 , and STAPHarb3 ) [90] were utilized for the PCR reactions . The first round was performed using the arbitrary primers STAPHarb1 and SATPHarb2 ( 0 . 6 μM ) paired with TnMexF1 ( 0 . 3 μM ) . 5 . 0 μL of a lysate of each mutant colonies obtained as previously described [91] was used for the PCR reaction: 95°C for 3 minutes; five cycles of 94°C for 30 seconds , 30°C for 30 seconds and 72°C for 1 min; then 25 cycles of 94°C for 30 seconds , 52°C for 30 seconds and 72°C for 1 minute; and finally 72°C for 5 minutes . Samples were kept at 4°C . The second PCR round was performed with primers TnMextF2 ( 0 . 3 μM ) and STAPHarb3 ( 0 . 6 μM ) as follow: 3 minutes at 94°C , 30 cycles of 94°C for 30 seconds , 55°C for 30 seconds and 72°C for 1 minute , followed by 72°C for 5 minutes . The samples were then kept at 4°C . The PCR products were visualized by agarose gel electrophoresis , and the second round PCRs containing at least one distinct visible fragment were used for further characterization . Nucleotide sequence analysis was performed with TnMextF2 primer . To identify of the Tn-Mariner insertion sites , a basic local alignment search tool ( BLAST ) was used . The mariner transposon mutants , glnA::TnM and rpiA::TnM , were complemented in-trans by inserting the corresponding WT gene in the pAT28 plasmid [82] . To this end , PCR-amplified gene products with their corresponding promoters were generated for rpiA and glnRA from purified MMH594 genomic DNA . For glnRA , we used primers JD15 and JD30 ( for sequences see S4 Table ) to amplify a fragment of 2248 bp , which included a region 388 bp upstream of glnR , as well as glnR and glnA open reading frames ( ORF ) . The PCR product was digested with EcoRI and BamHI ( NEB ) and ligated into pAT28 to generate the complementation vector pJR01 . Likewise , rpiA amplification was done using primers HV172 and HV173 ( S4 Table ) . The amplified product ( 1291 bp ) was digested with BamHI and XbaI and ligated into pAT28 to generate the complementation vector pAH01 . Plasmids , pJR01 and pAH01 , were electroporated in E . coli and after sequencing several colonies; one was selected for complementation of each transposon mutant . The complementation vectors were transformed by electroporation into the corresponding E . faecalis strains and recovered on TSB plates ( 750 μg/mL spectinomycin ) as previously described [78] . The fluorescence reporter strains were constructed by conjugation of the vector pV158-GFP between the donor E . faecalis OG1RF and the recipient MMH594 rpiA::TnM and glnA::TnM strains , as previously reported [92] . Briefly , TSB-grown overnight cultures of donor ( 15 μg/mL tetracycline ) and recipients ( 10 μg/mL chloramphenicol ) were diluted down to 0 . 05 OD600 and allowed to reach and absorbance of O . 5 OD600 . Then , the donor and recipient were mixed 1:10 , 10:1 and 1:1 prior to concentrating these solutions to a final volume of 50 μL . Each one was finally placed onto a 0 . 2 μm-pore-size polycarbonate membrane ( 13 mm; Nucleopore ) previously placed on TSB agar plates . After 24 hours at 37°C , filters were removed from the plates and placed in 1 ml 1X PBS ( Dulbecco’s phosphate buffer saline; Sigma , Aldrich ) . Cells suspensions were then diluted and 10−3 , 10−7 and 10−9 dilutions were plated on TSB agar with 15 μg/mL tetracycline , 10 μg/mL chloramphenicol and 250 μg/mL gentamycin . After 24 hours of incubation at 37°C , GFP fluorescent colonies were selected by microscopic analysis . The vector pV158-GFP was electroporated into electrocompetent cells of E . faecalis WT and ΔepaX strains prepared as previously described [93] . Cells were allowed to recover for 2 hours in 1 mL of SGM17MC recovery medium [93] before being plated and selected on TSB agar as described above . E . faecalis MMH594 was used for the generation of the EF2170 ( V583 epaX homolog ) deletion mutant by allelic exchange ( ef2170::spcR ) using the pMINIMAD thermosensitive plasmid [81] . Briefly , fragments upstream and downstream of the EF2170 gene were PCR amplified with Phusion polymerase ( NEB ) using JD1 , JD3; and JD6 , JD8 primers , respectively . The spectinomycin resistance gene spcR was amplified from vector pIC333 [79] with primers JD4 and JD5 . These three purified PCR fragments were assembled with Gibson Assembly Mix 2X ( NEB ) following the protocol suggested by the manufacturer . The final reaction was then used as template for amplifying a ~3 kb product ( using primers JD2 and JD7 ) that was inserted into the BamHI site of pMINIMAD generating the vector pJR02 . This plasmid was then modified by inserting in the SalI site , the chloramphenicol resistance gene cat , amplified from pLT06 [10] with primers JD44 and JD45 ( S4 Table ) generating pJR03 . This last vector was then transformed into E . coli Top10 and transformants were selected after growth overnight in LB broth with 10 μg/mL chloramphenicol at 37°C . The plasmid pJR03 was purified and electroporated into WT MMH594 as previously described [78] . Transformed bacteria were grown on Todd-Hewitt agar plates with 15 μg/mL chloramphenicol at RT . Next , positive colonies were grown overnight on TSB with chloramphenicol at RT . Cells were centrifuged , suspended in 200 μl of fresh media , and plated in TSB agar with 400 μg/mL of spectinomycin . After 48 hours at 42°C , candidate colonies were grown on TSB agar with either 400 μg/mL of spectinomycin or 15 μg/mL chloramphenicol at RT . Allelic exchange was confirmed by PCR for the spectinomycin resistant and chloramphenicol sensitive colonies . Bacterial strains were cultured in 500 mL of either MOLP broth under static conditions to an OD600 of 0 . 6 or MOLP agar for 6 days at 37°C . Polysaccharides were extracted as previously described [46 , 94] with minor modifications . Briefly , cells from either liquid cultures ( for glycosyl composition analysis ) or colonies were centrifuged 20 minutes at 4000 rpm and washed with 10 mL of sucrose-buffer ( 25% sucrose , 10 mM Tris-HCl; pH 8 ) . Pellets were then suspended in 15 mL sucrose-buffer supplemented with 1 mg/mL lysozyme ( Thermo Scientific ) and 10 U/mL mutanolysin and incubated at 37°C overnight with gentle agitation . Following this incubation , the cellular fraction was removed by centrifugation for 20 minutes at 4500 rpm . The supernatants were treated with 200 μg/mL RNase A , 200 μg/mL DNase , 5 mM MgCl2 , and 1 mM CaCl2 at 37°C for 8h to remove nucleic acids . Protein impurities were removed by adding proteinase K ( 50 μg/mL ) to each supernatant and incubating them at 42°C overnight . Remaining contaminants were extracted using 1 mL of chloroform . The aqueous phase was transferred to a new tube following centrifugation ( 4500 rpm ) for 15 minutes . Polysaccharides were precipitated by adding ethanol to a final concentration of 75% and incubation at − 80°C for 30 minutes , followed by a centrifugation ( 4500 rpm for 1 hour ) at 4°C . Precipitated pellets were washed using 75% ethanol and allowed to air dry . Cell wall polysaccharide samples were submitted for glycosyl composition analysis to the Complex Carbohydrate Research Center ( University of Georgia ) . Glycosyl composition analyses were performed using GC-MS of the per-O-trimethylsilyl ( TMS ) derivatives of the monosaccharide methyl glycosides . The TMS derivatives were produced from the sample by acidic methanolysis [95] . GC-MS analysis of TMS methyl glycosides was done on an Agilent 7890A GC interfaced to a 5975C MSD , using an Supelco Equity-1 fused silica capillary column ( 30 m x 0 . 25 mm ID ) . A total of 3 independent biological samples per strain were analyzed . To visually analyze the composition of polysaccharides extracted from non-penetrating cells of E . faecalis VE14089 WT , ΔepaX and ΔepaX p-epaX , dry samples were suspended in 100 mL of Tris–NaCl ( 50 mM Tris-HCl , 150 mM NaCl; pH 8 . 0 ) and mixed with 16% glycerol prior to be run ( 25 μL ) on a 10% polyacrylamide gel ( acrylamide to bisacrylamide , 29:1; Fisher Scientific ) in Tris-borate-EDTA buffer ( 89 mM Tris base , 89 mM boric acid , 2 mM EDTA; pH 8 . 0 ) for 90 minutes at 130 volts . Detection of polysaccharides was made with silver staining as previously described [46] with minor modifications . Briefly , the polyacrylamide gel was washed once with distilled water and incubated 45 minutes with 1 mg/mL of alcian blue in 3% acetic acid . Later , after three washes with distilled water , the gel was incubated in a solution with 3 . 4 mM K2Cr2O7 and 3 . 2 mM HNO3 for 7 minutes and then washed with water as described above . Following these steps , the gel was then treated with 12mM AgNO3 and exposed to intense light for 30 min . It was later washed with water , soaked in 50 mL of 0 . 28 M Na2CO3 and 6mM formaldehyde until signal was visually detected and transferred to a solution of 0 . 1 M acetic acid for storage . Bacterial samples from either external or penetrating E . faecalis grown on MOLP for 6 days were collected and processed as previously described [8] , with the following modification: Cells were suspended in 1000 μL of DPBS and then spotted onto microscope slides . After samples air-dried , they were fixed by methanol: acetone 1:1 for 10 min at -20°C . Then , they were treated with 100% ice-cold methanol for ~1 min , followed by 3 washes with PBS-NaCl buffer ( 20 mM PBS and 150 mM NaCl ) . Samples were blocked with 2 . 5% Normal Horse serum ( Vectors Lab ) for 45 min at RT . PBS-1% BSA ( bovine serum albumin; Sigma , Aldrich ) was then added after removing the blocking serum . After 1 min incubation at RT , slides were reacted overnight at 4°C with 20 μg/mL of human IgG mAb F598 , which specifically binds to β-1 , 6-linked GlcNAc polysaccharides [8 , 52] . After 3 washes with PBS-NaCl , samples were reacted with the secondary antibody , anti-human IgG labeled with Alexa Fluor-488 ( 15 μg/mL; Invitrogen ) and DAPI ( 2 . 0 μg/mL ) , for 2 hours at RT . To visualize GlcNAc residues , samples were incubated with 5 μg/mL of the lectin WGA ( wheat germ agglutinin ) directly conjugated to Texas Red ( Thermo Fisher Scientific ) for 30 min at RT . Slides were then washed and cover-slipped using Fluoromount-G media ( SouthernBiotech ) . Images were captured at 63x magnification at 1000 ms for DAPI , and 600–1500 ms for FITC and Rhodamine . Imaging was performed with a Zeiss AxioObserver inverted wide field/fluorescence microscope and processed using MetaMorph software . All images were adjusted to reduce background fluorescence . For enzymatic treatments , cell samples obtained as described above were diluted 1:10 in PBS . 10 μL of these cell solutions were suspended in 90 μL of Tris-buffered saline ( TBS; pH 7 . 4 ) containing 300 μg/mL DspB [96] . Samples were incubated for 24 hours at 37°C with constant shaking , and then centrifuged to suspend the pelleted cells into 50 μL of fresh TBS . Each suspension was subsequently placed onto glass slides to then be treated as described above . E . faecalis colonies were analyzed for extracellular production of polyGlcNAc-polymers using a protocol previously described [97] with some modifications: Briefly , 0 . 45 μm nitrocellulose membranes ( Bio-Rad ) were placed on 1-day-old colonies grown on MOLP until they became completely wet . The plates/membranes were incubated at 37°C for 10 minutes prior to be carefully removed and transfer colony side up to a glass petri dish for air-drying ( 10 minutes at 37°C ) . Following this step , the air-dried membranes were immersed in chloroform at RT for ~15 minutes or until the chloroform completely evaporated . Each nitrocellulose membrane was incubated colony side down for 60 minutes in the blocking buffer ( 25mM Tris-base , 0 . 15M NaCl , 0 . 1% Tween-20 , 5% non-fat milk ) . After 3 washes , 5 minutes each with TBS-T ( 25mM Tris-base , 0 . 15M NaCl , 0 . 1% Tween-20 ) , the membranes were incubated overnight at 4°C with gentle agitation in TBS-T with 5% bovine serum albumin ( BSA ) and 200 μg/mL of the primary antibody mAb F598 . Membranes were washed with TBS-T as described above and then incubated for 60 minutes in TBS-T containing a 1/10000 dilution of peroxidase-conjugated goat anti-human IgG polyclonal antiserum ( Thermo Fisher Scientific ) . Membranes were then washed 3 times for 5 min each with TSB-T and were developed using the SuperSignal West Pico Chemioluminescent Substrate Kit as directed by manufacturer ( Thermo Fisher Scientific ) . External and penetrating cells , from 2-day-old colonies grown on MOLP were collected and suspended in 2 mL of cold RNAlater . Samples were pelleted and supernatant was discharged prior to storage at -80°C . For cell lysis , these pellets were suspended in RLT buffer ( 500 μL; Qiagen ) and completely disrupted with a beat beater in one volume 0 . 5 mm zilica/sirconia beads for ~4 minutes ( 4 times × 60 seconds ) . Cellular debris were removed and supernatants were then subjected to probe hybridization and processing with the Nanostring nCounter Prep Station and Digital Analyzer according to the manufacturer’s instructions . Raw code counts were analyzed according to manufacturer’s guidelines; briefly , total transcript counts were normalized using internal controls with background subtraction . Transcript counts for 5 genes ( gyrB , def , sigA , aqpZ and folB ) were used for geometric mean normalization to correct for differences in total mRNA concentration . All data were collected from 2 biological replicates and gene expression was considered significantly altered if the transcript number changed >2-fold . Total counts were expressed as log2-change relative to the counts of non-penetrating cells at day 1 , a time point where invasion was not evidenced in MOLP . T84 human intestinal epithelial cells ( Sigma , Aldrich ) were grown and maintained as previously described [49] with some modifications . Briefly , cell monolayers were grown on plastic in a 1:1 Dubelcco’s Modified Eagle’s medium and nutrient mixture F-12 ( DMEM/F12; Corning Inc . ) supplemented with 10% heat inactivated fetal bovine serum ( FBS; Atlanta biologicals ) , 2 mM glutamine , 1 mM sodium piruvate , 10 mM HEPES buffer ( pH 7 ) , 1X non-essential amino acids , 50 units/mL penicillin and 50 μg/mL streptomycin ( Corning Inc . ) , 5 μg/mL prophylactic plasmocin ( InvivoGen ) , and 0 . 007% β-mecaptoethanol ( Sigma , Aldrich ) . When monolayers reached confluence or near-confluence , cells were detached and split as previously described [98] . Translocation was performed by seeding 105 T84 human epithelial cells from previous passages into a 24-well Transwell system with 3 . 0-μm-pore-size polycarbonate membranes ( Corning Costar Corp ) . This pore size allows bacteria , but not T84 cells , to penetrate the membrane . A volume of 300 and 1000 μL of the tissue culture medium described above was added to the apical and basolateral chambers , respectively; and this medium was changed every 2–3 days . The developing progress of T84 tight junctions was monitored by Millicell-ERS-2 measurement ( Millipore ) . Translocation experiments were performed after 8 days of culture , when the trans-epithelial electrical resistance ( TER ) of T84 monolayers reached ~8000 Ω/cm2 or higher . To prepare bacteria for translocation , 12-hours-bacterial cultures ( with appropriate media and antibiotics ) were diluted down in HBSS ( Hanks balanced salt solution without Ca2+ and Mg2+; Corning Inc . ) to an absorbance of 0 . 25 OD600 ( ~108 CFU/mL ) . Bacterial solutions were then washed twice with 1 mL of HBSS and finally suspended in Translocation Media ( TM; Gibco ) : Advanced DMEM/F-12 mixture supplemented with 5% FBS , 10 mM HEPES buffer ( pH 7 ) , 0 . 007% β-mecaptoethanol and when specified , 2 mM GluN . Prior to bacterial inoculation , the filters were washed twice with TM . After this step , 1000 μL of fresh medium were added to the basolateral chamber , and 300 μL of each TM-suspended bacterial culture prepared as described above , were inoculated to the apical side of the chamber; this inoculum is consistent with that used by others [49] and with the density of intestinal enterococci in some settings . TER was monitored at the beginning and after 8 hours post-infection . The TER values remained similar to those obtained for the pre-infected monolayers , indicating that the integrity of cell barriers was conserved throughout the experiments . CFUs of viable bacteria in both chambers were counted at 0 , and 8 hours by removing 20 μl aliquots , serially diluting and plating on TSB agar plates . For each strain , 8–9 independent transwells were used and the experiments were repeated at least three times . To visualize translocating bacteria , filters seeded with polarized human enterocyte-like T84 cells as described above were infected for 2-hours with E . faecalis constitutively expressing GFP , and then samples ( infected and uninfected ) were stained and observed by laser scanning confocal microscopy . Bacteria and epithelial cell translocation assays were done in TM supplemented with 15 μg/mL tetracycline . For immunofluorescence staining , medium on each transwell was removed and filters were washed two times with pre-warmed ( 37°C ) PBS . Cells were then fixed by 4% paraformaldehyde for 40 min . Following fixation , cells were washed with DPBS for 30 seconds , and permeabilized by incubating them with PBT ( PBS and 0 . 5% TritonX-100 ) solution for 15 minutes . The solution was removed and the cells ( transwells ) were washed twice with DPBS for 30 seconds . After this , samples were blocked with PBS-1% BSA for 1 hour at RT , and then washed once with PBS as previously described . Cells were reacted overnight at 4°C with 20 μg/mL of MAb F598 [8 , 52] . After three washes with PBS ( 5 minutes ) samples were incubated with the secondary antibody , goat anti-human IgG labeled with Alexa Fluor-647 ( 15 μg/mL; Invitrogen ) for 2 hours at RT . Thereafter , cells were washed three times with PBS ( 5 minutes ) , followed by incubation with cellular dyes ( 200nM of both Alexa Fluor 594-coupled phalloidin and DAPI; Invitrogen ) in PBS containing for 30 min in the dark at RT . The solution was removed and samples were washed three times with PBS for 30 seconds . Filters were cut and transferred into ~10 μL of ProLong Diamond Antifade Mountant ( Thermo Fisher ) , followed by sealing on glass slides and storing in the dark at 4°C until microscopy . Imaging was performed with a LSM880 confocal microscope and processed using Image J software . All images were adjusted to reduce background fluorescence . Unless noted otherwise , all experiments were repeated at least three times and results were similar between repeats . All statistical analyses were determined using GraphPad Prism 7 . 0 . Differences between the means of experimental groups were calculated using either a two-tailed unpaired Student’s t-test or one-way analysis of variance ( ANOVA ) . Error bars represent SEM from independent samples assayed within the represented experiments . P<0 . 05 was considered to be statistically significant .
|
Enterococcus faecalis is a microbial inhabitant of the human gastrointestinal tract that can cause lethal infections . Typically classified as a non-motile bacterium , E . faecalis can readily migrate and translocate across epithelial barriers to invade distant organs . Nevertheless , the molecular pathways driving enterococcal invasive attributes remain poorly understood . In this study , we uncover that E . faecalis produces a polyGlcNAc-containing extracellular glycopolymer to efficiently migrate into semisolid surfaces and translocate through human epithelial cell monolayers . Our work provides evidence that non-motile bacterial pathogens can exploit endogenous carbohydrate metabolic pathways to penetrate surfaces . Thus , targeting glycopolymer biosynthetic programs might be useful to control infections by Gram-positive cocci in the clinic .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"exopolysaccharides",
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"pathogens",
"microbiology",
"epithelial",
"cells",
"materials",
"science",
"enterococcus",
"macromolecules",
"bacteria",
"cellular",
"structures",
"and",
"organelles",
"bacterial",
"pathogens",
"digestive",
"system",
"polymers",
"polymer",
"chemistry",
"animal",
"cells",
"medical",
"microbiology",
"extracellular",
"matrix",
"microbial",
"pathogens",
"chemistry",
"enterococcus",
"faecalis",
"biological",
"tissue",
"gastrointestinal",
"tract",
"biochemistry",
"polysaccharides",
"cell",
"biology",
"anatomy",
"epithelium",
"biology",
"and",
"life",
"sciences",
"cellular",
"types",
"physical",
"sciences",
"materials",
"glycobiology",
"organisms"
] |
2019
|
PolyGlcNAc-containing exopolymers enable surface penetration by non-motile Enterococcus faecalis
|
Migration of Latin Americans to the USA , Canada and Europe has modified Chagas disease distribution , but data on imported cases and on risks of local transmission remain scarce . We assessed the prevalence and risk factors for Chagas disease , staged the disease and evaluated attitudes towards blood transfusion and organ transplant among Latin American migrants in Geneva , Switzerland . This cross-sectional study included all consecutive Latin American migrants seeking medical care at a primary care facility or attending two Latino churches . After completing a questionnaire , they were screened for Chagas disease with two serological tests ( Biomérieux ELISA cruzi; Biokit Bioelisa Chagas ) . Infected subjects underwent a complete medical work-up . Predictive factors for infection were assessed by univariate and multivariate logistic regression analysis . 1012 persons ( females: 83%; mean age: 37 . 2 [SD 11 . 3] years , Bolivians: 48% [n = 485] ) were recruited . 96% had no residency permit . Chagas disease was diagnosed with two positive serological tests in 130 patients ( 12 . 8%; 95%CI 10 . 8%–14 . 9% ) , including 127 Bolivians ( 26 . 2%; 95%CI 22 . 3%–30 . 1% ) . All patients were in the chronic phase , including 11 . 3% with cardiac and 0 . 8% with digestive complications . Predictive factors for infection were Bolivian origin ( OR 33 . 2; 95%CI 7 . 5–147 . 5 ) , reported maternal infection with T . cruzi ( OR 6 . 9; 95%CI 1 . 9–24 . 3 ) , and age older than 35 years ( OR 6 . 7; 95%CI 2 . 4–18 . 8 ) . While 22 ( 16 . 9% ) infected subjects had already donated blood , 24 ( 18 . 5% ) and 34 ( 26 . 2% ) considered donating blood and organs outside Latin America , respectively . Chagas disease is highly prevalent among Bolivian migrants in Switzerland . Chronic cardiac and digestive complications were substantial . Screening of individuals at risk should be implemented in nonendemic countries and must include undocumented migrants .
Chagas disease is a zoonosis caused by Trypanosoma cruzi ( T . cruzi ) , a flagellated protozoa transmitted to humans by the faeces of blood-sucking triatomine bugs . The parasite can also be acquired by blood transfusion , organ transplant , ingestion of food contaminated with triatomines or their feces , or congenital transmission [1] . In 2009 , we celebrate the 100th anniversary of the first complete description of the disease by Carlos Chagas , a Brazilian physician . Chagas disease affects eight to ten million people worldwide and kills more than any other parasitic disease in Latin America [2] . Until recently , its geographical distribution was mostly determined by the area of endemicity of the infected vectors . Successful vector control in endemic countries , urbanization , human migration and unpreparedness of newly affected areas have contributed to modify the distribution of Chagas disease [3] , [4] . Non-endemic countries ( i . e . countries free of vectors ) in North America , Europe and Western Pacific Region have seen the recent emergence of Chagas disease following the migration of more than 15 million people from endemic areas [4] . Estimates of the total number of T . cruzi infected people living in non-endemic countries and reported cases are both on the rise , reaching an estimated 25'000 to 40'000 in Western European countries in 2008 . [4]–[11] . These estimates are usually based on the number of registered Latin American migrants in the recipient country multiplied by the mean prevalence of the disease among blood donors in the home country . This mode of calculation has several limitations , as it does not include migrants without legal registration ( undocumented ) , and does not take into account regional variations of disease prevalence within endemic countries [12] . Acute T . cruzi infection - frequently asymptomatic - is followed by a long period of latency with few or no circulating parasites ( indeterminate form of the chronic phase ) [1] . After decades , 20–30% of infected persons develop chronic cardiac or digestive tract complications . Chronic chagasic cardiopathy ( CCC ) , which is responsible for the high morbidity , mortality , and socio-economical impact of the disease in affected areas , is frequently underdiagnosed , particularly in non-endemic countries [7] . ECG is the recommended screening test for cardiac damage [13] . Up to 10% of T . cruzi infected persons may develop gastro-intestinal motility disorder leading to progressive dilatation of the oesophagus ( megaoesophagus ) and/or the colon ( megacolon ) . Suspected digestive tract complications are investigated by barium studies . Specific clinical features of Chagas disease in non-endemic countries are not well characterized and may differ from those found in Latin America for various reasons , including different duration of exposure to infection . Until now , the few published studies describing disease patterns in Europe were conducted in specialized centres , and the findings may not be extrapolated to the global Latin American migrant community [5] , [14] . Blood-borne transmission of T . cruzi has been reported in several non-endemic countries [11] , [15] . Prevalence of infected blood donations in Europe and North America varies widely , reaching 0 . 62% in at-risk donors in Spain [15] . Recently , USA , Spain and France have implemented measures to reduce transfusional risk through blood donors screening and deferral strategies [11] . However , most European countries that may harbour blood donors at risk have yet to implement screening measures . The attitude and practice of Latin American migrants towards blood donation in non-endemic countries has yet to be investigated . In 2008 , Switzerland hosted 43'000 legal residents originating from Central and South America [16] . This figure did not include Swiss nationals from Latin American origin and the estimated 30–50'000 undocumented Latin American migrants . Undocumented migrants experience difficulties in accessing medical care in Switzerland , as health insurance is mandatory and expensive [17] . The first report of Chagas disease in Switzerland goes back to 1996 . Since then , several imported and congenital cases have been reported [18] , [19] . The objectives of this descriptive transversal study were to ( 1 ) determine the prevalence of Chagas disease in a community of Latin American adult migrants living in Geneva , ( 2 ) assess the risk factors for T . cruzi infection , ( 3 ) clinically stage the disease , and ( 4 ) evaluate the transfusional and transplantational risk to local recipients .
The study took place in a primary care centre ( the Community Care Mobile Unit ) of the Geneva University Hospitals which provides affordable care to more than 2000 Latin American migrants yearly , the majority of them living in Geneva with neither residency permit nor health insurance . Privacy is strictly ensured for undocumented persons . This unit cooperates closely with representatives of migrants communities . Information about the study was widely diffused in cultural centres , churches and migrant associations . In addition , two recruitment sessions took place in churches attended by migrants . Between June and December 2008 , all consecutive adult Latin American migrants were invited to participate to the study . Other inclusion criteria were age more than 16 years and signature of an informed consent form . Pregnant women were excluded from the study and were referred to the Maternity ward of the Geneva University Hospitals where a specific program for Chagas screening has been ongoing since January 2008 [9] . Written informed consent was requested from participants . Participants completed a questionnaire ( available in Spanish , Portuguese and French ) that collected socio-demographic and medical data , and assessed their prior and current attitudes towards blood donation and organ transplant . A multilingual volunteer was available to help onsite . Serological tests , clinical investigations and treatment were free of charge . This study was approved by the ethics committee of the Geneva University Hospitals in January 2008 ( protocol 07-285 ) . Peripheral blood was drawn by a qualified nurse and serum was kept refrigerated at −20°C . Two commercialized ELISA-based serological tests ( ELISA cruzi , Biomérieux , Brazil and Bioelisa Chagas , Biokit , Spain ) , which detect antibodies against crude and recombinant T . cruzi antigens respectively , were performed according to manufacturers' instructions with Dynatech-MRW Microplate Washer . Chagas disease was diagnosed when both tests were positive . The two tests were repeated in case of discrepant results ( e . g . positive-negative; doubtful-negative ) . External quality control consisted of testing serum samples from all individuals with positive or discordant ELISA tests and from 10% of individuals with negative tests ( Laboratory of Chagas disease , Goias University , Brazil ) . A combination of four serological tests was performed ( Chagatek ELISA , Biomérieux , Argentina; EIE Chagas Bio-Manguinhos , Brasil; Chagatest HAI , Wiener , Argentina; in-house immunofluorescent test using Biomerieux conjugate , Biomérieux , Brazil ) . Results were sent back with an integrated conclusion ( positive , negative or non-conclusive ) . All individuals with confirmed T . cruzi infection were contacted by phone and underwent a clinical evaluation that included full medical history , physical examination , and a 12-lead electrocardiogram ( ECG ) with a 30-second DII strip . In case of symptoms or signs suggestive of cardiac failure , history of syncope , or ECG changes consistent with CCC , an echocardiogram and a 24-hour Holter recording were performed . Results of cardiac investigations were independently reviewed by two cardiologists . The classification of CCC was based on the Brazilian Consensus [20] . Patients with dysphagia to solid or liquid food and/or with severe constipation ( less than 2 stools per week and/or use of laxatives more than 5 days per week for more than 6 months ) underwent gastro-intestinal tract barium examination . Oesophageal abnormalities were staged according to the classification of de Rezende [21] . The colon was considered abnormal if its diameter exceeded 6cm . In the absence of abnormal findings by ECG , echocardiography , 24-hour Holter recording , and barium studies , Chagas disease was classified in the indeterminate form of the chronic phase . According to recent recommendations , all eligible cases were treated with nifurtimox or benznidazole for 60 days [13] . In order to investigate the relationship between Chagas disease and possible predictive factors , we used 2×2 tables and performed Chi-square and Fisher's exact tests for categorical variables and unpaired Student's t-tests for continuous variables . Univariate and multivariate logistic regression analyses were used to assess factors associated with Chagas disease . All analyses were performed using SPSS for Windows ( version 15 . 0 ) .
On the basis of concordant positive serological tests , Trypanosoma cruzi infection was diagnosed in 130 participants , resulting in an overall prevalence of 12 . 8% ( 95% confidence interval ( CI ) 10 . 8–14 . 9 ) ; prevalence among Bolivians was 26 . 2% ( 95%CI 22 . 3–30 . 1; n = 127 ) . External quality control confirmed all positive cases . Three infected individuals originated from Argentina ( n = 2 ) and Brazil ( n = 1 ) ; all had lived for several years in Bolivia . All ( n = 12 ) discordant serological results in Geneva were controlled at the reference laboratory and proved to be negative . Socio-demographic characteristics and clinical data of T . cruzi infected individuals compared to non-infected ones and analysis of factors associated with infection are shown in Tables 1 and 2 , respectively . Multivariate analysis showed that major predictive factors for T . cruzi infection were Bolivian origin ( adjusted odds ratio ( OR 33 . 2; 95%CI 7 . 5–147 . 5 ) , maternal infection with T . cruzi ( OR 6 . 9; 95%CI 1 . 9–24 . 3 ) , and age older than 35 years ( OR 6 . 7; 95%CI 2 . 4–18 . 8 ) . Clinical evaluation was performed in 124 patients ( 95 . 4% ) , whereas 6 patients were lost to follow-up due to unexpected departure from Switzerland . Out of 14 patients ( 11 . 3% ) with ECG abnormalities consistent with CCC , 12 ( 9 . 7% ) were classified as grade A , one as grade B2 ( 0 . 8% ) and one could not be fully investigated ( Table 3 ) . Twelve ( 9 . 7% ) other patients with normal ECG had symptoms or signs consistent with heart disease . Seven of them underwent further investigations . Four had echocardiographic signs of low-grade diastolic dysfunction and one showed coronary sinus dilatation . Two others presented rhythmic abnormalities on Holter recording . In the absence of definite criteria defined by the Brazilian consensus , we did not consider these seven patients as cases of CCC . Twenty-one ( 16 . 9% ) patients reported dysphagia ( n = 10 ) and/or severe constipation ( n = 16 ) . Barium studies were performed in 16 patients . One patient ( 0 . 8% ) had grade I oesophageal involvement ( Figure 1 ) . 109 ( 87 . . 9% ) patients were classified as chronic infection in the indeterminate form . Two-hundred forty seven participants ( 24 . 4% ) and twenty-two patients ( 16 . 9% ) had already donated blood . Twenty-four ( 18 . 5% ) and 34 ( 26 . 2% ) patients expressed willingness to donate blood and organs outside Latin America , respectively ( Table 4 ) .
We found a high ( 12 . 8% ) prevalence of Chagas disease among 1012 Latin American migrants attending an urban primary care centre and two Latino churches in Geneva , Switzerland . This figure is much higher than previously reported in Canada ( 1% ) and Germany ( 2% ) , but lower than the very high ( 41% ) prevalence found at a referral centre in Spain [5] , [22] , [23] . Our finding is mostly explained by the high proportion ( 48% ) of Bolivian migrants in the study cohort , of whom 26 . 2% were diagnosed with Chagas disease . This figure is consistent with recent epidemiological studies conducted in affected provinces of Bolivia ( Santa Cruz , Cochabamba ) , where most of the Bolivian migrants living in Geneva originate from [24] , [25] . Bolivian participants were not over-represented in our study as they constitute 42% of Latin American migrants consulting in our primary care facility . Bolivian origin was the main predictive factor for T . cruzi infection , in concordance with other reports from non-endemic countries [5] , [9] . Only three of the 130 patients originated from other countries ( Argentina and Brazil ) . This is unclear whether these three patients were infected in their country of origin or in Bolivia , where they lived for several years . The absence of cases diagnosed in migrants from other endemic countries is likely to be explained by the insufficient number of persons tested , the possible effect of cluster sampling ( as shown in Bolivians ) and the lower average national prevalence of T . cruzi infection in other Latin American countries [4] . Nevertheless , cases originating from most Central and South American countries have been reported in non-endemic countries [5] , [14] . Therefore , consideration for T . cruzi infection should not be restricted to Bolivians . Older migrants were at increased risk for T . cruzi infection , most likely due to a longer and more intense exposition to vectorial transmission in their home country . Vector control campaigns have resulted in a sharp reduction of transmission in endemic countries during the last decades , therefore conferring relative protection to younger generations [3] . History of maternal infection ( defined by positive serology in the home country ) was also a strong and independent predictive factor for infection . Being borne from an infected mother cumulates the risk of vertical transmission and shared exposure to vectorial transmission . The risk of unrecognized vertical transmission in non-endemic countries is increased by the low proportion of patients aware of being infected , the lack of clinical signs in most infected newborns and the absence of systematic prenatal screening program [1] , [9] , [10] . Screening should be offered to women of childbearing age , pregnant women and their offspring ( in case of proven maternal infection ) [9] . Only 4% of patients had valid residency permit and health insurance . It is estimated that several millions of migrants at risk for Chagas disease reside undocumented in Europe and in North America [4] , [26] . In many countries , such as Switzerland , undocumented migrants face difficulties to access preventive and curative care . This socio-economic dimension must be taken into consideration by policy makers at the planning stage of screening programs for Chagas disease in non-endemic countries . ECG abnormalities consistent with CCC were found in 11 . 3% of cases , a proportion comparable to previous reports [5] , [27] . Most patients who could be classified were in grade A CCC according to the Brazilian consensus classification . One patient presented with advanced stage cardiopathy ( grade B2 ) requiring specific therapy . Interestingly , five symptomatic patients with normal ECG had low-grade diastolic dysfunction or coronary sinus dilatation by echocardiography and two others presented rhythmic abnormalities on Holter recording . In the absence of identified alternative aetiology , the diagnosis of early stage CCC is possible . However , the Brazilian criteria do not allow these patients to be classified as such . More studies are needed to define the diagnostic and prognostic value of echocardiography and Holter in symptomatic cases with normal ECG . The low rate of advanced CCC in our study can be explained by the overall young patients' age and , possibly , by the healthy migrant effect . The latter implies that persons who initiate a long distance migration tend to be healthier than persons who do not migrate [28] . Nevertheless , cases of advanced CCC have also been diagnosed in Geneva outside the study period [18] , [19] . The disease burden and treatment cost of CCC is recognized as an emerging challenge in some non-endemic countries like the USA and Spain [8] , [14] . Only one patient had radiological evidence of low-grade digestive tract alteration consistent with Chagas disease . This low prevalence of digestive tract complications is comparable to findings reported in Spain [5] . As strict clinical criteria were used before undergoing barium studies , we can not exclude to have missed one or more paucisymptomatic case ( s ) . T . cruzi transmission by blood transfusion has been sporadically reported in North America and in Europe [1] , [11] , [15] . Persistent parasitemia in infected blood donors can lead to infected donations over a long period of time [29] . In our cohort , 24 . 4% participants and 16 . 9% of T . cruzi infected patients had a prior history of blood donation . Despite a relatively short time ( mean: 4 . 9 years ) spent outside Latin America , 6 . 9% of participants had already donated blood in North America or in Europe . Moreover , a significant proportion of participants and of T . cruzi infected patients expressed the intention to donate blood outside Latin America in the future . This positive attitude towards blood donation and the large proportion of patients unaware of being infected highlights the risk of blood-borne transmission and support the implementation of preventive measures in non-endemic countries . Organ transplant is a rare mode of transmission that has been reported both in endemic and non-endemic countries [30] . Chagas disease can present as a fulminant systemic disease in immunosuppressed patients [1] . In our cohort , none of the participants had donated organs and none of the T . cruzi infected patients had a previous history of organ transplant . However , a high proportion of participants and cases considered organ donation while alive or after passing away . Health professionals involved in organ transplantation should be informed or reminded that organ donors or recipients at risk of being infected require screening for Chagas disease . The high proportions of migrants with no legal registration and of Bolivian origin , as well as the recruitment limited to one city , represent the main limitations of this study as they partially restrict the extrapolation of our findings to other settings . We believe that these limitations are counter-balanced by the large population screened and by the choice of a primary care setting as a recruitment site . Therefore , our study may offer a valuable insight into the current trends of this emerging health problem in Europe . According to our and others' findings , we recommend screening for Chagas disease in priority all Latin American persons at increased chance of ( 1 ) infection ( e . g . Bolivian origin , diagnosis of Chagas disease in the mother or in other close family members , prior history of blood transfusion in endemic countries , presence of suggestive cardiac or digestive complaints ) , ( 2 ) severe illness ( e . g . immunosuppressed individuals ) , ( 3 ) transmitting T . cruzi to others ( e . g . pregnant women and women of child bearing age , blood or organ donors ) , and ( 4 ) cure with existing treatments ( newborns and children ) . Cost-effectiveness studies may help to design more rational recommendations . Considering the millions of persons at risk who have recently migrated outside Latin America , medical students and physicians in non-endemic countries must be made aware of the emergence of this neglected tropical disease .
|
Chagas disease , a parasitic disease caused by Trypanosoma cruzi , is a leading cause of cardiac and digestive tract disorders in Mexico , Central and South America . An increasing number of cases have recently been reported in North America and Europe due to international human migration , but data outside Latin America remains scarce . This study showed that Chagas disease is an emerging health problem in Switzerland , affecting a substantial proportion of Latin American migrants ( 13% ) . Persons at increased risk of infection were Bolivian , older than 35 years or had a mother infected with T . cruzi . Early signs of cardiac or digestive tract disease were found in one out of six infected patients . The risk of local transmission by blood transfusion or organ transplant was illustrated by the frequent willingness expressed by patients to donate blood or organs in Switzerland . The authors recommend the screening of persons at risk of infection and the diffusion of appropriate information to the medical community to increase awareness of this emerging health problem . Considering that affected persons frequently lack health insurance in Switzerland , a facilitated access to medical care is an important step towards better recognition and management of Chagas disease .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases/protozoal",
"infections",
"infectious",
"diseases/neglected",
"tropical",
"diseases",
"infectious",
"diseases/tropical",
"and",
"travel-associated",
"diseases",
"infectious",
"diseases/epidemiology",
"and",
"control",
"of",
"infectious",
"diseases"
] |
2010
|
Prevalence, Clinical Staging and Risk for Blood-Borne Transmission of Chagas Disease among Latin American Migrants in Geneva, Switzerland
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Staphylococcus epidermidis is a leading nosocomial pathogen . In contrast to its more aggressive relative S . aureus , it causes chronic rather than acute infections . In highly virulent S . aureus , phenol-soluble modulins ( PSMs ) contribute significantly to immune evasion and aggressive virulence by their strong ability to lyse human neutrophils . Members of the PSM family are also produced by S . epidermidis , but their role in immune evasion is not known . Notably , strong cytolytic capacity of S . epidermidis PSMs would be at odds with the notion that S . epidermidis is a less aggressive pathogen than S . aureus , prompting us to examine the biological activities of S . epidermidis PSMs . Surprisingly , we found that S . epidermidis has the capacity to produce PSMδ , a potent leukocyte toxin , representing the first potent cytolysin to be identified in that pathogen . However , production of strongly cytolytic PSMs was low in S . epidermidis , explaining its low cytolytic potency . Interestingly , the different approaches of S . epidermidis and S . aureus to causing human disease are thus reflected by the adaptation of biological activities within one family of virulence determinants , the PSMs . Nevertheless , S . epidermidis has the capacity to evade neutrophil killing , a phenomenon we found is partly mediated by resistance mechanisms to antimicrobial peptides ( AMPs ) , including the protease SepA , which degrades AMPs , and the AMP sensor/resistance regulator , Aps ( GraRS ) . These findings establish a significant function of SepA and Aps in S . epidermidis immune evasion and explain in part why S . epidermidis may evade elimination by innate host defense despite the lack of cytolytic toxin expression . Our study shows that the strategy of S . epidermidis to evade elimination by human neutrophils is characterized by a passive defense approach and provides molecular evidence to support the notion that S . epidermidis is a less aggressive pathogen than S . aureus .
Staphylococcus epidermidis colonizes the epithelial surfaces of every human being . Furthermore , it is one of the most frequent causes of nosocomial infections . In addition to the abundant prevalence of S . epidermidis on the human skin , this high incidence is mainly due to the exceptional capacity of S . epidermidis to stick to the surfaces of indwelling medical devices during device insertion and form multilayered agglomerations called biofilms [1] , [2] . During infection , S . epidermidis is exposed to human innate host defenses , most notably professional phagocytes , among which neutrophils or polymorphonuclear leukocytes ( PMNs ) play a preeminent role [3] . While the biofilm mode of growth is believed to be broadly protective against host defenses [1] , [4] , we lack information on specific molecules of S . epidermidis that provide resistance to host defense mechanisms . The only S . epidermidis molecules known to facilitate evasion of killing by neutrophils are the extracellular polymers poly-N-acetylglucosamine ( PNAG , or PIA , polysaccharide intercellular adhesin ) and poly-γ-glutamic acid ( PGA ) , which inhibit uptake by neutrophils ( phagocytosis ) [5] , [6] . This is in contrast to S . aureus , a more pathogenic relative of S . epidermidis , which produces a series of proteins and enzymes dedicated to evade innate and adaptive host defense [7] , [8] . Immune evasion of S . aureus is due in part to cytolytic toxins , such as α-toxin , γ-toxin , or Panton-Valentine leukocidin , which are proinflammatory and have potential to lyse neutrophils and other leukocytes [9] . In addition , we recently identified a new class of S . aureus cytolytic toxins , the phenol-soluble modulins ( PSMs ) . Several PSM peptides have high capacity to attract , stimulate and lyse human neutrophils , and are significant contributors to pathogenesis of S . aureus bacteremia and skin infection [10] . PSMα3 , in particular , is the most cytolytic S . aureus PSM and encoded together with three other PSMs in the psmα operon of S . aureus . High expression of peptides encoded in the psmα operon is mainly responsible for the pronounced potential of hyper-virulent community-associated methicillin-resistant S . aureus ( CA-MRSA ) strains to lyse human neutrophils [10] , underpinning the importance of PSMs for neutrophil lysis . In contrast to S . aureus , toxins that lyse human leukocytes or other cell types have not been described in S . epidermidis . PSMs are characterized by common physico-chemical properties rather than similarity at the amino acid sequence level ( Fig . 1 ) . Identification of PSMs thus requires isolation and characterization by means such as mass spectrometry and Edman degradation . Using these methods , six members of the PSM family have been identified in S . epidermidis ( Fig . 1 ) [11] , [12] , [13] , [14] , but their biological significance is largely undefined . This is in part due to the fact that in earlier studies , a partially purified extract from S . epidermidis containing PSMs was used to measure PSM activities [12] , [15] , [16] , [17] . Therefore , it is possible that proinflammatory activities previously attributed to S . epidermidis PSMs were caused by contaminants such as lipopeptides , particularly as similar impurities have frequently led to the misinterpretation of stimulatory effects on innate immune system mechanisms in the past [18] . This emphasizes the need to analyze pure peptides , but pure S . epidermidis PSMs and especially cytolytic potencies of S . epidermidis PSMs have never been investigated . After phagocytosis , neutrophils kill bacteria with reactive oxygen species and non-oxygen-dependent processes [19] . Among the latter , antimicrobial peptides ( AMPs ) such as defensins and cathelicidins are believed to play a crucial role [20] . We have previously found that the secreted S . epidermidis protease SepA has considerable capacity to eliminate AMPs by proteolysis [21] . Furthermore , we identified the first Gram-positive AMP sensing system in S . epidermidis , apsRSX [22] . This system , which has also been named graRSX in S . aureus [23] , [24] , regulates a series of AMP resistance mechanisms , including Dlt-dependent D-alanylation of teichoic acids [25] , MprF-dependent lysinylation of phospholipids [26] , and an AMP exporter called VraFG [24] . However , it is not known whether Aps or SepA confer resistance to killing by neutrophils . In the present study , we examined the role of S . epidermidis PSMs in immune evasion , in particular by determining whether S . epidermidis PSMs are cytolytic toward human neutrophils . Furthermore , we analyzed whether the sepA and apsRSX loci facilitate survival during phagocytic interaction with neutrophils . Our study provides a better understanding of how S . epidermidis evades killing by human leukocytes in the susceptible host . Notably , we identified the first potent S . epidermidis cytolysin , PSMδ , a member of the α-type PSM family . However , despite the capacity to produce a potent cytolysin , S . epidermidis culture supernatants had little or no capacity to lyse neutrophils . In contrast , we show that the SepA protease and the Aps AMP sensor significantly promote resistance of S . epidermidis to killing by neutrophils . These findings provide molecular evidence to support the notion that S . epidermidis , in strong contrast to virulent S . aureus , has a defensive rather than aggressive approach to infection and immune evasion .
To evaluate the relative potency of S . epidermidis to kill human neutrophils , we compared culture filtrates of different S . epidermidis strains with those of S . aureus LAC , a CA-MRSA strain with demonstrated high capacity to lyse neutrophils [10] , [27] . We investigated four S . epidermidis strains that have been most frequently used in S . epidermidis pathogenesis studies: 1457 , O47 , ATCC12228 , and RP62A . ATCC12228 and RP62A represent the two S . epidermidis strains for which genome sequence data are available [14] , [28] . Furthermore , we included an agr mutant of strain 1457 , as the agr regulatory system is known to strictly regulate PSM production [10] , [29] , [30] . Culture filtrates of all four S . epidermidis strains showed significantly reduced lysis of human neutrophils compared to S . aureus LAC ( Fig . 2 ) , indicating that as a species S . epidermidis has low capacity to lyse neutrophils . Some low-level cytolysis was detected in culture filtrates from strain 1457 , but not strains RP62A and ATCC12228 . Furthermore , cytolytic capacity of culture filtrates was completely abolished in an agr deletion mutant of strain 1457 and in the natural agr mutant strain O47 ( Fig . 2 ) , in accordance with a potential function of the agr-regulated PSMs of S . epidermidis in neutrophil lysis . In vitro studies using S . aureus and S . epidermidis γ-toxins and S . epidermidis PSMδ indicated that PSMs lead to perturbation of synthetic membrane vesicles and likely work by pore formation in the absence of a specific receptor [31] , [32] , [33] . Presumably , the capacity of PSMs to lyse cells is thus dependent on their physico-chemical features , namely the ability to form amphipathic α-helices , a characteristic property of pore-forming peptides . To evaluate whether S . epidermidis PSMs form amphipathic α-helices , we determined secondary structures of PSM peptides using circular dichroism ( Fig . 3A , B ) . These experiments demonstrated that all S . epidermidis PSMs are predominantly α-helical . When PSM sequences were arranged in α-helical wheels , all predicted α-helices showed a distinct hydrophilic opposed to a hydrophobic side , which is characteristic for amphipathic α-helices ( shown as an example for PSMβ1 in Fig . 3C ) . These findings indicate that S . epidermidis PSMs have the basic structural requirements for membrane perturbation and pore formation . To analyze whether S . epidermidis PSMs lyse neutrophils , we incubated human neutrophils with pure , synthetic S . epidermidis PSMs . Remarkably , one S . epidermidis PSM , PSMδ , caused high levels of neutrophil lysis , to an extent comparable to that of the potent S . aureus PSMα3 ( Fig . 4A ) . In contrast , S . epidermidis δ-toxin , PSMα , and PSMε showed only very limited cytolytic capacity . The β-type PSMs were non-cytolytic toward neutrophils , in keeping with findings achieved for the β-type PSMs of S . aureus [10] . These differences indicate that while the formation of amphipathic α-helices is a likely prerequisite for membrane perturbation , further yet unknown structural features determine the degree of cytolytic activity in PSMs . This notion is also supported by our observation that the degree of α-helicity ( Fig . 3A ) did not correlate with the cytolytic potential of PSMs ( Fig . 4A ) . Of note , PSMδ to our knowledge represents the first potent cytolysin of S . epidermidis to be identified . Remarkably , PSMδ is less closely related to S . aureus PSMα3 by amino acid sequence comparison than are PSMα1 , PSMα2 , and S . epidermidis PSMε ( Fig . 1 ) , underlining the notion that cytolytic properties of PSMs are determined by secondary rather than primary structure . The strong potency of PSMδ to lyse human neutrophils was confirmed by expression of PSMδ in an agr-negative S . epidermidis strain that lacks production of PSMs ( Fig . 4B ) . Induction of PSMδ production resulted in a significant increase in the capacity of culture filtrates from the agr-negative strain to lyse human neutrophils ( p = 0 . 0015 , agr pTXpsmδ versus agr pTX16 control ) . As we have observed previously [10] , [34] , plasmid-based expression of PSM peptides often does not result in concentrations of PSMs as high as those found in wild-type culture filtrates , which also was the case for PSMδ . However , the degree of neutrophil lysis exerted by culture filtrates of the PSMδ expression strain ( 20 . 1% of that by the wild-type ) corresponded very well to PSMδ expression ( 18 . 6% of that in the wild-type ) ( Fig . 4B ) , highlighting the major contribution PSMδ has to the overall cytolytic capacity of S . epidermidis . We showed previously that S . aureus PSMs also lyse cells other than neutrophils , such as monocytes or erythrocytes [10] . To analyze whether lysis of erythrocytes by synthetic PSMs and staphylococcal culture filtrates follows the same pattern as observed using human neutrophils , we tested hemolysis . Results were in very good accordance with those achieved with human neutrophils , inasmuch as only PSMδ showed strong hemolytic activity at a level comparable to that exerted by S . aureus PSMα3 ( Fig . 5A ) . Similarly , culture filtrates of S . epidermidis strains were much less hemolytic than those of S . aureus LAC , with that of S . epidermidis 1457 causing slightly higher hemolysis than culture filtrates from the other S . epidermidis strains ( Fig . 5B ) , in keeping with the neutrophil lysis findings . The finding that S . epidermidis PSMδ has considerable cytolytic activity at first appeared to contradict the low cytolytic activity of S . epidermidis culture filtrates . Indeed , it was reminiscent of the situation in S . aureus , in which the cytolytic potential is also mostly determined by one strongly cytolytic PSM peptide , PSMα3 [10] . However , potential differences in PSM production are not considered in this comparison . Therefore , we next measured PSM production patterns in S . epidermidis strains compared to those in S . aureus . We found considerable differences in the relative PSM production patterns between S . aureus and S . epidermidis , while patterns among the different S . epidermidis strains were similar ( Fig . 6 ) . In addition to the S . epidermidis strains that are shown , we analyzed a large S . epidermidis strain collection . Results were similar in all strains , except for strains that completely lacked PSM production ( data not shown ) . These PSM-negative strains are likely functionally agr-negative , owing to frequently occurring mutations in the agr system [35] , which includes the agr-negative strain O47 [36] . The most noticeable difference between S . epidermidis and S . aureus was strongly reduced production of α-type PSMs , except δ-toxin , in S . epidermidis . In contrast , the non-cytolytic β-type PSMs represented almost half of the total PSM peptide produced in S . epidermidis , whereas concentrations of β-type PSMs were extremely low in S . aureus . Furthermore , the difference between the production levels of the most cytolytic PSMs in the two species , PSMα3 and PSMδ ( ∼5∶1 ) , correlated with the degree of overall neutrophil lysis ( ∼5∶1 , S . aureus LAC to S . epidermidis 1457 ) , underlining that these most potent PSMs predominantly determine cytolytic capacity . Moreover , the notion that any cytolytic activity of S . epidermidis is largely determined by production of PSMδ is in accordance with the observed low production of PSMδ and overall low cytolytic activity of all tested S . epidermidis strains . Thus , although S . epidermidis has the capacity to secrete a potent cytolytic toxin , PSMδ , it limits hemolysis or lysis of neutrophils by keeping production of PSMδ at a low level . The N-formyl methionine group present at the N-terminus of newly synthesized bacterial proteins is recognized by immune cells as a pathogen-associated molecular pattern ( PAMP ) [37] . Removal of the N-formyl group by bacterial peptide deformylase thus serves to evade recognition by human innate host defense . N-formylated bacterial proteins commonly are not exported with N-formyl-methionine , as their signal peptides are removed during export . In contrast , PSMs are secreted as the unaltered translation product by a yet unidentified mechanism and thus always carry N-formyl methionine , likely representing a very considerable portion of N-formylated peptides released by staphylococci [10] . In S . aureus LAC culture filtrates , about one-half of the total PSM peptide was N-deformylated , which is in good accordance with a previous report on δ-toxin deformylation in another S . aureus strain [38] . In remarkable contrast , no significant deformylation was detected in S . epidermidis PSMs ( Fig . 6 ) . Thus , despite the presence of a peptide deformylase in S . epidermidis that is highly homologous to the S . aureus enzyme ( 80% identity on the amino acid level ) , proteins are not N-deformylated in S . epidermidis as efficiently as in S . aureus . In addition to causing cytolysis , PSMs of S . aureus are known to stimulate neutrophil and monocyte chemotaxis , activate neutrophils , and elicit release of the chemokine IL-8 [10] . These proinflammatory capacities of PSMs indicate that the innate immune system recognizes PSMs as PAMPs , which as we recently discovered is achieved by recognition of PSMs by the FPR2/ALX receptor [39] . To determine S . epidermidis proinflammatory capacities , we analyzed stimulation of IL-8 release ( Fig . 7A ) . IL-8 is an important chemokine that causes recruitment of neutrophils to the site of infection [40] . PSMδ had very strong capacity to stimulate release of IL-8; but overall , stimulation of IL-8 release did not correlate with the cytolytic capacities of PSMs . Notably , all S . epidermidis PSMs to some degree stimulated release of IL-8 despite the lack of cytolytic capacity in several of them . Accordingly , capacities of S . epidermidis culture filtrates to stimulate IL-8 release were in the same range as those of S . aureus LAC ( Fig . 7B ) . Finally , stimulation of IL-8 release was significantly lower for the S . epidermidis agr mutant of strain 1457 compared to the corresponding isogenic wild-type strain , and very low for the natural agr mutant strain O47 , in keeping with strict regulation of PSMs by agr [30] . Thus , while the different PSM production pattern in S . epidermidis correlates with considerably reduced neutrophil lysis compared to S . aureus , S . epidermidis PSMs still appear to be recognized efficiently as PAMPs . These results suggest that PSM cytolytic and proinflammatory capacities are dependent on distinct interactions with host cells . Our results suggest that S . epidermidis does not use PSM cytolytic activity to a significant extent to evade killing by human neutrophils . However , the capacity of S . epidermidis to cause chronic infections indicates that S . epidermidis has means to inhibit elimination by human professional phagocytes . As an alternative strategy to evade killing by human neutrophils , bacteria may secrete enzymes to destroy – or use mechanisms to decrease – the antimicrobial efficiency of neutrophil bactericidal agents [3] . Among those agents , antimicrobial proteins and peptides likely play an important role in the killing of ingested bacteria [41] . We previously showed that the secreted S . epidermidis protease SepA has strong capacity to destroy human AMPs [21] . In addition , we identified a system that we named Aps ( for antimicrobial peptide sensor ) that senses the presence of human AMPs and coordinates a series of AMP resistance mechanisms in S . epidermidis [22] and S . aureus [24] . While the mechanistic function of these loci is thus well understood , evidence for a significant role of Aps and SepA in immune evasion using human cells is lacking . Therefore , we investigated whether S . epidermidis SepA and S . epidermidis and S . aureus Aps contribute to survival after uptake by human neutrophils . Isogenic sepA and aps mutants of S . epidermidis 1457 had significantly reduced ability to survive after phagocytic interaction with human neutrophils compared to the wild-type strain ( Fig . 8 ) , providing evidence for an important function of the aps and sepA loci in S . epidermidis immune evasion . Similarly , the Aps system had a significant impact on the survival of the S . aureus CA-MRSA strain MW2 after phagocytosis . Of note , this effect was comparable to that of the psmα locus , which encodes the most important cytolytic PSM peptides of S . aureus ( Fig . 8B , C ) . These findings indicate that the Aps AMP-sensing system has an important immune evasion task in both species , while only S . aureus makes additional use of cytolytic toxins , such as PSMs , to evade killing by human neutrophils . This discrepancy is reflected by the higher capacity of S . aureus to survive interaction with human neutrophils compared to S . epidermidis ( Fig . 8 ) .
As a commensal organism living on the human skin , S . epidermidis commonly has a benign relationship with its host and may even contribute to reducing inflammatory responses [2] , [42] . However , S . epidermidis may cause infection after breach of the epithelial barrier and entry into the bloodstream , such as through contamination of indwelling medical devices during surgery . Although most S . epidermidis infections are only moderately severe and usually chronic , their sheer frequency poses a considerable problem , predominantly in the hospital setting [2] , [43] . Despite the immense importance of S . epidermidis infections for public health , the interaction of S . epidermidis with host defenses is poorly understood . In particular , it has not been investigated in detail if and how S . epidermidis resists killing by human neutrophils , which are largely responsible for elimination of invading bacteria . Therefore , we here investigated the interaction of S . epidermidis with neutrophils . As direct lysis of neutrophils by bacterial cytolysins is an efficient means to evade killing , we focused our investigation on PSMs as the only S . epidermidis gene products with potential cytolytic activity [14] , [28] . A major finding of our study was the identification of PSMδ as the first S . epidermidis toxin with significant cytolytic capacity . However , despite the cytolytic potential of PSMδ , culture filtrates of S . epidermidis strains had very low capacity to lyse human neutrophils . Importantly , according to our findings this phenotypic difference between virulent S . aureus and S . epidermidis is caused at least in part by a pattern of PSM production in S . epidermidis that is shifted , compared to S . aureus , to PSMs with lower cytolytic potential . Thus , PSMs in S . epidermidis do not contribute significantly to neutrophil lysis , in contrast to many virulent strains of S . aureus . Likely , PSMs fulfill other roles in S . epidermidis that are yet poorly understood , such as in biofilm development [44] or bacterial interference [33] . The production of PSMs that are not potent cytotoxins would thus ascertain that S . epidermidis may cause chronic , biofilm-associated infection without promoting acute , purulent inflammation . This is in keeping with a general strategy of S . epidermidis to reside inside the human host in a state of “hiding” from the immune system . Potentially , a similar strategy is pursued by strains of S . aureus , such as functionally Agr-negative strains , which are less virulent , cause chronic rather than acute infection , and produce less cytolytic toxins , such as PSMs . In addition , our study revealed significant contributions of the SepA protease and the Aps AMP sensor/regulator to promoting S . epidermidis survival in human neutrophils . Thus , S . epidermidis is able to combat important mechanisms that neutrophils use to kill bacteria after phagocytosis . However , together with previous findings on S . aureus survival in human neutrophils [27] , our data indicate that these mechanisms are not as efficient as leukocyte toxins , underlining the notion that S . epidermidis is in general less virulent than S . aureus as a result of lower capacity to survive after neutrophil phagocytosis . This is in accordance with a very early study that showed increased survival of “pathogenic” ( i . e . coagulase-positive ) versus “non-pathogenic” ( i . e . coagulase-negative ) staphylococci in human leukocytes [45] . Nevertheless , our study shows that - combined with mechanisms preventing neutrophil phagocytosis , such as surface exopolymers and biofilm formation - S . epidermidis has a multi-faceted program providing resistance to neutrophil killing , explaining at least in part the capacity of S . epidermidis to cause long-lasting infection in the susceptible host . Moreover , as we have shown previously that SepA production is under control of Agr and SarA [21] , our findings confirm the notion that global regulatory systems play key roles in S . epidermidis immune evasion [46] , and are reminiscent of similar functions of Agr and SarA in S . aureus [47] , [48] . Finally , the observed significant effects of AMP resistance mechanisms on survival in neutrophils underline the importance of non-oxygen-dependent antimicrobial processes of the host . Collectively , our findings indicate that the molecular mechanisms that S . epidermidis uses to evade elimination by innate host defense reflect a passive defense strategy rather than use of aggressive toxins and point to a different major role of PSM production in S . epidermidis compared to S . aureus .
Human neutrophils were obtained from healthy volunteers in accordance with a protocol approved by the Institutional Review Board for Human Subjects , NIAID . Informed written consent was received from human volunteers . Bacterial strains used in this study were S . epidermidis strains 1457 [49] , RP62A [14] , [50] , ATCC12228 [28] , O47 [51] , isogenic agr , sepA , and apsS deletion mutants of strain 1457 [21] , [22] , [52] , S . aureus strains LAC ( pulsed-field type USA300 ) [53] and MW2 ( pulsed-field type USA400 ) [54] and the isogenic aps and psmα mutants of strain MW2 [24] . LAC and MW2 are virulent community-associated MRSA strains . Strains were grown in tryptic soy broth ( TSB ) . The psmδ over-expression plasmid pTXpsmδ [34] was transformed in S . epidermidis agr . Expression of PSMδ by this construct is achieved by adding xylose , which acts on a xylose-inducible promoter in front of the cloned psmδ gene [55] . PSM peptides were synthesized by commercial vendors with an N-terminal formyl methionine residue in each peptide . Peptide sequence fidelity was determined by the Peptide Synthesis Unit of the NIAID . Peptide stock solutions were prepared at 10 mg/ml in DMSO ( dimethylsulfoxide ) ; further dilutions were made in water . PMNs were isolated from venous blood of healthy volunteers as described [56] . Lysis of PMNs by synthetic PSMs or clarified S . aureus or S . epidermidis culture media was determined essentially as described [27] , [56] . Synthetic PSMs were added to wells of a 96-well tissue culture plate containing 106 PMNs and plates were incubated at 37°C . After 1 h , PMN lysis was determined by release of lactate dehydrogenase ( LDH ) ( Cytotoxicity Detection Kit , Roche Applied Sciences ) . Alternatively , S . aureus and S . epidermidis strains were cultured for 18 h at 37°C in 50 ml TSB ( +/− 0 . 5% xylose ) with shaking using a 100 ml flask . Bacteria were removed by centrifugation and culture media were sterilized by filtration and stored at −80°C in aliquots until used . Culture medium was mixed with human PMNs ( 106 ) and tested for its ability to cause PMN lysis using incubation times of up to 6 h , as indicated . For measurement of S . epidermidis/S . aureus survival after phagocytic interaction with neutrophils , PMNs ( 106 ) in RPMI were combined with ∼107 RPMI-washed bacteria from mid-logarithmic growth phase in 96-well flat-bottom microtiter plates . Plates were centrifuged at 380×g for 8 min to synchronize phagocytosis and incubated at 37°C for up to 1 h . At the desired time points , 22 µl of 1% saponin was added , well contents were mixed , and the plates were incubated on ice for 15 min . Surviving bacteria were enumerated . % survival was calculated by comparing the numbers of surviving bacteria to those at t = 0 . After isolation and washing , neutrophils were resuspended in RPMI 1640 medium ( Sigma ) supplemented with 10% human serum , 2 mM L-glutamine , 100 U/ml penicillin , 100 µg/ml streptomycin , 2 mM sodium pyruvate , and 10 mM HEPES . Cells were distributed to a 96-well culture plate at 200 µl and 5×105 cells per well . Synthetic PSMs or filtered bacterial culture supernatants were diluted in fresh culture medium ( 1∶100 ) and added to the plate at 100 µl/well . Plates were incubated at 37°C in a 5 . 5% CO2 incubator for 5 h . Then , the plate was centrifuged at 1500 rpm for 10 min , and supernatant was harvested from each well . IL-8 was measured in the culture supernatants with commercial ELISA assay kits ( R&D systems ) according to the manufacturer's instructions . Diluted culture filtrates were further diluted 1∶2 for the ELISA . Hemolytic activities of culture filtrates from 18-h cultures of S . epidermidis strains or synthetic PSM peptides at different concentrations were determined by incubating samples with sheep red blood cells ( 2% v/v in Dulbecco's phosphate-buffered saline , DPBS ) for 1 h at 37°C as previously described [10] . Assays were performed in triplicate . RP-HPLC/ESI-MS was performed on an Agilent 1100 chromatography system coupled to a Trap SL mass spectrometer using a Zorbax SB-C8 2 . 3×30 mm column as described [30] . Quantification was based on extracted ion chromatograms using the most abundant peaks of the electrospray ion mass spectra of the respective PSM peptides , with calibration using synthetic peptides , as described [30] . The structures of synthetic PSM peptides were analyzed by CD spectroscopy on a Jasco spectropolarimeter model J-720 instrument . Solutions of PSM peptides , each at 1 . 0 mg/ml , were prepared in 50% trifluoroethanol . Measurements were performed in triplicate and the resulting scans were averaged , smoothed , and the buffer signal was subtracted . Computation of relative fraction of helix , sheet , turn , and unordered structure , using 3 different algorithms , was performed according to Sreerama and Woody [57] . Statistical analyses were performed with Graph Pad Prism 5 software using t-tests or 1-way ANOVA with Bonferroni or Dunnett post tests , as appropriate .
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Staphylococcus epidermidis frequently causes chronic infections , indicating pronounced capacity to evade host defenses . However , S . epidermidis is in general much less aggressive than its close relative , S . aureus . Here we identify molecular underpinnings of that discrepancy by showing that S . epidermidis immune evasion mechanisms are limited to those involving molecules that protect against or eliminate antimicrobial agents secreted by white blood cells , while immune evasion mechanisms of virulent S . aureus include the production of destructive toxins . This is especially noteworthy , because we demonstrate here for the first time that S . epidermidis has the capacity to produce a toxin with great potential to destroy white blood cells , but keeps its production at a very limited level . Thus , our study shows that two closely related human pathogens have adapted specific molecular mechanisms to evade host defenses , reflecting the unique approach used by each to cause human disease .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"infectious",
"diseases/bacterial",
"infections",
"microbiology/cellular",
"microbiology",
"and",
"pathogenesis",
"microbiology/innate",
"immunity"
] |
2010
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Staphylococcus epidermidis Strategies to Avoid Killing by Human Neutrophils
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Mycobacterium tuberculosis ( Mtb ) must cope with exogenous oxidative stress imposed by the host . Unlike other antioxidant enzymes , Mtb’s thioredoxin reductase TrxB2 has been predicted to be essential not only to fight host defenses but also for in vitro growth . However , the specific physiological role of TrxB2 and its importance for Mtb pathogenesis remain undefined . Here we show that genetic inactivation of thioredoxin reductase perturbed several growth-essential processes , including sulfur and DNA metabolism and rapidly killed and lysed Mtb . Death was due to cidal thiol-specific oxidizing stress and prevented by a disulfide reductant . In contrast , thioredoxin reductase deficiency did not significantly increase susceptibility to oxidative and nitrosative stress . In vivo targeting TrxB2 eradicated Mtb during both acute and chronic phases of mouse infection . Deliberately leaky knockdown mutants identified the specificity of TrxB2 inhibitors and showed that partial inactivation of TrxB2 increased Mtb’s susceptibility to rifampicin . These studies reveal TrxB2 as essential thiol-reducing enzyme in Mtb in vitro and during infection , establish the value of targeting TrxB2 , and provide tools to accelerate the development of TrxB2 inhibitors .
Endogenous oxidative stress represents an inevitable challenge for microbes adapted to an aerobic lifestyle [1] . In addition , pathogens like Mycobacterium tuberculosis ( Mtb ) are confronted with exogenous oxidative stress imposed by the host [2] . The production of antimicrobial oxidants is a critical host defense mechanism against Mtb [3 , 4] . Patients with germline mutations in phagocyte NADPH oxidase resulting in an impaired macrophage respiratory burst are predisposed to mycobacterial diseases including tuberculosis [5] . Mice lacking inducible nitric oxide synthase succumb to Mtb infection much faster than their wild type littermates [3] . The reactive oxygen and nitrogen species generated by these host enzymes can inactivate microbial iron-dependent enzymes , damage lipids and destroy DNA [1 , 6] . Not unexpectedly , Mtb is armed with a number of dedicated antioxidant systems to ensure replication and survival within its host . Notable members include catalase , alkyl hydroperoxidase , superoxide dismutase , mycothiol , ergothioneine , thiol peroxidase , thioredoxin reductase and a recently identified membrane-associated oxidoreductase complex [4 , 7–13] . The thioredoxin system , together with the glutathione system , regulates many important cellular processes , such as antioxidant pathways , DNA and protein repair enzymes , and the activation of redox-sensitive transcription factors [6 , 14] . Unlike many Gram-negative bacteria , which possess both systems , Mtb lacks the glutathione system [6 , 10] . Instead , mycothiol has been suggested as substitute for glutathione in Mtb [10] . Mycothiol-deficient Mtb requires addition of catalase for growth in vitro , but is not significantly attenuated in mice [15] . In contrast , there is evidence that thioredoxin reductase ( TrxB2 ) is essential for growth in vitro , implying a unique role for TrxB2 [16–18] . Although purified TrxB2 has been shown to mediate detoxification of H2O2 , peroxide , and dinitrobenzene in vitro [12 , 19 , 20] , its role in oxidative stress defense in physiological conditions and its specific biological functions in Mtb physiology are poorly understood . Bacterial thioredoxin reductases have recently been demonstrated to be druggable targets [18 , 21] , however , it has not been determined whether inactivating TrxB2 in vivo , in acute and chronic infections , attenuates Mtb . To address these questions , we applied a tunable dual-control genetic switch [22] to generate a conditional TrxB2 mutant and evaluated the impact of TrxB2 depletion . Unexpectedly , depleting TrxB2 not only rapidly killed Mtb , but also led to bacterial lysis . TrxB2 depletion perturbed growth-essential processes , including sulfur and DNA metabolism and death could be prevented by addition of a strong disulfide reductant . In vivo depletion of TrxB2 resulted in clearance of Mtb during both the acute and chronic phases of infection . We generated deliberately leaky knockdown mutants to dissect the contribution of TrxB2 to oxidative stress detoxification and found Mtb with partially depleted TrxB2 highly susceptible to thiol-specific oxidizing stress , but , surprisingly , not to peroxide and reactive nitrogen species . The leaky knockdown mutants were used to evaluate the specificity of two TrxB2 inhibitors and revealed that targeting TrxB2 results in hypersusceptibility to the frontline anti-tuberculosis drug rifampicin .
We first established that TrxB2 is indeed required for growth of Mtb under standard laboratory conditions ( S1 Fig ) . Because a deletion mutant could not be isolated , we generated a TrxB2 dual-control ( DUC ) strain ( S2 Fig ) . In TrxB2-DUC expression of TrxB2 is controlled by both transcriptional silencing and inducible proteolytic degradation , while TrxC is constitutively expressed from its native promoter [22] . Upon addition of anhydrotetracycline ( atc ) TrxB2 protein was rapidly depleted and below the limit of detection after 6 hours , which corresponds to less than 5% of TrxB2 amount in wild type ( wt ) H37Rv ( Fig 1A and S3 Fig ) . TrxB2 depletion not only inhibited Mtb growth in nutrition-rich 7H9 medium , but also led to rapid killing ( Fig 1B and 1C ) . Bacterial viability declined by 2 . 7 log after 24 hours , and 3 . 4 log after 4 days of atc treatment , indicating that TrxB2 is required for bacterial growth and survival in replicating conditions . We also assessed the impact of inactivating TrxB2 on non-replicating Mtb , which is known to be tolerant to anti-TB drugs and , in part , responsible for the long duration of anti-TB chemotherapy [23] . TrxB2 depletion was induced with atc after 10 days of incubation in PBS . Remarkably , TrxB2 depletion killed ~90% of the bacilli after 48 h and 99 . 9% within two weeks of PBS starvation , highlighting that starvation-induced non-replicating Mtb depends on TrxB2 for survival as well ( Fig 1D and 1E ) . While culturing TrxB2-depleted Mtb in liquid growth medium , we observed that the culture gradually declined in optical density and turned clear ( Fig 1F and 1G ) . This motivated us to ask whether TrxB2 depletion caused lysis of Mtb . Notably , mycobacterial death is not always accompanied by lysis . So far , only a small number of cell-wall targeting compounds have been shown to induce lytic death [24] . To further investigate whether lysis occurred upon TrxB2 depletion , we monitored the release of the cytoplasmic enzymes enolase ( Eno ) , dihydrolipoamide acyltransferase ( DlaT ) and the proteasome beta subunit ( PrcB ) into the culture supernatant . Because Eno , DlaT and PrcB are generally not detected in the culture supernatant of intact mycobacterial cells , we consider their release as an indicator of bacterial lysis . Consistent with a previous report that meropenem-clavulanate caused Mtb lysis [24] , we found Eno , DlaT and PrcB in the culture filtrate 6 days after exposure to meropenem-clavulanate ( Fig 1H ) . There was no detectable lysis of TrxB2-DUC in the absence of antibiotic or atc , even after 9 days of incubation . In contrast , cytoplasmic proteins were readily detectable in the supernatant of TrxB2-DUC treated with atc for 6 or 9 days , confirming our hypothesis that TrxB2 depletion caused lytic death ( Fig 1H ) . In contrast , depletion of nicotinamide adenine dinucleotide synthetase ( NadE ) which also rapidly kills Mtb [22] , did not result in detectable lysis of NadE-DUC ( S4 Fig ) . Microscopic analysis revealed that lysis of TrxB2-depleted Mtb was preceded by significant cell elongation ( Fig 1I and S5 Fig ) . The majority of TrxB2-depleted bacteria were twice as long as those expressing TrxB2 , suggesting that TrxB2 depletion affects processes required for cell division . To evaluate the importance of TrxB2 for virulence of Mtb , mice were infected with TrxB2-DUC and fed doxycycline ( doxy ) containing food to inactivate TrxB2 at selected time points . The infection was rapidly cleared in mice given doxy food from the time of infection or during the acute phase of infection on day 10 ( Fig 2A and 2B ) . No pulmonary pathology was observed in these mice ( S6 Fig ) . Even when TrxB2 depletion was initiated during the chronic phase of infection on day 35 , colony forming units ( CFU ) declined rapidly and no bacteria could be isolated from both lungs and spleens on day 160 ( Fig 2A and 2B ) . The decline of CFU was accompanied by progressive healing of lesions in the lungs ( Fig 2C and 2D ) . These results establish that TrxB2 is required for growth and persistence of Mtb in mice and point to the value of targeting TrxB2 to treat TB . Although purified TrxB2 has been shown to reduce H2O2 and other peroxides , little is known about the detoxification function of TrxB2 in a physiological setting [12 , 19] . Therefore , we sought to evaluate the impact of partial TrxB2 depletion on the susceptibility of Mtb to oxidative stress . Achieving partial TrxB2 depletion to an extent that does not affect viability but significantly reduces the intracellular TrxB2 protein amount is technically challenging with a DUC strain because of the steep atc dose response curve of this regulatory switch [22] . To circumvent this problem , we generated a panel of TrxB2-TetON mutants that contain point mutations in the operator of the tet promoter resulting in different degrees of constitutive , leaky transcription upon atc removal . Transcription from the mutated tet promoters is similar without TetR , however leaky repression results in a range of promoter activities without atc ( Fig 3A ) . Two of the leaky TrxB2-TetON mutants , TrxB2-tetON-WT and TrxB2-tetON-1C , showed growth defects in the absence of atc ( Fig 3B ) . Their growth defects correlated well with the protein depletion kinetics of TrxB2 ( Fig 3C ) . These mutants thus achieved a phenotypically significant level of TrxB2 depletion yet retained enough TrxB2 to support growth . The moderate impact on growth of TrxB2-tetON-1C permitted the use of standard minimal inhibitory concentration ( MIC ) assays to measure how inhibition of TrxB2 affects susceptibility of Mtb to different chemical stresses . Surprisingly , partial inhibition of TrxB2 did not affect Mtb’s susceptibility to growth inhibition or killing by plumbagin , a superoxide generator ( Fig 4A and S7 Fig ) . TrxB2 silencing only caused a 2-fold shift of the MIC of H2O2 , and we did not detect significant survival differences between wild type H37Rv and the TrxB2-tetON mutant following H2O2 exposure ( Fig 4B and S7 Fig ) . Additionally , we measured Mtb’s susceptibility to reactive nitrogen species and found that TrxB2-silenced Mtb was only slightly less resistant to acidified nitrite at a high concentration ( Fig 4C ) . In contrast , TrxB2-silenced Mtb was 8–16 fold more susceptible to growth inhibition by diamide , a thiol-specific oxidant ( Fig 4D ) . This hypersusceptibility suggested a specific role for TrxB2 in detoxifying thiol-oxidizing stress . To determine if thiol-specific oxidizing stress was responsible for the lethality caused by TrxB2 depletion , we tested if supplementation with the strong thiol-reducing agent dithiothreitol ( DTT ) could prevent death of TrxB2-depleted Mtb . Indeed , DTT rescued viability of TrxB2-DUC in a dose-dependent manner ( Fig 4E ) . In contrast , neither glutathione nor catalase provided any survival benefit ( Fig 4F ) . These results indicate that the primary function of TrxB2 in Mtb is to detoxify thiol-specific oxidative stress and that TrxB2 is the dominant thiol-reducing enzyme in Mtb . We sought to investigate the pathways affected in TrxB2-depleted Mtb and analyzed the transcriptome changes associated with TrxB2 depletion . We found an early induction of 61 genes after 6 hours of atc treatment ( fold change >2 , p<0 . 02 ) , 12 of which belong to sulfur metabolism pathways ( Fig 5A ) . Mtb converts imported inorganic sulfate into adenosine 5’-phosposulfate ( APS ) , which can be used for metabolite sulfation [25 , 26] . Alternatively , APS can be sequentially reduced for the biosynthesis of essential sulfur-containing metabolites , including cysteine , methionine and mycothiol . The first committed step in this reductive branch , the conversion of sulfate to sulfide by APS reductase ( cysH ) , requires reducing potential supplied by the thioredoxin system [25 , 26] . We observed extensive up-regulation of sulfate importer genes ( cysT , cysW , cysA1 and subI ) and genes in the reductive branch , including cysH and the O-acetylserine sulfhydrylase encoding cysK1 and cysK2 , which indicates a response to compensate for a defect in sulfur assimilation . Consistent with that , TrxB2 depletion also resulted in increased expression of cysE , encoding a serine acetyl transferase , which is required for de novo cysteine biosynthesis ( Fig 5A ) . The expression of sulfur metabolism genes remained induced at 24 hrs post atc treatment ( Fig 5B ) and we asked whether death could be prevented or delayed by addition of reduced sulfur metabolites . A cysH deletion mutant was viable and had no growth defect , as long as it was supplemented with either 2 mM cysteine or methionine [27] . However , neither cysteine nor methionine protected TrxB2-depleted Mtb from death , indicating that TrxB2 is required for other essential pathways besides sulfur metabolism ( Fig 5C ) . Indeed , among the most highly up-regulated genes after 24 h of TrxB2 depletion were those involved in DNA metabolism ( Fig 5B ) . We observed extensive up-regulation of genes involved in three DNA repair pathways , including base excision repair ( nei , alkA , ung , ogt and xthA , ) , nucleotide excision repair ( ercc3 , uvrA and uvrD2 ) , and homologous recombination ( recA , ruvA and ruvC ) , suggesting that inhibition of TrxB2 was associated with DNA damaging stress . In support of this , we found that partial TrxB2 depletion decreased Mtb’s tolerance to genotoxic stress caused by mitomycin C , a potent DNA crosslinker ( Fig 5D ) . Of note , several genes involved in cell division were significantly down regulated in TrxB2-depleted Mtb ( Fig 5B ) consistent with the observed cell elongation ( Fig 1I ) . The induction of antioxidant genes ( trxB1 , trxC , thiX , ahpC , ahpD and mshA ) and whiB3 encoding an intracellular redox sensor and regulator [28] further supports that TrxB2 depletion induces thiol-oxidizing stress . Because DTT rescued survival of TrxB2-depleted Mtb ( Fig 4E and 4F ) we investigated its impact on the transcriptional changes caused by TrxB2 depletion . DTT treatment alleviated most of the mRNA changes associated with TrxB2 depletion without affecting atc-mediated transcriptional silencing and proteolytic degradation of TrxB2 ( S8 and S9 Figs ) . It reduced the expression of most antioxidant genes to basal levels , suppressed the induction of sulfur metabolism genes , reduced suppression of cell division genes and decreased the activation of genes involved in DNA repair ( S9 Fig ) . Together , these data demonstrate that death following TrxB2 depletion was caused by pleiotropic effects on a number of growth-essential pathways , including sulfur and DNA metabolism , and was mediated primarily through exhaustion of thiol-reducing power ( S10 Fig ) . We utilized the leaky TrxB2 knockdown mutants to evaluate the specificity of two thioredoxin reductase inhibitors , ebselen and auranofin . Ebselen is a substrate of mammalian thioredoxin reductase , a competitive inhibitor of thioredoxin reductase from E . coli , and inhibits growth of Mtb [21] . Mtb’s susceptibility to ebselen was , however , not altered by partial TrxB2 depletion suggesting that ebselen inhibits Mtb growth by affecting other targets ( Fig 6A ) . Auranofin , a gold-containing compound , was recently found to inhibit the enzymatic activity of Mtb’s TrxB2 in vitro and to kill Mtb [18] . Partial depletion of TrxB2 caused a 3 . 6-fold shift of the MIC of auranofin and sensitized Mtb to killing by 0 . 65 μg/ml auranofin , a concentration that did not affect viability of wt Mtb ( Fig 6B and 6C ) . However , wt and mutant were killed similarly in the presence of a higher concentration of auranofin ( Fig 6C ) . Our data suggest that TrxB2 is one of the major targets of auranofin , although auranofin likely inhibits multiple enzymes with reactive cysteine residues in Mtb , such as mycothione reductase [18] . To determine whether targeting TrxB2 sensitizes Mtb to other antimicrobial compounds , we screened the leaky TrxB2-TetON-1C mutant against a panel of antibiotics , including most of the first and second line anti-TB drugs . We found TrxB2-depleted Mtb highly susceptible to the cell wall biosynthesis inhibitors vancomycin and moenomycin ( Fig 6D and 6F ) . Moenomycin directly inhibits bacterial peptidoglycan glycosyltransferases , while vancomycin can block both transglycosylation and transpeptidation by binding to the terminal D-Ala-D-Ala residues of the peptide stem [29] . Other inhibitors of peptidoglycan transpeptidation such as ampicillin , did not affect TrxB2-depleted Mtb more than wt Mtb ( Fig 6F ) . Thus inhibiting TrxB2 may impair transglycosylation , which could contribute to the lysis phenotype we observed . Unexpectedly , depleting TrxB2 decreased the MIC of rifampicin by 5 . 6 fold , suggesting that a compound that inhibits TrxB2 may synergize with this important first line anti-TB drug ( Fig 6E ) .
The paucity of targets that are both biologically validated and susceptible to inhibition by drug-like small molecules , i . e . “druggable” , is a major bottleneck in antimycobacterial drug development . Mtb’s thioredoxin reductase TrxB2 has recently been shown to be druggable , yet its biological evaluation has not advanced beyond the prediction of its essentiality for growth of Mtb on standard agar plates [18] . Auranofin inactivates thioredoxin reductase in vitro but has multiple targets in bacteria , including in Mtb [18 , 30] . It was thus unknown how the specific inhibition of TrxB2 would affect Mtb in different environments including those encountered during acute and chronic infections . We addressed these questions using genetic strategies and found that inactivating TrxB2 quickly eradicated Mtb during the acute and , importantly , the chronic phase of mouse infection , validating TrxB2 as a valuable target for therapeutic intervention . Deliberately leaky TrxB2 knockdown mutants revealed that a TrxB2 inhibitor may synergize with rifampicin . Treatment combinations of rifampicin and a TrxB2 inhibitor could thus reduce the required drug dosage and limit the frequency of resistant mutants as shown for the synergistic action of carbapenems and rifampicin [31] . We used a leaky TrxB2 mutant to determine the specificity of two TrxB2 inhibitors . The MIC of ebselen was not affected by partial TrxB2 depletion , suggesting that ebselen inhibits Mtb growth primarily through targets other than TrxB2 . Ebselen has been shown to bind covalently to a cysteine residue located near the antigen 85 complex ( Ag85C ) active site and may thereby disrupt the biosynthesis of the mycobacterial cell envelope [32 , 33] . Auranofin was significantly more active against TrxB2-depleted Mtb than wild type indicating that it exerts its antimycobacterial activity at least partially through inhibiting TrxB2 . However , auranofin exhibits a higher affinity for human thioredoxin reductase than for bacterial enzymes [34] . Furthermore , auranofin , an FDA-approved anti-rheumatic drug , has immunosuppressive activities by inhibiting NF-κB signaling and decreasing the production of nitric oxide and pro-inflammatory cytokines , which are critical for anti-TB immune responses [35 , 36] . It also has anti-tumor activity through inhibition of proteasome-associated deubiquitinases [37–39] . The catalytic mechanisms of mammalian and bacterial thioredoxin reductases are significantly different and the crystal structure of TrxB2 has been solved [6 , 20] . It should thus be possible to identify inhibitors that are more specific for TrxB2 than auranofin . We expect the leaky TetON mutants we constructed for this study to facilitate the identification of such inhibitors . In addition to determining TrxB2’s value as a potential target for drug development we wanted to gain insights into the physiological functions of TrxB2 , especially its role in detoxifying oxidative stress . TrxB2 expression is induced upon oxidative and nitrosative stress and purified TrxB2 can mediate the reduction of H2O2 , peroxide , and dinitrobenzene [12 , 19] . However , TrxB2-depleted Mtb was hypersensitive specifically to thiol-oxidizing stress , but not to other types of oxidants , and the thiol reductant DTT prevented death caused by TrxB2 depletion . DTT did not promote growth of TrxB2-depleted Mtb , likely because DTT is very labile in neural aqueous solution and it is therefore difficult to maintain a constant concentration over time . Alternatively , TrxB2 has a function beyond its enzymatic activity , which is required for optimal growth and cannot be replaced by DTT . Notwithstanding , these results indicate that the primary function of TrxB2 in Mtb is to detoxify thiol-specific oxidative stress . Its potential role in defending against H2O2 , superoxide and nitrosative stress is likely redundant with other antioxidant systems . Mycothiol , a low-molecular-weight thiol present in millimolar quantities in mycobacterial cells , is thought to function as the mycobacterial substitute for glutathione and serve as the major redox buffer system in Mtb [10] . Mtb mycothiol-deficient mutants have a dramatically reduced intracellular thiol concentration , require catalase for optimal growth in vitro and exhibit increased sensitivity to oxidants . However , they are viable in vitro and only slightly attenuated in immunecompetent mice [15 , 40] . In contrast , TrxB2 depletion caused rapid lytic death even in the absence of exogenous oxidative stress and death was only prevented by DTT , but not catalase , cysteine and glutathione . Furthermore , TrxB2-depleted Mtb was unable to establish and maintain infection in mice . These phenotypic differences between mutants of the two major mycobacterial thiol-reducing systems emphasize that the TrxB2-dependent system provides the dominant thiol-reducing source to maintain thiol redox homeostasis . Recently , upregulation of thioredoxin genes in mycothiol deficient Mtb has been observed supporting that the thioredoxin system can restore mycothiol [11 , 41] . Some genes involved in DNA and sulfur metabolism were also differentially expressed in both mycothiol and ergothioneine deficient Mtb [11] , however , the majority of these was down regulated , while we found them induced in response to TrxB2 depletion . Thus , while some relationships exist between ergothionine , mycothiol and the thioredoxin system , they represent to a large degree systems with distinct activities in maintaining redox balance . Depriving thiol-reducing power via TrxB2 depletion affected numerous essential processes , including sulfur and DNA metabolism pathways . The conversion of sulfate to sulfide by APS reductase ( CysH ) requires reducing potential from the thioredoxin system , which may explain why TrxB2 depletion induced extensive up-regulation of the genes involved in cysteine biosynthesis [25 , 42] . TrxB2 depletion also strongly induced three different mycobacterial DNA repair pathways and consistent with this caused hypersusceptibility to the genotoxic drug mitomycin C . Ribonucleotide reductase ( RNR ) requires reducing power from the thioredoxin system to catalyze the reduction of NTP to dNTP [43] , but TrxB2 depletion did not lead to increased sensitivity to the RNR inhibitor hydroxyurea . This is possibly due to the presence of both class I and class II RNRs in Mtb while hydroxyurea only inhibits class I RNR [44] . It is also possible that other DNA biosynthesis and repair enzymes rely on the thioredoxin system , a hypothesis we are currently investigating . Surprisingly , we found that TrxB2 depletion lysed replicating Mtb . We observed significant cell elongation preceding lytic death consistent with the observed down-regulation of cell division genes . TrxB2-depleted Mtb was also highly susceptible to the peptidoglycan glycosyltransferases inhibitors moenomycin and vancomycin , but not to inhibitors of peptidoglycan transpeptidation , mycolic acid synthesis and arabinogalactan synthesis . We speculate that some enzymes or regulatory proteins involved in transglycosylation may depend on the thioredoxin system to maintain their intracellular redox states and function . Inactivation of TrxB2 may impair transglycosylation and thereby contribute to bacterial lysis . This observation also suggests a connection between redox-homeostasis and cell-envelope integrity in Mtb . We can therefore not exclude that TrxB2 depletion caused increased permeability to the sensitized compounds , although TrxB2-depletion did not cause susceptibility to all high molecular weight antibiotics . In summary , this work identified TrxB2 as the dominant thiol-reducing enzyme in Mtb and refined understanding of its physiological roles in defending against thiol-oxidative stress and maintaining growth-essential pathways . Our results establish the importance of TrxB2 in Mtb pathogenesis and validate the enzyme as a drug target . The leaky TetON mutants we developed will facilitate target-based whole cell screens for the identification of TrxB2 inhibitors and can help maintaining on-target activity during drug development . We expect this strategy of partial transcriptional silencing to be widely applicable and to facilitate chemical-genetic interaction studies for other growth-essential proteins in Mtb and other pathogens .
All animal experiments were performed following National Institutes of Health guidelines for housing and care of laboratory animals and performed in accordance with institutional regulations after protocol review and approval by the Institutional Animal Care and Use Committee of Weill Cornell Medical College ( Protocol Number 0601-441A ) . Wild type Mtb ( H37Rv ) and its derivative strains were grown in Middlebrook 7H9 medium supplemented with 0 . 2% glycerol , 0 . 05% Tween-80 , 0 . 5% BSA , 0 . 2% dextrose and 0 . 085% NaCl or on Middlebrook 7H10 agar containing OADC ( Becton Dickinson and Company ) and 0 . 5% glycerol . For growth of the TrxB2 leaky mutants , the above media were supplemented with 400 ng/ml anhydrotetracycline . To generate Mtb trxB2-DUC , we first transformed wild type Mtb H37Rv with an attL5-site integration plasmid expressing trxB2 and trxC under the control of P750 promoter to obtain a merodiploid strain; trxB2 and the first 4 bps of trxC ( the OFR of trxB2 overlaps with the first 4 bps of trxC ORF ) were then deleted from the merodiploid strain by allelic exchange as previously described [45 , 46] . After confirming deletion of the native copy of trxB2 by Southern blot , we performed replacement transformations of attL5 inserts to generate TrxB2-DUC [22] . In the TrxB2-DUC mutant , TrxB2 was expressed under the control of a TetOFF promoter and with a C-terminal DAS+4 tag . We also introduced a copy of trxC under the control of its native promoter to the attL5 site of TrxB2-DUC . The leaky TrxB2-TetON mutants were generated by replacement transformation of Mtb ΔtrxB2::P750-trxB2-trxC with plasmids containing trxB2 under the control of leaky tet promoters . A copy of trxC under the control of its native promoter was also introduced to the attL5 site of leaky TrxB2-TetON mutants . We transformed Mtb ΔtrxB2::P750-trxB2-trxC mutant with zeocin resistant plasmids expressing trxB2 and trxC , trxB2 , trxC or vector control . The transformants were selected on zeocin containing 7H10 agar . ΔtrxC was isolated from Mtb ΔtrxB2::P750-trxB2-trxC transformed with the plasmid expressing only trxB2 . The PBS starvation assay was set up as previously described [22] . Bacteria were grown in 7H9 medium to mid-log phase , washed three times with PBST , and suspended in PBST . After incubation for 10 d , atc was added to the cultures of TrxB2-DUC , and CFU were determined by plating on 7H10 plates . We prepared cell lysates from mid-log phase culture by bead-beating cell pellets in lysis buffer ( 50 mM Tris HCl pH 7 . 4 , 150mM NaCl and 2mM EDTA ) containing protease inhibitor cocktail ( Roche ) . We then centrifuged the lysates at 13 , 000 rpm for 20 min and sterilized the supernatant by passing through 0 . 22 μm Spin-X filters ( Costar ) . 30–60 μg total protein were separated by SDS–PAGE and transferred to nitrocellulose membranes for probing with rabbit antisera against Mtb TrxB2 , enolase ( Eno ) , proteasome beta subunit ( PrcB ) and dihydrolipoamide acyltransferase ( DlaT ) . Recombinant full-length Eno and TrxB2 were expressed with a C-terminal His tag , purified and used as antigen for immunization of rabbits . Culture filtrates were prepared as follows . Mtb strains were grown in 7H9 medium with 0 . 2% glycerol , 0 . 05% Tween-80 , 0 . 5% BSA , 0 . 2% dextrose and 0 . 085% NaCl until the culture reached an OD of 0 . 6 ~ 0 . 8 . Cultures were then washed three times with PBS to remove BSA and Tween-80 . We next suspended the pellet in 7H9 medium supplemented with 0 . 2% glycerol , 0 . 2% dextrose and 0 . 085% NaCl . After incubation , culture supernatant was harvested by centrifugation and filtration through 0 . 22 μm filters . Filtrates were concentrated 100-fold by using 3K centrifugal filter units ( Millipore ) and analyzed by immunoblotting with antisera against DlaT , Eno , PcrB and Ag85B ( Abcam , ab43019 ) . We infected female C57BL/6 mice ( Jackson Laboratory ) using an inhalation exposure system ( Glas-Col ) with mid-log phase Mtb to deliver approximately 200 bacilli per mouse . Mice received doxycycline containing mouse chow ( 2 , 000 ppm; Research Diets ) starting at the indicated time-points . Lungs and spleens were homogenized in PBS , serially diluted and plated on 7H10 agar to quantify CFU . Upper left lung lobes were fixed in 10% buffered formalin , embedded in paraffin and stained with hematoxylin and eosin . For transcriptome analysis of TrxB2-depleted Mtb , we grew TrxB2-DUC in 7H9 medium to an OD of 0 . 5~0 . 6 and then added 400 ng/ml atc . Samples were collected at 6 hr and 24 hr later . Each experiment was performed with at least three independent cultures . To determine the impact of DTT , TrxB2-DUC was treated with atc , DTT ( 2 mM ) or both for 24 hrs . Microarray analysis was performed as previously described [47] . Cultures were mixed at a 1:1 ratio with GTC buffer containing guanidinium thiocyanate ( 4 M ) , sodium lauryl sulfate ( 0 . 5% ) , trisodium citrate ( 25 mM ) , and 2-mercaptoethanol ( 0 . 1 M ) and pelleted by centrifugation . Bacterial RNA was isolated and labeled using a Low Input Quick Amp Labeling Kit ( Agilent ) according to the manufacturer’s instruction . Custom-designed Mtb H37Rv whole genome microarray ( GEO platform GPL16177 ) were used . Analysis and clustering were performed with Agilent GeneSpring software . One-way ANOVA was used to compare microarray data , with Benjamini–Hochberg correction for multiple hypothesis testing . All the data have been deposited in the GEO database with the accession numbers GSE72328 , GSE72329 , GSE72330 and GSE78894 . For oxidative stress , Mtb strains were grown to mid-log phase and washed twice in 7H9 medium . Bacterial single cell suspension was then prepared by centrifuging the cultures at 800 g for 10 min to remove clumps . We then adjusted the OD to 0 . 03 , treated Mtb strains with H2O2 , plumbagin , diamide or acidified nitrite and determined CFU by plating . Mtb was grown to mid-log phase and diluted to an OD of 0 . 03 in 7H9 medium . Bacteria were then exposed to 1 . 5-fold serial dilution of antimicrobial compounds . Optical density was recorded after 14 days and normalized to the corresponding strains without drug treatment . Minimum inhibitory concentration is defined as the lowest concentration of a drug at which bacterial growth was inhibited at least 90% , as compared to the control containing no antimicrobial compounds . Ampicillin , auranofin , D-cycloserine , ebselen , ethambutol , faropenem , hydroxyurea , isoniazid , kanamycin , levofloxacin , meropenem , mitomycin C , moxifloxacin , piperacillin , rifampicin , streptomycin and vancomycin were purchased from Sigma Aldrich , St . Louis , MO . Moenomycin was from Santa Cruz Biotechnology . Bedaquilline was received as a gift from C . Barry . One-way ANOVA was used for multiple group comparisons . Two-tailed unpaired Student’s t test was used for the analysis of differences between two groups . Statistical significance was defined as P < 0 . 05 unless otherwise stated . No statistical methods were used to predetermine sample size .
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Mycobacterium tuberculosis ( Mtb ) antioxidant systems represent attractive targets for developing novel tuberculosis therapies . We demonstrate that targeting thioredoxin reductase TrxB2 eradicates Mtb during acute and chronic mouse infections . TrxB2 inactivation caused thiol-specific oxidizing stress , perturbed growth-essential processes and resulted in lytic death . Unexpectedly , TrxB2 deficiency did not cause increased susceptibility to oxidative and nitrosative stress . To uncover the mechanistic consequences of depleting TrxB2 , or other growth essential proteins , in viable and growing bacteria , we developed a “leaky” knockdown system , with which partial TrxB2 depletion was achieved . Importantly , these leaky mutants revealed that one of two TrxB2 inhibitors kills Mtb via TrxB2 inactivation . They also demonstrated that TrxB2 depletion results in hypersusceptibility to rifampicin suggesting that a TrxB2 inhibitor will synergize with this frontline anti tuberculosis drug .
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2016
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Mycobacterium tuberculosis Thioredoxin Reductase Is Essential for Thiol Redox Homeostasis but Plays a Minor Role in Antioxidant Defense
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LANA is the KSHV-encoded terminal repeat binding protein essential for viral replication and episome maintenance during latency . We have determined the X-ray crystal structure of LANA C-terminal DNA binding domain ( LANADBD ) to reveal its capacity to form a decameric ring with an exterior DNA binding surface . The dimeric core is structurally similar to EBV EBNA1 with an N-terminal arm that regulates DNA binding and is required for replication function . The oligomeric interface between LANA dimers is dispensable for single site DNA binding , but is required for cooperative DNA binding , replication function , and episome maintenance . We also identify a basic patch opposite of the DNA binding surface that is responsible for the interaction with BRD proteins and contributes to episome maintenance function . The structural features of LANADBD suggest a novel mechanism of episome maintenance through DNA-binding induced oligomeric assembly .
Kaposi's sarcoma-associated herpesvirus ( KSHV ) is a human gammaherpesvirus that was first identified as the etiological agent of Kaposi's sarcoma and is also associated with pleural effusion lymphomas and multicentric Castleman's disease [1]–[3] . KSHV-associated tumors harbor latent viral genomes that persist as multicopy episomes [4] , [5] ( reviewed in [6] , [7] ) . During latency the genome is circularized at the terminal repeats ( TR ) , which function as an origin of DNA replication and as sites for tethering the episome to the host cell's metaphase chromosomes [8]–[11] . During latency , the viral episome replicates during S phase using host-cell replication machinery and expresses a limited set of viral proteins and non-coding RNAs responsible for viral genome maintenance and host cell survival [12]–[15] . Latency associated nuclear antigen ( LANA ) is a 130 kDa multifunctional protein required for TR-dependent DNA replication and episome maintenance during latency [5] , [7] , [16]–[20] . LANA also maintains latency by suppressing transcription and activity of the lytic trigger Rta [21]–[23] . Additionally , LANA interacts with numerous host cell proteins that mediate viral replication , episome maintenance , transcription regulation , and host-cell survival [15] , [18] , [24]–[33] . LANA binds to TR DNA through a conserved carboxy-terminal DNA binding domain ( DBD ) [15] , [34]–[38] . LANADBD shares some common features with the functional orthologs Epstein-Barr virus nuclear antigen 1 ( EBNA1 ) and human papillomavirus E2 [39] , [40] . Each of these proteins binds to specific semi-palindromic 16–18 bp viral DNA sequences as an obligate dimer [41]–[44] . LANA binds to two adjacent sites in the 800 bp GC-rich terminal repeats , referred to as LANA binding site 1 ( LBS1 ) and LBS2 [42] . Binding to LBS2 is highly cooperative with binding to LBS1 and precisely phased binding to both LBS1/LBS2 is essential for DNA replication function . Episome maintenance requires at least two LBS1/2 binding sites and the viral genome consists of 30–40 terminal repeats [4] , [45] . The precise mechanism of DNA binding and how DNA binding and spacing confers replication and episome maintenance remains poorly understood . There are at least two distinct mechanisms by which LANA can tether viral episomes to metaphase chromosomes . The extreme N-terminus of LANA can interact with host chromosomes through a direct interaction with histones H2A and H2B [46] , [47] . A second independent mechanism involves the interaction of the LANADBD with host chromatin-associated protein [30] , [48]–[50] . Prominent among these are the BET proteins BRD2 and BRD4 , which contain two bromodomains that bind to the acetylated tails of histones H3 and H4 , and a conserved C-terminal extraterminal ( ET ) domain [51] , [52] . The BRD ET domains interact directly with the DBD of LANA , providing a linkage between LANA and acetylated histone tails [53] , [54] . In both mechanisms , tethering requires a sequence-specific interaction between the LANADBD and the viral episome at LBS1/2 . To better understand the mechanism of LANA function through DNA binding we determined the crystal structure of LANADBD . This structure reveals several remarkable features that provide new insight into the regulation and function of LANA . We found that LANA can form a higher-order decameric ring structure . Mutagenesis studies demonstrate that the hydrophobic interface between LANA dimers is important for cooperative DNA binding , DNA replication , and episome maintenance . We also show that an amino terminal arm is important for DNA binding and replication function . Finally , we demonstrate that the BRD ET domain interacts with a basic patch located opposite to the DNA binding face that is crucial for episome maintenance .
To characterize the structural properties of LANADBD , we crystallized a construct encompassing residues 1011–1153 and determined its X-ray crystal structure at 2 . 05 Å resolution . A comparison with the DBD of EBNA1 shows that the two proteins share the same general fold ( RMSD of Cα atoms = 5 . 1 Å ) despite sharing very little amino acid sequence homology [55] ( Figures 1A , 1B , and S1 ) . A central , anti-parallel beta-sheet forms a hydrophobic core through which two proteins subunits form a dimer . Three helices , which contain the key residues involved in DNA binding , flank this core region [56] . The most notable differences between the homologs are in the intervening loop regions , with LANADBD exhibiting a more compact structure . Remarkably , the crystal structure of LANADBD reveals a higher-ordered assembly comprised of five dimers interacting end-to-end , forming a decameric ring with an exterior diameter of 110 Å and an interior diameter of 50 Å ( Figure 1C ) . The dimers are arranged such that the DNA binding surface faces the exterior of the decameric ring . The interface between the dimers is small and hydrophobic , consisting of residues Phe1037 , Phe1041 , Met1117 , Ala1121 , and Ala1124 , and buries a total solvent excluded surface area of 963 Å2 ( Figure 1D ) . Cooperative DNA binding has been described for LANA at the KSHV TR LBS1/2 binding sites [42] . To assess the role of the tetramer interface of LANADBD in cooperative DNA binding , we used a fluorescence polarization ( FP ) assay with LBS1 and fit the data using a single binding site model with a Hill coefficient ( h ) . We then created LANA mutants F1037A/F1041A and M1117A that lie within the tetramer interface . We found that wild-type LANADBD exhibits cooperativity in DNA binding ( h >1 ) . However , F1037A/F1041A and M1117A had reduced DNA binding affinity , with 38% and 12% of wild-type affinity , respectively ( Figure 2 ) . Moreover , the F1037A/F1041A mutation resulted in a complete loss of cooperativity ( h = 1 ) . As these residues are not positioned near the DNA binding face , it is likely that the reduced binding affinity observed is due to reduced cooperativity . Since only a single site was used in this assay , there is most likely a structural change that occurs upon DNA binding that primes a second LANADBD molecule for DNA interaction . To determine if the oligomeric states of LANA that are observed in the crystals exist in solution , we performed chemical crosslinking experiments both with and without DNA . In the absence of DNA , we observed the formation of two crosslinked complexes with a molecular weight corresponding to a dimer and a tetramer ( Figure 3A ) . However , when LBS1 DNA was added , a laddering of crosslinked complexes occurred with a maximum molecular weight of ∼150 kDa , corresponding to approximately a decamer . This suggests that DNA binding induces oligomerization of LANADBD . To further investigate the role of the oligomeric interface in DNA binding-induced oligomerization of LANADBD , we assayed LANA mutants by electrophoretic mobility shift assay ( EMSA ) using full length LANA derived from human cells ( Fig . 3B ) . In the presence of LBS1 DNA , we found that full-length LANA formed multiple oligomeric nucleoprotein complexes , indicative of DNA binding-induced oligomerization ( Figure 3B lanes 1–9 ) . When LBS1/2 DNA was used , we observed the formation of tetramer/DNA complexes as well as additional higher-ordered oligomeric species ( Figure 3B lanes 10–18 ) . Mutations predicted to disrupt the tetramer interface , M1117A and F1037A/F1041A , were capable of binding LBS1 but showed greatly reduced capacity to form oligomeric species . Furthermore , M1117A exhibited reduced ability to form fully occupied LBS1/2 complexes and F1037A/F1041A was severely impaired in forming higher order LBS1/2 DNA complexes ( Figure 3B , lanes 12–13 ) . These results indicate that hydrophobic residues at the tetramer interface are essential for higher order protein-DNA complex formation and cooperative DNA binding . To determine if mutations that disrupt oligomerization and cooperative DNA binding are important for LANA function in vivo , we tested these and additional mutations in the context of full-length LANA using plasmid replication and plasmid maintenance assays ( Figures 4 and 5 ) . The plasmid contained eight consecutive terminal repeats ( p8xTR ) and replication was assessed 72 hours post-transfection by measuring the levels of DpnI resistant plasmid DNA ( Figure 4 ) . Mutations that disrupted the oligomerization interface ( F1037A/F1041A ) completely abrogated DNA replication function ( Figure 4 ) . All of these proteins were expressed at similar levels as measured by Western blot analysis ( Figure 4D and G ) . LANA mutations were also assessed for their ability to support long-term plasmid maintenance in B-lymphoid cell lines ( Figure 5 ) . Full length LANA and LANA mutant proteins were selected for stable expression in BJAB B cell-lymphoma cell lines , and then co-selected with plasmids containing p8xTR and a G418-resistance marker . While most LANA mutations were expressed at similar protein levels to wild-type LANA , their ability to support plasmid maintenance varied substantially . Single substitution mutations F1037A and M1117A supported nearly wild-type levels of plasmid replication . In contrast , the F1041A mutant showed impaired replication and maintenance activity and the double mutant F1037A/F1041A , which disrupts oligomeric interactions , was nearly completely void of episome maintenance function . In the crystal structure of EBNA1DBD bound to DNA , the N-terminus of the domain acts as an arm that wraps around the minor groove of the DNA . In our DNA-free structure of LANADBD the N-terminal portion of this construct lies across the DNA binding face , based on homology with the EBNA1 co-crystal structure ( Figure 6A and 6B ) . We anticipate that , when bound to DNA , this arm undergoes a conformational change to allow DNA recognition with the conserved DNA contact residues , and wrap around the DNA similarly to that observed in EBNA1DBD . To determine if this arm plays a role in DNA binding we prepared several mutants , R1013A , Y1014A , P1017G , P1018G , Y1021A , Y10141A/Y1021A , and Y1014F/Y1021F , and an N-terminal deletion construct 1021–1153 . As measured by FP and EMSA , mutation of P1017 or P1018 to glycine caused a slight decrease in affinity to about 75% compared to wild-type ( Figure 2 ) . In FP assays , Tyr1014 and Tyr1021 could be singly mutated to alanine with no detriment to DNA binding affinity . Furthermore , the Y1014F/Y1021F double mutant had nearly the same binding affinity as wild-type . However , the Y1014A/Y1021A double mutant had approximately 40% binding affinity compared to wild-type . This effect was comparable to the N-terminal deletion construct 1021–1153 . Interestingly , single alanine substitutions of Tyr1014 and Arg1013 completely disrupted DNA binding in EMSA ( Fig . 3 ) . Although the differences between FP and EMSA are several , including different protein sources and different biophysical parameters , the results substantiate the importance of the N-terminal arm in stabilizing LANA-DNA binding . Generally , the results of the in vivo assays agreed with the FP data . Mutants R1013A , Y1014A , and Y1014A/Y1021A showed greatly reduced levels of replicated plasmid with Y1021A also showing some reduction ( Figure 4 ) . These same mutants also had a reduced capacity to support the persistence of p8xTR in plasmid maintenance assays ( Figure 5 ) . Based on this data , we conclude that the NTA of LANADBD participates in DNA binding and is required for replication and episome maintenance function in vivo . The surface opposite to the DNA binding face of LANADBD is a dense basic patch composed mostly of lysine residues ( Figure 6C and 6D ) . This feature appears to be unique to LANA , as the analogous part of EBNA1 is composed mainly of acidic residues ( Figure 6D ) . Previous studies have shown that a region of LANADBD encompassing a portion of this patch is important for interacting with the ET domain of BRD2 and BRD4 [53] , [54] . To further identify which residues are important for this interaction we made mutations of these lysine residues by subdividing the basic patch into two regions , Lys1138/1140/1141 , near the dimer interface , and Lys1109/1113/1114 , near the tetramer interface . Mutation to alanines resulted in decreased plasmid replication and maintenance functions with K1109/1113/1114A being most impaired ( Figures 4 and 5 ) . Further reduction of these activities was achieved by charge reversal mutations to glutamate . To understand the role that these mutations play in interacting with the ET domain of BRD2 and BRD4 , we performed in vivo co-immunoprecipitation and in vitro pulldowns . Using a FLAG-tagged version of wild-type or mutant full-length LANA , we performed co-IPs looking for the presence of BRD4 ( Figure 7A ) . While wild-type LANADBD showed binding to BRD4 , we found that the K1138/1140/1141A and K1138/1140/1141E mutants exhibited decreased levels of BRD4 interaction . The effect of mutations at Lys1109/1113/1114 was minimal . Other mutations in both the N-terminal arm and the tetramer interface did not show any impairment in interactions with BRD4 . These findings indicate that amino acid residues within the basic patch near the dimer interface ( Lys1138/1140/1141 ) are principally responsible for BRD interaction . To confirm that the co-immunoprecipitation was due to a direct interaction between LANA and the ET domain of BRD we performed in vitro pulldowns . For these assays we prepared His-SMT3-tagged versions of BRD2 and BRD4 ET domains and untagged forms of LANADBD and EBNA1DBD ( as a negative control ) . The long constructs ( BRD2L and BRD4L ) comprised the ET domain with the serine-rich C-terminal tail ( Figure S2 ) . The short constructs ( BRD2S and BRD4S ) contained the portion of the ET domain exhibiting the highest sequence identity between the two proteins . The results show that LANADBD is capable of interacting directly with all four BRD constructs ( Figure 7B and 7C ) , whereas EBNA1DBD did not show any binding to these constructs ( Figure 7D and 7E ) . This data demonstrates that LANADBD directly binds to the BRD ET domain .
The cooperative nature of DNA binding suggests that LANA may form a tetramer prior to DNA binding or that DNA binding induces a conformational change in either the protein or the DNA , which facilitates binding to the lower affinity site , LBS2 . We have shown using chemical crosslinking that we can capture LANADBD tetramers in the absence of DNA and that DNA binding induces the formation of high molecular weight oligomers ( Figure 3A ) . This same behavior was observed using full-length LANA , indicating that oligomerization is not an artifact of the truncated protein ( Figure 3B ) [37] . Furthermore , cooperativity was observed in FP assays that utilized a single site LBS1 probe , and this cooperativity was reduced by mutations in the tetramer interface ( Figure 2 ) . This suggests that tetramerization enhances the interaction with lower affinity sites , such as LBS2 . Cooperative binding has also been observed for LANA's functional homolog in EBV , EBNA1 [57] . EBNA1 binding sites exist in two locations in the latent origin of replication ( oriP ) , the family of repeats ( FR ) and the dyad symmetry ( DS ) element . Similar to LBS1/2 , DS is composed of two sets of tandem EBNA1 binding sites , positioned 21 bp apart , center-to-center [58] . The available structures of EBNA1DBD present a dimeric structure either alone or in the presence of DNA [55] , [59] . The residues at the location of the anticipated tetramer interface of EBNA1DBD are generally acidic and , would be unable to participate in a homotypic interaction similar to that seen in LANA . Thus it is likely that a structural change , post-translational modification , or additional cellular protein would be required for the cooperative tetrameric DNA binding observed with EBNA1 . In the crystal structure presented here , LANADBD dimers interact to form a novel decameric ring structure ( Figure 1C ) . While we have not validated that a decamer forms in vivo , the molecular details of the interactions between dimers and the geometric organization of the dimers provide insight into the means by which cooperative binding occurs . The tetramer interface is relatively small and composed of hydrophobic residues Phe1037 , Phe1041 , Met1117 , Ala1021 , and Ala1024 . We and others have shown that mutation of these residues has adverse effects on DNA binding activity [56] . In particular , the F1037A/F1041A mutant fails to produce DNA binding induced oligomers in solution ( Figures 2 and 3 ) . This mutant also fails to form the higher molecular weight complexes with LBS1/2 that are characteristic of full-length wild-type LANA . Most notably , F1037A/F1041A is severely impaired for plasmid replication and maintenance activity ( Figures 4 and 5 ) , indicating the importance of the tetramer interface in cooperative DNA binding and LANA functionality in vivo . The geometric arrangement of the dimers in the crystal structure may indicate the manner in which two dimers interact when bound to LBS1/2 . Wong and Wilson demonstrated that the binding of LANA to LBS1 induces a 57° bend in the DNA and that binding to LBS1/2 additively increases the bend angle to 110° [60] . Consistent with this , the angle between dimers in the decameric ring is approximately 110° ( Figure 1C ) . Mutational analysis of residues near the tetramer interface located distal to the DNA binding surface further demonstrates that the angle between dimers is important in mediating cooperativity . Met1117 may act to maintain the angle between dimers at approximately 110° . We observed a decrease in the level of oligomeric species formation by the M1117A mutant in EMSA analysis using an LBS1/2 probe ( Fig . 3 ) . Additionally , in FP assays ( Fig . 2 ) the Kd of DNA binding is reduced to about 10% and the Hill coefficient is reduced to less than 1 for M1117A , indicating negative cooperativity . This implies that the angle between dimers in the preexisting tetramers of these mutants exceeds the optimal angle for cooperative binding and the formation of higher ordered complexes . In the context of full-length LANA this geometry may be maintained by additional regions not determined in our crystal structure , as the effects of M1117A are less pronounced in EMSA , replication , and maintenance assays ( Fig . 3B , 4 , and 5 ) . Taken together , our data supports the role of the tetramer interface as the basis for cooperative DNA binding to TR and functionality in KSHV DNA replication and episome maintenance . Several other studies support the role of LANA oligomerization in KSHV biology . LANA has been shown to bind TR DNA as an oligomer in EMSA and mutations that disrupt oligomerization were found to block DNA replication and episome maintenance [37] . The oligomerization domain was mapped to the N-terminal arm , which we have also found is required for cooperative DNA binding . Some evidence for oligomeric binding in vivo may be inferred from nucleosome mapping studies of the TR in latently infected BCBL1 cells [17] . This study revealed that four nucleosomes are positioned at regular intervals within the 809 bp TR , and a nucleosome-free region of ∼350 bp surrounds the LBS1/2 binding site [17] . This nucleosome-free region could accommodate two LANA dimers and replication factors , and this extended region is essential for efficient DNA replication [38] . It is also possible that one or two LANA decamers could occupy this nucleosome free region , assuming that the each decamer wrapped ∼120 bp of TR DNA . In this model , only one or two dimers of the decamer would have high-affinity interactions with LBS1 and LBS2 , and the remaining three dimers would interact non-specifically with adjacent TR DNA . Whether LANA oligomers mediate additional long-distance interactions between tandem TRs with ∼800 bp of intervening DNA is not yet known . However , long-distance interactions have been described for EBNA1 , which can form DNA loops between the family of repeats and the dyad symmetry elements of OriP [61] . Higher order oligomeric conformations have also been described for other viral origin binding proteins , including SV40 T antigen and papillomavirus E1 , which undergo conformational changes after DNA and ATP-binding [62] , [63] . More recently , a complex oligomeric structure has been described for the adenovirus-encoded E4-ORF3 , which forms an intracellular network that is important for compartmentalization during viral DNA replication [64] . Interestingly , the structure of E4-ORF3 was shown to share a similar fold to EBNA1 , suggesting that LANA , EBNA1 , E2 , and E4-ORF3 may share some similarities in the self-assembly of larger structures . Thus , it is possible that higher-order oligomerization of LANA plays an important functional role in KSHV biology . Another interesting feature of the crystal structure of LANADBD is the N-terminal arm ( NTA ) of this domain . Mutagenesis studies revealed that the LANADBD NTA is essential for stable DNA binding , replication , and episome maintenance function . In the LANADBD crystal structure , the NTA occupies the exact position where DNA would be located based on superposition with the EBNA1 co-crystal structure ( Figure 6B ) . In the EBNA1DBD-DNA co-crystal structure , the N-terminal arm of the domain wraps around the minor groove of the DNA ( Figure 6B ) [59] . We have shown that mutation of residues located within LANA NTA ( specifically Arg1013 , Tyr1014 , and Tyr1021 ) decrease DNA binding affinity and causes a loss of plasmid replication and maintenance . It is possible that the NTA occupies this position due to the effects of crystal packing or because the body of the protein presents an entropically favorable environment . However , in this position the arm would occlude DNA from interacting with LANA ( Figure 6B ) . Therefore , it is most likely that the NTA undergoes a conformational change prior to DNA binding , wrapping around the minor groove in a manner similar to that observed in EBNA1 . This would also help to explain some of the cooperative oligomerization induced by a single LBS1 DNA biding site . We suggest that DNA binding induces a change in the conformation of the NTA that facilitates higher order oligomerization and cooperative DNA binding by LANA . A novel feature of LANADBD observed in the crystal structure is the presence of a lysine-rich basic patch located opposite to the DNA binding . The only prior evidence for a function of the residues within this patch was the observation that deletion of the last 23 amino acids of LANA abrogates interaction with BRD2 and BRD4 . These same studies showed that the ET domain of the BRD proteins mediates the interaction with LANA . The sequence of the ET domains of BRD2 and BRD4 reveals two regions that may be important , the N-terminal portion contains a large number of glutamates and the C-terminal tail is serine-rich . By dividing the basic patch on LANADBD into two parts we were able to show that the internal portion of the patch ( Lys1138 , Lys1140 , and Lys1141 ) contains the key residues involved in BRD binding ( Figure 7A ) . These lysines appear to interact with the acidic residues in the N-terminal portion of the ET domains ( Figures 7B and 7C ) . We did not observe any interaction with either BRD ET domain with EBNA1DBD , as would be expected since the analogous surface of EBNA1 is mostly acidic ( Figure 6D ) . BRD interactions have been shown previously to mediate LANA function in chromosome binding [53] , [54] . Our data is consistent with a role of BRD binding in episome maintenance and DNA replication . However , BRD proteins have also been implicated in other LANA functions , including transcription regulation and cell cycle control . BRD proteins have been shown to mediate multiple functions of E2 family members , including metaphase chromosome tethering , transcriptional repression , and DNA replication . Thus , it is possible that BRDs contribute to multiple functions of LANA . The structure and associated biochemical and cell-based studies of LANADBD reported here reveal new insights into the higher-order oligomerization , cooperative DNA binding , DNA replication , episome maintenance , and BRD interaction interface of LANA . We identified the N-terminal arm , which is crucial for DNA interaction and , based on homology to EBNA1 , likely wraps around the minor groove of the cognate DNA , providing for high affinity binding . We have demonstrated that LANA has the capacity to oligomerize upon binding its cognate DNA . The decameric ring observed in the crystal structure provides one possibility for the arrangement of oligomeric LANA , however it is not known for certain if this is the state of oligomerization in vivo . We also determined that a basic patch located opposite to the DNA binding face of LANADBD serves as the interaction site with host cell BRD proteins . These activities are critical for LANA's function in viral DNA replication and tethering the KSHV episome to host cell chromosomes to allow for passage of the genome upon cell division during latent viral infection .
A construct comprising residues 1011–1153 of LANA ( LANADBD ) was expressed as a His-SMT3 tagged protein in E . coli . Cells were sonicated in 2 M NaCl , 25 mM HEPES , 25 mM imidazole , 5 mM beta-mercaptoethanol ( BME ) , pH 8 . 0 and purified over Ni-NTA resin . The His-SMT3 tag was removed by overnight cleavage with ULP1 protease and the cleaved product was further purified by a second run over Ni-NTA . The protein was then run on a Superdex 75 column equilibrated with 2 M NaCl , 20 mM HEPES , 0 . 5 mM TCEP , pH 7 . 4 . Mutants were prepared using site-directed mutagenesis and purified as described . The protein was concentrated to 30 mg/mL and crystallization screening was performed . Initial crystallization hits did not provide diffraction beyond 4 Å . To improve crystal quality , crystals grown in 20% polyethylene glycol monomethyl ether 2000 , 100 mM Tris pH 8 . 0 , 150 mM calcium acetate were crushed , diluted 1∶10000 in reservoir solution , and used to re-seed entire crystallization screens . This yielded crystals in 1 M ammonium formate and 100 mM HEPES pH 8 . 0 that diffracted to 2 Å and formed in the monoclinic space group P21 ( α = 51 . 4 Å , β = 175 . 2 Å , γ = 97 . 1 Å , b = 95 . 3° ) . Initial phasing was unsuccessful using molecular replacement with the crystal structure of EBNA1DBD ( PDB 1vhi ) . Crystals were then soaked with a variety of heavy atom salts and potassium osmate was identified as a successful derivatization . Diffraction data were collected to 3 Å at beamline X29a at the National Synchrotron Light Source using wavelengths at the peak ( 1 . 1401 Å ) and inflection ( 1 . 1404 Å ) . Ten initial osmium sites were located using AutoSol in Phenix . The phases obtained were of adequate quality to generate electron density maps in which the secondary structure elements could be modeled manually . Once the majority of the protein was built , molecular replacement was performed using a higher resolution native dataset . Model building and refinement were completed using Phenix . The model was refined to convergence with Rwork = 18 . 18% and Rfree = 22 . 62% . Complete data reduction and refinement statistics are given in Table 1 . The final structure has been submitted to the Protein Data Bank with accession code 4J2K . To determine dissociation constants for DNA binding , fluorescence polarization assays were performed . Protein was serially diluted 2-fold starting at 100 µM in 1 M NaCl , 20 mM HEPES , 1 mM DTT , pH 7 . 4 . These protein samples were then diluted 10-fold into reactions resulting in a final condition of 200 mM NaCl , 20 mM HEPES , 1 mM DTT , 1 µg/mL BSA , 0 . 1 µg/mL poly ( dA:dT ) ( Invivogen , San Diego , CA ) , 0 . 001% Tween 20 , and 1 nM LBS1 probe ( Integrated DNA Technologies , Cedar Rapids , IA ) . This probe was a 21-mer ( AGCGGCCCCATGCCCGGGCGG ) centered on the LBS1 site and was 5′ labeled with 6-carboxyfluorescein . The reactions were incubated at room temperature for 30 minutes and then read using a Beacon 2000 Fluorescence Polarization reader . Data were collected in triplicate and analyzed using a one-site specific binding with Hill slope model in GraphPad Prism ( version 5 . 0a; GraphPad Software , La Jolla , CA ) . Crosslinking reactions were performed using ethylene glycol bis[succinimidyl succinate] ( EGS; Thermo Scientific ) . The DNA was an unlabeled version of the probe used in the FP experiments . Protein was incubated with a 1 . 2 molar excess of DNA , where applicable , in a reaction buffer of 200 mM NaCl , 20 mM HEPES pH 7 . 4 , and 1 mM DTT for 30 minutes at room temperature . EGS was dissolved and diluted in DMSO and added to the reaction at 5% of the reaction volume . This was incubated at room temperature and then quenched with 5% volume of 1 M Tris , pH 7 . 5 for 15 minutes . Samples were boiled with loading dye before loading into gels . FLAG-LANA proteins were expressed by transient transfection in 293T cells , and isolated by FLAG-affinity purification after multiple wash steps with extraction buffer ( 20 mM Tris , 400 mM NaCl , 10% glycerol , 0 . 5 mM EDTA , 0 . 05% NP-40 , 0 . 5 mM DTT , and protease inhibitors ( Sigma ) ) to remove weakly associated cellular proteins and nucleic acids . Binding reactions and gel conditions were described previously [65] DNA binding reactions were resolved on a horizontal 1 . 5% agarose gel in 0 . 5× TBE ( 45 mM Tris-borate , 1 mM EDTA ) at 10 V/cm for 2 hours and then dried on DE80 paper prior to PhosphorImager exposure . Binding reactions were in a final of 10 µL containing 10 mM HEPES ( pH7 . 9 ) , 10% glycerol , 100 mM KCl , 5 mM MgCl2 , 0 . 1 U/µl poly-dIdC , 5 mM β-mercaptoethanol with LANA protein at ∼200 nM and radiolabelled LBS1 or LBS1/2 probe at 2 . 5 nM . BJAB ( uninfected B cell lymphoma ) cells or BJAB cells stably expressing triplicate FLAG-epitope-tagged LANA were grown in RPMI medium supplemented with 10% fetal bovine serum ( FBS ) and maintained at concentrations of 0 . 2 to 0 . 8×106/mL . Stable LANA expression was maintained with puromycin selection ( 2 µg/ml ) ( Sigma ) . After cotransfection of p8TR plasmids , LANA stable cells were maintained with both puromycin ( 2 µg/ml ) ( Sigma ) and G418 ( 600 µg/ml ) ( Mediatech ) selection . 293T cells were maintained in Dulbecco's modified Eagle's medium ( DMEM ) containing 10% v/v FBS . For transient transfection experiments , transfection of actively growing 293T cells was processed with Lipofectamine reagent ( Invitrogen ) , and cells were harvested 72 hours post transfection . For creating stable cells , 10×106 of actively growing BJAB cells were resuspended in 450 µl 10% FBS RPMI media without antibiotics . 30 µg DNA of interest were mixed together with cells in a microcentrifuge tube and incubated at RT for 10 min . All transfections were carried out with the Gene Pulser Xcell ( Bio-Rad ) setting at 0 . 22 kv and 960 µF as external capacitor . The transfected cells were incubated at RT for 10 min post electroporation , and then maintained as described above . Plasmid p8TR contains eight copies of the TR unit cloned into pREP9 ( Invitrogen ) ( gift of K . Kaye , Harvard Medical School ) . KSHV LANA was cloned into p3XFLAG-CMV-24 ( Sigma ) as described previously . All the LANA mutants created described in this study are based on this plasmid . Human BRD4 expression construct was a gift from Dr . Jianxin You ( University of Pennsylvania , School of Medicine ) . For immunoprecipitation ( IP ) , co-transfected 293T cells expressing FLAG-tagged LANA wild-type , mutant , or vector only control and human BRD4 expression vector were lysed in 300 µL of IP buffer ( 50 mM Tris pH 7 . 6 , 60 mM NaCl , 1% glycerol , 0 . 5 mM EDTA , 0 . 2% NP- 40 , and protease inhibitor ( Sigma ) ) at 4°C with sonication . FLAG-tagged proteins were precipitated with anti-FLAG rabbit serum ( Sigma ) followed by protein A/G bead ( Thermo Scientific ) capture . For immunoblot assays , proteins were resolved in 8–16% Novex Tris-Glycine gels , LANA was detected using an HRP conjugated anti-FLAG antibody ( Sigma ) , and BRD4 was detected using Anti-BRD4 antibody ( Bethyl Laboratories , Inc . ) at 1∶2000 dilution in conjunction with HRP-conjugated secondary antibodies ( GE Life Sciences ) and ECL reagents ( Invitrogen ) . At day 7 post-transfection and selection , BJAB cells were lysed in 1 mL lysis buffer ( 0 . 6% SDS , 10 mM EDTA , 10 mM , Tris-HCl pH 7 . 5 , 50 µg/ml RnaseA ) per 5×106 cells and incubated at 37°C for 2 hours . NaCl was then added to 1 M final concentration and incubated overnight at 4°C . After a 30 minute centrifugation at 8 , 500 rpm at 4°C , DNA was extracted once with phenol∶chloform ( 1∶1 ) , twice with chloroform∶isoamyl alcohol ( 24∶1 ) , ethanol precipitated , and the DNA pellet was washed with 70% ethanol , air-dried and resuspended in TE buffer . FLAG- or LANA-expressing BJAB cells were transfected with p8TR DNA . After 48 h , cells were maintained in medium containing G418 ( 600 µg/ml ) ( Mediatech ) . Hirt DNA extraction was performed about 40 days post transfection . 30 µg DNA was digested with BglII to linearize the p8TR DNA . Double digestion of 30 µg of Hirt DNA with BglII and DpnI was also performed and electrophoresis in a 0 . 8% agarose gel in Tris-borate-EDTA buffer . DNA was then transferred to a nylon membrane . KSHV DNA was detected with a 32P-labeled TR probes . Quantitation of the linearized BglII- or BglII/DpnI-digested p8TR was performed using a PhosphorImager SI ( Molecular Dynamics ) . BRD2 and BRD4 were expressed in two forms , both as His-SMT3 tagged proteins . The long form encompassed the extraterminal domain including the serine-rich C-terminus ( BRD2 633–801 , BRD4 601–722 ) . The short form included the portion of the ET domain that is most conserved between BRD2 and BRD4 ( BRD2 633–714 , BRD4 601–681 ) . The tagged BRD proteins were incubated with Ni-NTA resin ( Thermo Scientific ) equilibrated with 200 mM NaCl , 20 mM HEPES , 5 mM BME , at pH 8 . 0 and washed with 10 column volumes ( CV ) of this buffer . Untagged LANADBD ( 1011–1153 ) or EBNA1DBD ( 461–642 ) was then added and the resin was washed again with 10 CV of wash buffer . The protein was then eluted with wash buffer supplemented with 300 mM imidazole . As a negative control the untagged LANADBD or EBNA1DBD was incubated with the Ni-NTA resin and washed and eluted as described above .
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Kaposi's sarcoma-associated herpesvirus ( KSHV ) establishes latent infections that are associated with several cancers including Kaposi's sarcoma , pleural effusion lymphoma , and multicentric Caslteman's disease . One of the major viral proteins required for establishment and maintenance of the latent state is the latency-associated nuclear antigen ( LANA ) . LANA binds to DNA sequences within the terminal repeats ( TR ) of the viral genome and stimulates both DNA replication and episome maintenance during latency . Here we present the X-ray crystal structure of the DNA binding domain of LANA ( LANADBD ) and show that it has the capacity to form oligomeric complexes upon DNA binding . We characterize structural features of LANADBD that are required for oligomerization , DNA binding , and interaction with host cell BET proteins , BRD2 and BRD4 , which are important for mediating multiple functions of LANA , including episome maintenance .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[] |
2013
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Molecular Basis for Oligomeric-DNA Binding and Episome Maintenance by KSHV LANA
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The reovirus fusion-associated small transmembrane ( FAST ) proteins are virus-encoded membrane fusion proteins that function as dedicated cell–cell fusogens . The topology of these small , single-pass membrane proteins orients the majority of the protein on the distal side of the membrane ( i . e . , inside the cell ) . We now show that ectopic expression of the endodomains of the p10 , p14 , and p15 FAST proteins enhances syncytiogenesis induced by the full-length FAST proteins , both homotypically and heterotypically . Results further indicate that the 68-residue cytoplasmic endodomain of the p14 FAST protein ( 1 ) is endogenously generated from full-length p14 protein expressed in virus-infected or transfected cells; ( 2 ) enhances syncytiogenesis subsequent to stable pore formation; ( 3 ) increases the syncytiogenic activity of heterologous fusion proteins , including the differentiation-dependent fusion of murine myoblasts; ( 4 ) exerts its enhancing activity from the cytosol , independent of direct interactions with either the fusogen or the membranes being fused; and ( 5 ) contains several regions with protein–protein interaction motifs that influence enhancing activity . We propose that the unique evolution of the FAST proteins as virus-encoded cellular fusogens has allowed them to generate a trans-acting , soluble endodomain peptide to harness a cellular pathway or process involved in the poorly understood process that facilitates the transition from microfusion pores to macrofusion and syncytiogenesis .
The formation of multi-nucleated syncytia is an essential feature of a diverse range of biological processes [1] . Syncytiogenesis is contingent upon regulated cell–cell membrane fusion , which requires the involvement of protein catalysts to overcome the thermodynamic barriers that prevent spontaneous fusion of biological membranes [2] . The fusion proteins responsible for cell–cell fusion remain largely undiscovered and/or their mechanism of action poorly defined [1] , [3] . Our current understanding of protein-mediated membrane fusion derives largely from the study of enveloped virus proteins designed to promote virus–cell fusion [4] , [5] , and from the SNARE proteins involved in intracellular vesicle fusion [6] . These studies converge on what may be a unifying model of membrane fusion involving a multi-step fusion-through-hemifusion pathway mediated by dynamic remodelling of the fusion protein complex [7] , [8] . While mechanisms by which membrane fusion proteins promote membrane merger and the formation of focal fusion pores are beginning to emerge , relatively little is known about the processes that drive expansion of these fusion apertures , an essential step for those cell–cell fusion events that result in syncytium formation [9] , [10] . The fusogenic orthoreoviruses encode a unique family of membrane fusion proteins , termed the fusion-associated small transmembrane ( FAST ) proteins . There are currently three distinct members of the FAST protein family named according to their molecular masses; p10 , p14 and p15 [11]–[13] . Unlike enveloped virus fusion proteins , the FAST proteins are nonstructural proteins and are therefore not involved in promoting virus–cell fusion and virus entry [12] , [13] . Following their expression inside virus-infected or transfected cells , the FAST proteins traffic to the plasma membrane where they perform their sole defined function , to induce cell–cell fusion and polykaryon formation in a wide variety of cell types [14] . The FAST proteins therefore function as promiscuous , virus-encoded “cellular” fusogens . The FAST proteins are both necessary and sufficient to induce membrane fusion , they need only be present in one of the two membranes being fused , and at only 95–140 residues in size , are the smallest known autonomous fusogens [15] , [16] . All of the FAST proteins are single-pass membrane proteins that position very small N-terminal ectodomains ( ∼20–41 residues ) external to the membrane and relatively larger C-terminal endodomains of ∼36–97 residues in the cytosol [11] , [13] , [17] . In contrast , most enveloped virus fusion proteins and the SNARE proteins are oriented with the majority of their mass positioned to interact with the proximal leaflets of the membranes to be fused [4] , [6] , [18] . We have been interested in reconciling the unusual topologies of the FAST proteins with their role as dedicated cell–cell fusogens . Although enveloped virus fusion proteins can induce cell–cell membrane fusion , their primary function is to serve as virus–cell fusogens; their endodomains are therefore designed to function from the interior of the virion , not necessarily from the cytoplasm of the cell . This evolutionary imperative may explain why the endodomains of many enveloped virus fusion proteins either have no essential role in the membrane fusion reaction , or actually serve to inhibit cell–cell fusion activity , thereby coupling fusion competence to virion maturation [19]–[23] . In instances where the endodomain is required for membrane fusion , it is frequently involved in subcellular localization of the fusion protein , virus assembly and/or the formation of stable fusion pores [24]–[27] . As nonstructural viral proteins dedicated to executing cell–cell fusion , the endodomains of the FAST proteins do not need to inhibit fusion to promote virus assembly , and have specifically evolved to function during membrane fusion while in contact with the cytoplasm . These distinct evolutionary imperatives suggest the endodomains of the FAST proteins , and other yet to be identified cellular fusogens , might serve a different function during the fusion process than the endodomains of most enveloped virus fusion proteins . The homologous p10 FAST proteins of avian reovirus ( ARV ) and Nelson Bay reovirus ( NBV ) contain 95–98 residues , distributed approximately equally on either side of the transmembrane domain [13] . The p14 FAST protein of reptilian reovirus is a 125-residue integral membrane protein , with a single transmembrane domain that separates a small , 36-residue N-terminal ectodomain from a considerably larger 68-residue C-terminal endodomain [11] . The asymmetric membrane topology of p14 is even more pronounced in the p15 FAST protein of baboon reovirus , which contains ecto- and endodomains of 20 and 97 residues , respectively [17] . Previous studies indicate that progressive deletion of the C-terminal endodomain of the p14 FAST protein leads to a progressive loss in cell–cell fusion activity , implying the C-terminal tail is essential for cell–cell membrane fusion [11] . The basis for this phenotype , however , has not been determined . We now show that ectopic expression of the FAST protein endodomains enhances the syncytiogenic activity of the full-length FAST proteins , both homotypically and heterotypically . Results further indicate that the biologically active endodomain fragment of the p14 FAST protein is endogenously generated from the full-length protein in virus-infected or transfected cells . Furthermore , the p14 endodomain peptide , when ectopically expressed in transfected cells , displays the surprising capacity to enhance syncytiogenesis mediated by unrelated viral or cellular fusogens . The syncytium-enhancing ability of the p14 endodomain is not dependent on interactions with either the fusogen or the membranes being fused , and occurs downstream of stable fusion pore formation . The FAST proteins are the first example of viral membrane fusion proteins that generate a soluble , bioactive endodomain fragment that presumably stimulates a cellular process central to the poorly understood sequence of events that promote the transition of stable fusion pores into syncytia .
While analyzing a series of N-terminal truncations of the p14 FAST protein , we made the surprising discovery that co-expression of the p14 endodomain fragment ( that induces no syncytium formation on its own ) with the full-length p14 protein increased syncytiogenesis . Cells co-transfected with full-length p14 plus the p14 endodomain significantly increased the extent of syncytium formation relative to cells co-transfected with p14 plus empty vector , as shown by quantifying syncytial nuclei ( Figure 1A , End construct ) and from microscopic examination of transfected cells ( Figure 1B ) . The p14 endodomain was capable of increasing the fusogenic activity of the full-length protein , but did not rescue the fusion-dead N-terminal ( ΔEct ) or C-terminal ( ΔEnd ) truncated versions of p14 ( data not shown ) . The enhancing activity of the p14 endodomain was only significant at early times post-transfection ( ∼6–8 h for p14 ) , and was not manifested by either ecto- or endodomain constructs that retained the p14 transmembrane domain ( Figure 1A ) . Using the extent of syncytium formation in cells co-transfected with the p14 expression plasmid plus empty vector as a baseline , co-transfection of the non-fusogenic p14 endodomain with authentic p14 increased syncytiogenesis to 60–80% of that obtained in cells transfected with a double-dose of the full-length protein ( Figure 1C ) . In other words , the non-fusogenic p14 endodomain functions almost as well as the full-length protein in enhancing p14-mediated syncytium formation . An N-terminal FLAG-tagged version of the p14 endodomain retained enhancement activity ( Figure 1C ) , and Western blotting with an anti-FLAG antibody was used to confirm expression of the endodomain in transfected cells ( Figure 1D ) . A scrambled version of the endodomain exhibited no enhancement capability ( Figure 1C ) , suggesting this activity is sequence-specific . To determine whether the bioactive property of the p14 endodomain was generally applicable to members of the FAST protein family , similar studies were conducted with the endodomains of the p10 and p15 FAST proteins , using both homotypic and heterotypic co-transfections . Since the kinetics of syncytium formation for the various FAST proteins varies widely [14] , we determined the time range where doubling the dose of the fusogen yielded approximately twice the extent of syncytium formation . The enhancing activity of the endodomain fragments was quantified during this time range , which varied from 6–15 h post-transfection for the various FAST proteins . Results are presented as relative fusion , using cells transfected with a double-dose of the full-length fusogen as 100% fusion capacity and cells co-transfected with the fusogen plus empty vector as 0% fusion . The relative fusion scale accounts both for the varying times and the different extents of cell fusion mediated by the various FAST proteins ( which ranged from ∼60–130 nuclei per field for single and double doses of p10 , respectively , versus ∼390–770 syncytial nuclei per field for p14 ) . Ectopic expression of the p10 and p15 endodomains enhanced syncytiogenesis mediated by their corresponding full-length FAST proteins , albeit at reduced levels compared to the p14 endodomain ( Figure 2A ) , which could reflect either inherent differences in their enhancement activities or variable expression levels of the different endodomains . Interestingly , the activity of the various FAST protein endodomains was not confined to enhancing the activity of the corresponding full-length protein , since syncytium formation was consistently higher in cells co-transfected with various combinations of endodomain and FAST protein than in cells co-transfected with the fusogen plus empty vector ( Figure 2A ) . Using the more robust p14 endodomain as the prototype , we examined the cell-type and fusogen specificity of the endodomain enhancing activity . The syncytium-enhancing activity of the p14 endodomain was not cell-specific , functioning to approximately the same degree in human HT1080 fibroblast and monkey Vero epithelial cells as it did in QM5 quail fibroblasts ( Figure 2B ) . Most interestingly , the p14 endodomain also enhanced the low pH-induced syncytium formation mediated by the unrelated influenza virus hemagglutinin ( Figure 2B ) , and the syncytiogenic activity of the unidentified , endogenous fusogen ( s ) responsible for the differentiation-dependent fusion of C2C12 murine myoblasts into myotubes ( Figure 2C and 2D ) . The 68-residue , non-membrane–anchored form of the p14 endodomain therefore has the surprising ability to function as a general enhancer of syncytiogenesis . A cell–cell pore-forming assay was used to determine whether the p14 endodomain peptide enhanced syncytiogenesis prior or subsequent to the formation of stable fusion pores . QM5 fibroblasts co-expressing p14 , EGFP and either empty vector or the p14 endodomain plasmid were co-cultured with Vero cells labelled with the small aqueous fluor calcein red-orange . The extent of fusion pore formation was estimated using flow cytometry to quantify the percent of EGFP-containing donor cells that acquired calcein red from the target cells . Cells transfected with vector alone displayed a low level of spontaneous dye transfer while expression of p14 resulted in a time-dependent increase in the percent of co-fluorescent cells that coincided with the appearance of syncytia ( Figure 3 ) . In independent experiments , doubling the dose of p14 resulted in a 1 . 6–2 . 2 fold increase in pore formation ( depending on the time point ) , but unlike the syncytiogenesis assay , pore formation in cells co-expressing p14 and the endodomain peptide was indistinguishable from cells co-expressing p14 and empty vector ( Figure 3A ) . In duplicate experiments conducted in triplicate , examining multiple time points over the linear time course of the pore formation assay ( Figure 3B ) , the extent of pore formation in cells expressing p14 plus the endodomain never exceeded that observed in control cells expressing p14 plus empty vector . The p14 endodomain therefore has no inherent ability on its own to promote pore formation or syncytiogenesis , but it displays the remarkable ability to enhance the syncytiogenic activity of functional p14 , and this enhancing activity exerts its effect subsequent to the formation of stable fusion pores . To determine whether the endodomain fragment is naturally generated in cells transfected with only the full-length p14 protein , Western blots of p14-transfected cell lysates obtained 12 h post-transfection were probed using a polyclonal antiserum raised against the p14 protein . In addition to full-length p14 , these blots clearly detected sub-molar amounts of a p14 fragment whose gel mobility closely approximated that of the ectopically expressed p14 endodomain ( Figure 4; p14* ) . In addition , a second , smaller p14 fragment was detected on some blots ( Figure 4; p14** ) , but at reduced levels relative to the p14* fragment . Neither of these fragments ( p14* and p14** ) was ever detected in lysates from vector-transfected cells ( Figure 4 , lane 1 ) . A ten-residue C-terminal truncation of p14 increased the gel mobility of both the p14 and p14* polypeptides but not the p14** fragment ( Figure 4 , lane 4 ) , while a 21-residue N-terminal truncation eliminated detection of the p14** fragment with no effect on mobility of the p14* polypeptide ( Figure 4 , lane 5 ) . These results suggested proteolytic processing of the full-length p14 protein generated the p14* endodomain fragment and the corresponding p14** N-terminal fragment , which was either further degraded or shed from membranes resulting in reduced or undetectable steady state levels of this fragment . Confirmation that p14* represented endogenous generation of the p14 endodomain was obtained using a p14 construct containing a C-terminal FLAG tag . Western blot analysis using an anti-FLAG antibody detected both the p14 and p14* polypeptides but never the p14** fragment ( Figure 4 , lane 9 ) . Most notably , a fragment representing the p14 endodomain was also detected in Vero cells infected with reptilian reovirus ( Figure 4 , lane 6 ) , and the levels of the p14 endodomains endogenously generated in transfected or virus-infected cells were equivalent to , or exceeded , those observed by ectopic expression . The biological activity displayed by ectopic expression of the p14 endodomain is therefore not due to artificial over-expression of the peptide , and the same endodomain fragment is endogenously generated by proteolytic processing of a percentage of the p14 protein at concentrations sufficient to serve as an enhancer of syncytiogenesis . Since the p14 endodomain is endogenously generated from the full-length protein at levels equivalent to those obtained by exogenous expression and sufficient to be bioactive , this raised the question as to the relative contribution of the endogenous and exogenous endodomains to syncytiogenesis . The endogenous and exogenous endodomains were both detectible at similar levels 12 h post-transfection ( Figure 4A ) , ∼4 h after the time when the exogenous endodomain exerts a significant enhancing effect on syncytiogenesis . Expression levels of the endogenous ( data not shown ) and exogenous ( Figure 4B ) endodomains were below detectible levels by Western blotting at 6–8 h post-transfection , when syncytial enhancement was evident . Doubling the dose of the ectopic endodomain resulted in barely detectible levels by 8 h post-transfection ( Figure 4B , lane 3 ) . These results suggested that low levels of the endodomain are sufficient to exert an enhancing effect on syncytium formation . This conclusion was further supported by converting the optimized Kozak consensus sequence used for translation initiation of the exogenous endodomain ( ACCAUGG ) to a sub-optimal sequence ( CTTAUGA ) [28] . This change in the translation start site substantially reduced expression levels of the exogenous endodomain , as shown at 24 h post-transfection to reveal the low level of expression from the sub-optimal translation start site ( Figure 4C ) , but had no significant effect on diminishing fusion enhancement activity ( Figure 4D ) . The p14 endodomain therefore displays bioactive properties at low levels of intracellular expression . However , since only a small percentage of p14 is processed to generate the endodomain , it seems likely that the endogenous endodomain will exist at sub-saturating levels at early times post-transfection , which may explain why ectopic expression enhanced syncytiogenesis at early times but not at later times when the endogenous endodomain may reach saturating levels . A biological and biophysical characterization of the endodomain was undertaken to gain some insight into how this peptide fragment might exert its enhancing activity . Co-expression analysis indicated the endodomain did not increase the steady-state levels of p14 ( see Figure 1D ) . To determine whether the p14 endodomain altered cell surface expression of p14 , cells were co-transfected with the p14 endodomain and p14G2A , a fusion-minus mutant of p14 that displays normal cell surface expression [11] ( p14G2A avoided the complications associated with analyzing large syncytia by flow cytometry ) . Live cells were immunostained using an antiserum specific for the p14 ectodomain . As indicated ( Figure 5A ) , the endodomain did not enhance syncytiogenesis by increasing the surface expression of p14 . The ability of the p14 endodomain to enhance syncytiogenesis mediated by heterologous fusogens makes direct physical interactions between the endodomain and the fusogen unlikely . This was further confirmed by immunoprecipitation of the FLAG-tagged endodomain construct using anti-FLAG antibody , which did not result in co-precipitation of the full-length p14 protein ( Figure 5B ) . Similar analysis of a known multimeric protein , p53 , provided a positive control for the co-immunoprecipitation assay ( Figure 5B ) . The p14 endodomain therefore does not exert its biologically activity via direct interactions with the fusogen . Analysis of the subcellular distribution of the p14 endodomain by immunofluorescence microscopy revealed a diffuse cytosolic/nuclear staining pattern ( Figure 6A ) . In contrast , and as previously reported [11] , the p14 protein displayed the reticular and surface staining pattern characteristic of an integral membrane protein . Subcellular fractionation further indicated the endodomain is a soluble polypeptide , residing within the cytosolic fraction while p14 is found exclusively in the membrane fraction of cells ( Figure 6B ) . Coupled with the observation that the membrane-anchored version of the endodomain did not augment p14-induced cell–cell fusion ( ΔEct in Figure 1A ) , these results imply the endodomain exerts its enhancement activity independent of direct interactions with the membranes being fused . The ability of the endodomain to serve as a general enhancer of syncytiogenesis , functioning in trans from a separate subcellular location as the fusogen , suggested the endodomain influences an intracellular process common to all cell–cell fusion reactions . In view of the generic role of dynamic actin remodelling on membrane fusion events [29] , we examined whether ectopic expression of the p14 endodomain resulted in cytoskeletal rearrangements . Staining F-actin in transfected and non-transfected cells using fluorescent phalloidin revealed no observable differences in the overall architecture of the actin cytoskeleton ( Figure 7 ) , suggesting that any effects of the endodomain on actin are not manifested by gross changes in the structure of the cytoskeleton . This does not preclude the possibility that more subtle effects of the endodomain on actin distribution might influence its trans-enhancing activity . The enhancing activity of the p14 endodomain is sequence-specific , as indicated by the inability of a scrambled endodomain construct to enhance cell–cell fusion ( see Figure 1C ) , suggesting a linear motif may be important in the enhancement mechanism . The p14 endodomain contains a membrane-proximal polybasic region ( KRRERRR ) and a C-proximal polyproline region ( PYEPPSRRKPPPPP ) that contains a pentaproline motif and a PXXP motif , a ligand for SH3 domains [30] . To determine whether these , or other , motifs might contribute to endodomain fusion enhancement activity , we conducted an alanine scan , substituting consecutive groups of three amino acids with alanine residues . These 23 endodomain mutants were quantitatively assessed for their enhancing capacity ( Figure 8 ) . Western blot analysis of the FLAG-tagged mutants revealed slight variations in steady-state levels , but well within the range of expression levels previously shown to be saturating for enhancement activity ( see Figure 4C ) . The three alanine mutants spanning the polybasic region ( Figure 8 , bars 2–4 ) had little if any deleterious effect on the capacity of the endodomain to enhance cell–cell fusion , implying the polybasic region does not exert a significant effect on the fusion enhancing activity of the endodomain . Three other regions of the endodomain were , however , sensitive to alanine substitutions . Region A lies between the polybasic and polyproline motifs , and several substitutions in this region had adverse effects on fusion enhancement ( Figure 8 , bars 7–11 ) . These substitutions affect two potential protein kinase A recognition sites ( XRX[ST}XXX ) , identified using the Eukaryotic Linear Motifs resource ( ELM; http://elm . eu . org ) . Region B ( Figure 8 , bars 15–17 ) occurs in the endodomain polyproline region; alanine substitutions in this region that affected the pentaproline motif ( Figure 8 , bars 17–19 ) had no significant effect on enhancement activity while disruption of the PXXP motif ( PAAA; Figure 8 , bar 15 ) severely restricted enhancement activity . However , the PAAA substitution affects not only the PXXP motif , but also a predicted src homology-2 ( SH2 ) ligand motif ( YEPP ) . Mutant 14 , which eliminated the PXXP motif but not the YEPP SH2 domain-binding motif , was not significantly impaired in its enhanced syncytiogenic activity ( Figure 8 ) , suggesting the potential SH3 domain PXXP ligand motif is unlikely to contribute to the enhancing activity of the endodomain fragment . All four of the substitution mutants contained within region C , the extreme C-terminus of the endodomain , displayed significantly diminished enhancing activity . This C-terminal region includes potential SH2 ( Y[IV]X[VILP] ) and PDZ ( X[DE]X[IVL] or X[ST]X[VIL] ) ligand motifs . Therefore , several regions of the 68-residue p14 ectodomain contain potential linear motifs or structural determinants involved in the ability of this soluble peptide fragment to function as a general enhancer of syncytiogenesis .
The reovirus FAST proteins are a new family of viral fusogens whose structural and functional properties differ extensively from the well-characterized fusion proteins encoded by the enveloped viruses . The unusual topology of the FAST proteins positions ∼60–90% of their mass within the transmembrane and endodomains , suggesting the mechanism by which they induce cell–cell fusion and syncytium formation is particularly focused on the donor cell , the membrane in which they reside . We recently demonstrated that the FAST proteins rely on surrogate cellular adhesins to mediate the membrane attachment and close apposition stages of the fusion reaction [31] . This observation provided the first explanation for the exceedingly small size of the FAST protein ectodomains , which are charged with promoting the fusion of closely apposed lipid bilayers , not with bringing the membranes into close proximity . We now show that an additional explanation for the unusual asymmetric membrane topology of the FAST proteins reflects the generation of a soluble endodomain fragment which functions as a general enhancer of syncytium formation , functioning in trans to promote the conversion of fusion pores into syncytia . The use of surrogate adhesins coupled with the generation of a bioactive endodomain peptide presumably reflects the unique evolution of the FAST proteins as virus-encoded cell–cell fusogens , allowing these diminutive cell–cell fusogens to efficiently induce syncytium formation within the confines of their rudimentary structures . Since the endodomain fragment is endogenously generated from full-length p14 , both in transfected and virus-infected cells ( Figure 4 ) , it seems likely that the enhancing activity of the endodomain is relevant to the mechanism of p14-induced syncytium formation . Additional observations support this speculation . C-terminal residues influence the enhancing , though non-essential , syncytiogenic activity of the soluble endodomain ( Figure 8 ) . In the context of the full-length protein , C-terminal truncation of p14 , which generates a truncated endodomain fragment ( Figure 4 ) , simultaneously reduces the rate , but not the final extent , of p14-induced syncytiogenesis by ∼50% [11] . The C-terminus of the full-length p14 protein therefore enhances syncytiogenic activity , and this same region is essential for the trans-acting activity of the soluble endodomain . Results further indicate that low steady state levels of the endodomain are all that is required for biological activity ( Figure 4 ) . The sensitivity of Western blots was not sufficient to correlate fusion enhancement activity with the steady state levels of the exogenous and endogenous endodomains at early time points . Nonetheless , intracellular concentrations of the endogenously generated endodomain exceed bioactive levels at slightly later time points , consistent with the concept that the enhancing activity of the soluble endodomain is relevant to the natural function of p14 as a cell–cell fusogen . The expression data also serves to explain why ectopic endodomain expression would augment the enhancement activity of the endogenous soluble endodomain , functioning at early times post-transfection to increase the rate at which the soluble endodomain accumulates to bioactive levels inside cells . In addition to the C-terminus , other regions of the endodomain that affect its enhancing activity contain potential protein–protein interaction motifs ( Figure 8 ) . The degenerate nature of the consensus sequences for these motifs makes it unclear whether the endodomain deletion and substitution results reflect disruption of a specific linear motif or global changes to the endodomain structure . If specific linear motifs do contribute to endodomain function , then predicted protein kinase A sites and SH2 and PDZ domain-binding motifs present in the p14 endodomain may be involved . These motifs are widely involved in diverse cell signalling pathways that could influence the efficiency by which the cell promotes the conversion of fusion pores to syncytia [32]–[35] . Since all of the FAST protein endodomains appear to contain at least some level of trans-enhancing activity ( Figure 2A ) , it seems reasonable to assume they might function through the same cellular pathway . It also seems reasonable to assume that the potential protei–protein interactions motifs identified in the p14 endodomain alanine scan might be conserved in the FAST protein endodomains , even though the FAST protein endodomains lack any extended regions of direct sequence conservation . An ELM scan of the p10 and p15 endodomains identified numerous potential protein interaction or post-translational modification motifs . The only common motifs identified in all three endodomains were different classes of PDZ domain ligands , which occur at the C-terminus of p14 and p10 , but internally in the p15 endodomain . Whether these motifs are relevant to the bioactive property of the endodomain and if so , how mutations outside these motifs influence their role in protein interactions remains to be determined . NMR structural analyses of the FAST protein endodomains coupled with pull-down assays are currently underway to assist in interpretation of the mutagenic analyses and to identify cellular partners that may serve as effectors of endodomain bioactivity . There are no direct parallels in the viral membrane fusion protein field to the trans-acting enhancement activity of the p14 endodomain . There are examples where enveloped viral fusion proteins are proteolytically cleaved , for example the maturation cleavage involved in the assembly stage of several retroviruses [36] , [37] . In this instance , cleavage activates the fusion complex by removal of an inhibitory C-terminal peptide , rather than by generating a functional peptide fragment . Similar to the trans-enhancing activity of the soluble p14 endodomain , an artificially truncated version of the fusogenic vesicular stomatitis virus G protein comprised of the endodomain , transmembrane domain and a fragment of the ectodomain enhances the fusion activity of heterologous fusogens [38] . However , this membrane-anchored G-stem polypeptide appears to influence the membrane apposition and/or hemifusion stages of the fusion reaction , which is clearly not the case with the soluble p14 endodomain peptide that functions in an indirect manner , independent of direct membrane interactions , to promote fusion pore expansion . In C . elegans , the Eff-1 fusogen involved in developmental epithelial cell–cell fusion generates a soluble ectodomain fragment that enhances syncytiogenesis [3] , [9] . This fragment has no demonstrated role in enhancing the activity of heterologous fusogens , and it seems unlikely that it would function from the extracellular milieu in a similar manner as the cytosolic p14 endodomain . The features of the trans-acting activity of the p14 endodomain are therefore unique amongst both viral and cellular fusogens . While the precise mechanism by which the soluble FAST protein endodomains enhance syncytiogenesis remains to be determined , several features of this mechanism are apparent . Coupled with observations from other studies , these results provide some interesting into insights into this remarkable biological activity . The ability of the endodomain to enhance syncytiogenesis mediated by the influenza HA fusogen ( which occurs within minutes after triggering by treatment with low pH ) , and the gradual cell–cell fusion induced by the FAST proteins and the endogenous fusogens responsible for myoblast fusion , which induce fusion over hours or days , suggests the effects of the endodomain are constant and sustained over time . Expression of the endodomain did not alter overall cell function since cell morphology and growth properties were not affected ( Figure 7 ) , suggesting the p14 endodomain likely functions in a somewhat specific manner . Furthermore , low steady state levels of the endodomain are all that is required to enhance a step in syncytium formation that occurs after formation of stable fusion pores ( Figures 3 and 4 ) in a manner that is not dependent on direct physical interactions with either the fusogen or the membranes being fused ( Figures 5 and 6 ) . Taken together , the most straightforward explanation for the ability of the p14 endodomain to function as a general enhancer of syncytiogenesis is that the endodomain functions as a signalling peptide to activate or recruit an intracellular pathway broadly involved in the conversion of cell–cell fusion pores to syncytia . We know of no system where the mechanism by which fusion pores expand into syncytia has been clearly defined . In C . elegans , epithelial cell fusion has been kinetically divided into two distinct stages designated microfusion , the actual membrane fusion event that results in rapid and stable pore formation , and macrofusion , a slower pore expansion stage required for syncytium formation [9] , [10] . A similar , kinetically distinct two-stage process has been demonstrated to occur during yeast mating , where fusion pores ( i . e . microfusion ) open quickly and reversibly , followed by slow expansion and macrofusion [39] . Various explanations for how fusion pores might expand to the macrofusion stage have been put forward . These scenarios include , but are not limited to , membrane removal by vesiculation [10] , lateral membrane tension [39] , direct or indirect effects of the fusion protein itself [7] , [25] , and actin-driven effects on membrane tension [29] , [40] . There is also evidence that the rate of pore expansion is influenced by the cell type [25] , suggesting there are cellular pathways that directly influence the macrofusion stage of syncytiogenesis . We therefore propose that the soluble endodomains of the FAST proteins harness a cellular pathway involved in driving the transition from microfusion to macrofusion , perhaps the most energy demanding stage of syncytiogenesis [2] , [23] . There are interesting parallels between the ability of the FAST proteins to generate a bioactive , soluble endodomain peptide , and membrane receptors and ligands that undergo regulated intramembrane proteolysis ( RIP ) [41] , [42] . Proteins such as sterol-regulatory-element–binding protein ( SREBP ) and the Notch receptor are two well-characterized examples of membrane protein substrates that undergo RIP to mediate membrane-to-nucleus signalling . Cleavage by intramembrane cleaving proteases ( iCLIPs ) , such as the presenilin/γ-secretase complex or the site-2 protease , results in release of a bioactive cytoplasmic domain that translocates to the nucleus to initiate signalling cascades that regulate lipid metabolism or diverse cell differentiation processes [43]–[45] . We note that the endogenously generated p14 endodomain fragments were consistently slightly larger than the ectopically expressed endodomain ( Figure 4 ) , suggesting that p14 may also be processed within its transmembrane domain by iCLIPs to generate the bioactive endodomain peptide . Since the soluble p14 endodomain exists as a nucleocytoplasmic peptide ( Figure 6 ) , interaction with cellular proteins in either compartment could alter cellular signalling pathways important in the process that drives expansion of cell–cell fusion pores . Although the soluble endodomain clearly has trans-acting activity , only a small percent of p14 is processed to generate the soluble endodomain . It therefore seems likely that the endodomains of the FAST proteins may also function in cis to influence cell–cell fusion activity . A similar dual cis/trans function has been proposed for other type I membrane proteins that undergo RIP , for instance the Notch receptor ligand Jagged-1 that interacts in cis with proteins involved in organizing cell–cell junctions while functioning in trans as a signalling peptide [46] , [47] . The FAST proteins are the first example of a fusion protein that naturally generates a trans-acting subunit capable of modulating a cellular pathway or process that may be common to all biological cell–cell fusion events . By promoting the transition of fusion pores into syncytia , the trans-acting activity of the C-terminal tail of the FAST proteins allows these simple cell–cell fusogens to efficiently induce syncytium formation within the confines of their rudimentary structures . Clearly , numerous questions regarding the function of the FAST protein endodomains remain to be addressed . What , if any , cis-acting role is exerted by the endodomain ? What regulates p14 processing and why is only a small percentage cleaved ? Does the soluble endodomain exert its enhancing activity from the cytoplasm and/or nucleus ? What cellular partners interact with the soluble endodomain , what pathways are regulated by these partners , and how do these pathways promote fusion pore expansion and syncytium formation ? Most importantly , the general enhancing activity of the p14 endodomain suggests that discovering the effectors regulated by the p14 endodomain may provide insights into cellular pathways that are central to the process of cell–cell fusion in a diversity of biological processes .
The cDNA clones of the NBV p10 , p14 , and p15 FAST proteins in pcDNA3 ( Invitrogen ) were previously described [11]–[13] . Standard PCR techniques were used to generate the p10 and p15 endodomain constructs , and the p14 endodomain ( End , residues 58–125 ) , scrambled p14 endodomain ( SEnd ) , N- ( ΔEct , residues 35–125 ) and C- ( ΔEnd , residues 1–78 ) terminally truncated p14 , and N- ( EΔN , residues 35–125 ) and C- ( EΔC , residues 1–78 ) terminally truncated p14 endodomain expression plasmids . Each N-terminal truncation included an additional alanine residue immediately following the initiator methionine , a consequence of optimizing the context of the translation start site [13] . The p14 endodomain was subjected to alanine scan mutagenesis , substituting consecutive groups of three amino acids with alanine residues , using nested primers and standard PCR techniques . All constructs were confirmed by sequencing . The influenza hemagglutinin ( strain X-31 ) was a gift from Judy White , and was subcloned into pcDNA3 . The N-terminal 3× FLAG-tagged p14 endodomain ( F-End ) construct was obtained by subcloning into pBICEP ( Sigma ) . QM5 and Vero cells were grown and maintained as previously described [11] . HT1080 and C2C12 cells were cultured in MEM or DMEM , respectively , supplemented with penicillin/streptomycin ( 50 µg/ml ) and 10% FBS . The C2C12 myoblasts were induced to differentiate into myotubes by culturing the cells in DMEM supplemented with 2% horse serum for 72 h . The rabbit antiserum against full-length p14 was previously described [11] . Rabbit antiserum against the p14 ectodomain ( residues 1–36 ) was prepared by New England Peptide ( anti-p14ecto ) . Mouse anti-FLAG antibodies ( Sigma ) , horseradish peroxidase-conjugated goat anti-rabbit ( KPL ) and goat anti-mouse ( Jackson ImmunoResearch ) secondary antibodies , Alexa-488-conjugated goat anti-rabbit IgG and Alexa-555-conjugated goat anti-mouse IgG secondary antibodies ( Invitrogen ) were from the indicated commercial sources . FITC-conjugated phalloidin was from Molecular Probes . Cells at 70–80% confluency in 12-well cluster plates were transfected or co-transfected with equivalent quantities ( 0 . 5 µg ) of the various expression plasmids using Lipofectamine ( Invitrogen ) , then supplemented with appropriate serum-containing medium 5 h post-transfection . Transfected cells were fixed at different times post-transfection based on control experiments that determined the linear dose-response range ( i . e . , cells transfected with 1 µg of the p14 expression plasmid yielded twice the level of fusion as cells transfected with 0 . 5 µg of the same plasmid ) . Cells expressing HA were trypsin-activated and fusion was induced by low pH treatment as previously described [31] . A syncytial index from triplicate samples was determined as previously described [14] , by microscopic examination to quantify the average number of syncytial nuclei per field from five random fields of the Giemsa-stained monolayers . The syncytial index was converted to a relative fusion scale to permit comparisons between replicate experiments ( n>3 ) using the formula ( Cfe−Cfv/Cff−CeV ) ×100 . This formula sets cells co-transfected with the fusogen plus empty vector ( Cfv ) as the baseline and cells transfected with a double-dose of the fusogen ( Cff ) as the maximum possible extent of fusion ( 100% ) , and quantifies the extent to which cells co-transfected with the fusogen plus the p14 endodomain ( Cfe ) approach the fusion maximum . Results were analyzed using a two-tailed unlinked t-test to determine statistical significance . QM5 cells were lysed in RIPA buffer ( 50 mM Tris , pH 8 . 0 , 150 mM NaCl , 1 mM EDTA , 1% Igepal , 0 . 5% SDS ) at 8–24 h post-transfection and equivalent protein loads were analyzed by Western blotting , as previously described [13] . Cell lysates were similarly prepared from cells infected with reptilian reovirus [48] for 20 h . For detection of the sub-molar , endogenously generated endodomain fragment , the anti-p14 antiserum was used at 1∶3000 dilution instead of 1∶10 , 000 . Sub-confluent monolayers of QM5 fibroblasts were co-transfected with plasmids expressing p14 and EGFP and either empty vector or the p14 endodomain plasmid . At 4 h post-transfection , these cells were overlaid with Vero cells ( 5∶1 ratio of Vero to QM5 ) labelled with 20 µM calcein red-orange AM ( Molecular Probes ) . The two cell populations were co-cultured at 37°C to allow fusion to proceed . At various times ( 2–4 h ) , the cell cultures were detached from the substratum , fixed and analyzed by flow cytometry ( FACSCalibur ( Becton Dickinson ) ) using appropriate filter sets and Cell Quest software . EGFP-positive donor cells were gated , and the percent of these donor cells that acquired calcein red was quantified . A minimum of 10 , 000 events were recorded , and all data were analyzed using FSC Express 2 . 0 ( De Novo Software ) . Cells were co-transfected with p14G2A , a fusion-minus mutant of p14 that displays normal cell surface expression ( this mutant was used to avoid the complication of trying to analyze large syncytia by flow cytometry ) and either empty vector or the p14 endodomain . Transfected cells were washed with PBS supplemented with 5% BSA at 8–24 h post-transfection , and cells were then incubated with 1∶200 dilution of anti-p14ecto antiserum followed by 1∶2000 dilution of Alexa-647–conjugated goat anti-rabbit antibody . Cells were detached from the substratum with 50 mM EDTA in PBS and analysed by flow cytometry . Transfected cells grown on gelatin-coated coverslips were fixed at various times post-transfection using 3 . 7% formaldehyde , and permeabilized with 0 . 1% Triton X-100 . The cells were blocked with normal goat serum , then stained using rabbit anti-HA and mouse anti-FLAG antibodies ( 1∶200 and 1∶2000 , respectively ) and Alexa-488–conjugated goat anti-rabbit IgG and Alexa-555–conjugated goat anti-mouse IgG ( Invitrogen ) . Images were captured using a Zeiss META 510 confocal microscope .
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The reovirus FAST proteins are the only known examples of nonenveloped virus membrane fusion proteins . Functioning as virus-encoded cellular fusogens , they mediate cell–cell membrane fusion and syncytium formation rather than virus–cell fusion . The FAST proteins are also the smallest protein fusogens and assume an unusual membrane topology , positioning the majority of their mass within or internal to the membrane in which they reside . We have been interested in reconciling the donor membrane-biased structural features of the FAST proteins with their ability to orchestrate the multi-step cell–cell membrane fusion process that leads to syncytium formation . We now show that the FAST proteins generate a soluble endodomain fragment that functions in trans from the cytosol , enhancing the capacity of diverse viral and cellular fusogens to drive the conversion of fusion pores into syncytia . The FAST proteins may therefore function in a similar manner as membrane receptors whose signalling activity requires regulated intramembrane proteolysis to generate a soluble signalling peptide . The endodomain signalling peptide of the FAST proteins provides a novel approach to identify cellular effectors involved in the fusion pore expansion stage of biological cell–cell membrane fusion .
|
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"Introduction",
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"and",
"Methods"
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"molecular",
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"regulation",
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2009
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Enhanced Fusion Pore Expansion Mediated by the Trans-Acting Endodomain of the Reovirus FAST Proteins
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The ability of human and murine APOBECs ( specifically , APOBEC3 ) to inhibit infecting retroviruses and retrotransposition of some mobile elements is becoming established . Less clear is the effect that they have had on the establishment of the endogenous proviruses resident in the human and mouse genomes . We used the mouse genome sequence to study diversity and genetic traits of nonecotropic murine leukemia viruses ( polytropic [Pmv] , modified polytropic [Mpmv] , and xenotropic [Xmv] subgroups ) , the best-characterized large set of recently integrated proviruses . We identified 49 proviruses . In phylogenetic analyses , Pmvs and Mpmvs were monophyletic , whereas Xmvs were divided into several clades , implying a greater number of replication cycles between the integration events . Four distinct primer binding site types ( Pro , Gln1 , Gln2 and Thr ) were dispersed within the phylogeny , indicating frequent mispriming . We analyzed the frequency and context of G-to-A mutations for the role of mA3 in formation of these proviruses . In the Pmv and Mpmv ( but not Xmv ) groups , mutations attributable to mA3 constituted a large fraction of the total . A significant number of nonsense mutations suggests the absence of purifying selection following mutation . A strong bias of G-to-A relative to C-to-T changes was seen , implying a strand specificity that can only have occurred prior to integration . The optimal sequence context of G-to-A mutations , TTC , was consistent with mA3 . At least in the Pmv group , a significant 5′ to 3′ gradient of G-to-A mutations was consistent with mA3 editing . Altogether , our results for the first time suggest mA3 editing immediately preceding the integration event that led to retroviral endogenization , contributing to inactivation of infectivity .
Retroviruses that integrate into the germ line may be inherited vertically as endogenous retroviral sequences ( ERVs ) [1] . A considerable fraction of mammalian genomes consists of ERVs [2–4] , most with numerous inactivating mutations , thus presenting the only known viral “fossil” record . One of the recently discovered cellular defense mechanisms against retroviral propagation involves APOBEC3 ( A3 ) -induced C-to-U deamination in negative-strand retroviral DNA during reverse transcription [5] , resulting in a G-to-A hypermutated provirus [6–9] . The role of human and murine A3 ( hA3 and mA3 , respectively ) family members in inhibiting infection by exogenous retroviruses and retrotransposition of some mobile elements is becoming well established [10–12] . Less clear is the possible effect that these restriction factors may have had on the establishment of the many thousands of endogenous proviruses present in vertebrate genomes [13] . Although A3-induced G-to-A mutations can be readily detected in experimental infection , such mutations are difficult to discern in elements that have had long residence in the germline and that have suffered considerable post-integration mutagenesis . The mouse genome harbors a diversity of endogenous ( nonecotropic ) murine leukemia viruses ( MLVs ) , which form the best-characterized large set of recently integrated proviruses , as indicated by their insertional polymorphism among inbred mouse strains [14] , and by the presence of some infectious members [15 , 16] . The group can be subdivided into the polytropic ( Pmv ) , modified polytropic ( Mpmv ) , and the xenotropic ( Xmv ) proviruses [17] . Each common inbred mouse strain contains about 20 proviruses of each type , and shares about half of them with any other inbred strain [18] . Although several infectious Xmv loci , including Bxv1 ( Xmv43 ) , have been described [15 , 18–20] , and functional Pmv and Mpmv env genes can be rescued by recombination [17 , 21–23] , no infectious Pmv or Mpmv has yet been detected . Here , we have taken advantage of the well-characterized endogenous nonecotropic MLVs as an appropriate model for studying recent evolution of the host–virus interaction , in an attempt to demonstrate probable events associated with endogenization . Genetic studies [18] have revealed 54 nonecotropic proviruses in C57BL/6J mice; we have now identified 49 of these proviruses within the genome sequence of these mice ( http://genome . ucsc . edu/ ) . We analyzed genetic variation within and among subgroups and found mutation patterns consistent with mA3 editing of Pmv and Mpmv DNA , but not Xmv DNA , as a plausible factor contributing to inactivation of these ERVs in the mouse genome .
We mined the C57BL/6J genome sequence for sequences of proviruses we had previously identified using a restriction mapping strategy [18] . We used the sequences of MLV env probes JS-4 , JS-5 , and JS-6 [14] in BLAST searches ( http://www . ensembl . org ) and BLAT searches ( http://genome . ucsc . edu/ ) . Based on predicted reactivity with the specific probes , predicted restriction fragment size , and other features , we were able to identify 49 ( 23 Pmv , 13 Mpmv , and 13 Xmv ) of the known 54 non-Y-linked nonecotropic proviruses in this strain ( Table S1; Figure S1 ) [18] . Sequences encoding viral proteins were verified using RetroTector as described in earlier papers [13 , 24] . Automated PERL scripts were used to verify integration sites and proviral orientation and to extract target site duplications from the C57BL/6J genome version mm8 freeze date Feb . 2006 ( Figure S2 ) . gag , pol and env genes were concatenated and aligned using ClustalX [25] followed by manual tuning to reconstruct open reading frames ( ORFs ) for each provirus compared to alignment majority rule consensus sequences ( Figure S3 ) . Stop codons were mainly caused by G-to-A mutations , which we altered to maintain a nonsynonymous substitution for PAML analyses [26] ( see below ) . Additionally , to extend the analysis outside coding genes and retrieve as many detectable mutations as possible , full provirus nucleotide sequences were aligned using BLASTalign [27] , with no additional attention to ORFs , and consensus provirus sequences were constructed . Maximum parsimony , maximum likelihood , and Bayesian methods , using MEGA3 [28] , PHYML [29] and MrBayes [30] , were utilized for different steps and confirmations of phylogenetic reconstructions . A maximum likelihood phylogeny was reconstructed for the codon and ORF adjusted internal regions ( gag , pol , and env ) of the nonecotropic MLVs and reference sequences ( MoMLV , MLV-ecotropic , and HuXmv ) using PHYML , with the HKY + γ model ( parameter values estimated from dataset ) . Nonsynonymous versus synonymous substitution ratios ( dN/dS ) were calculated for the branches in the maximum likelihood tree by using PAML [26] . A single dN/dS ratio for the subtrees ( one-ratio model ) and separate estimated values for the inner and outer branches ( two-ratio model ) were estimated for each subgroup . Significance of the differences between the two models was evaluated by likelihood ratio tests , by comparing twice the difference of log likelihoods of subtrees to the χ2 distribution with 1 degree of freedom [31] . We tested the internal branches for deviation from neutrality by fixing their dN/dS to 1 and comparing the difference by using the likelihood ratio test . Mutations of aligned sequences compared to each respective group consensus were collected for gag , pol , and env using automated PERL scripts . Codons from each provirus alignment position including at least one G-to-A mutation compared to respective subgroup consensus sequence were recorded for each gene . Codons were aligned and analyzed for synonymous- and nonsynonymous mutations . For each gene , we also analyzed which codon positions had G-to-A mutations and if stop codons were introduced by the mutations . Using automated PERL scripts , we tested if G-to-A mutations followed a distribution suggestive of A3 editing correlating with the persistence of ( − ) single-stranded DNA during reverse transcription [32–34] . Alignment positions between the primer binding site ( PBS ) and polypurine tract in the full provirus alignments ( without additional attention to ORFs , see above ) of each subgroup were collected and divided into ten equally large bins varying slightly in size among subgroups due to different alignment lengths . Thereafter , the fraction of G-to-A mutations divided by the number of consensus sequence G positions was recorded for each bin . A similar analysis was conducted for C-to-T mutations . The sum of all G-to-A mutations for each subgroup was calculated and used for simulation of theoretical G-to-A mutations at possible sites ( G nucleotide positions ) in the consensus sequences . We applied two probability models: ( i ) An equal random probability model for a G-to-A mutation to occur for every alignment consensus G nucleotide; and ( ii ) A triangular skewed random probability model with a minimum probability for G-to-A mutations at consensus G nucleotides at the 5′-end of the genome and a maximum at the 3′ end . Fractions of G-to-A mutations in simulations were calculated as for the observed data above . Likewise , simulations were conducted for C-to-T mutations .
Because of their relatively large copy number , relatively recent insertion into the host germline , and thorough genetic characterization , we took advantage of the nonecotropic MLVs [14 , 16 , 18] to examine events surrounding endogenization of these elements in the mouse genome . Of particular interest is the apparent absence , as judged by the absence of reports to the contrary , of infectious virus from two of the three subgroups ( Pmv and Mpmv ) . BLAST searches ( http://www . ensembl . org ) using MLV env probes JS-4 , JS-5 , and JS-6 [14] , and BLAT searches ( http://genome . ucsc . edu/ ) led to identification of sequences of 49 nonecotropic proviruses ( 23 Pmv , 13 Mpmv , and 13 Xmv ) of the 54 known to be present in this strain of mouse ( Table S1; Figures S1 and S2 ) [18] . To examine the relationship of the three subgroups , we performed maximum likelihood phylogenetic analyses on the manually adjusted gag , pol , and env regions , as well as on internal regions from three reference sequences ( Figure 1 ) . The Pmv and Mpmv subgroups were monophyletic , whereas the Xmv sequences were not , and could themselves be divided into three well-supported clades , with branch lengths implying larger numbers of viral replication cycles between the integration events than in the other two groups ( Figure 1 ) . The tree also implies that the most recent common ancestor of all the proviruses was xenotropic . To investigate differences in proliferation and possible purifying selection between the subgroups , we analyzed the nonsynonymous-to-synonymous substitution ratios ( dN/dS ) for the branches of subtrees derived from the maximum likelihood tree ( Figure 1 ) . We tested two models: ( i ) A one-ratio model with the same dN/dS calculated for each branch , and ( ii ) A two-ratio model distinguishing internal branches from terminal branches . A dN/dS ratio less than 1 implies purifying selection and would normally be expected in a group of retroviruses that is actively replicating . When a provirus becomes immobilized in the genome and is no longer subject to purifying selection , it adapts a neutral mutation rate ( dN/dS = 1 ) . These properties are expected to result in phylogenetic reconstruction for endogenous retroviruses where internal branches show lower dN/dS ratios than terminal branches [35] . The difference between the two models can then be estimated by a likelihood ratio test; i . e . , twice the difference in likelihood of the two different trees , compared to the χ2 distribution with 1 degree of freedom [31] . With a caveat for small sequence differences ( Figure 1 ) and thus low analysis power , we found that the Pmv and Mpmv proviruses showed high dN/dS ratios , not significantly different from 1 with no significant difference between the two models ( Table 1 ) , a result that may be attributable to the small intragroup differences observed in short branch lengths and low bootstrap supports in the maximum likelihood tree ( Figure 1 ) . However , the Xmv proviruses , taken as a whole , had dN/dS significantly below 1 , with significantly lower values for the internal branches ( Table 1 ) , indicative of purifying selection and , therefore , more cycles of active proliferation in both internal and terminal branches compared to the other two subgroups . MLV PBSs have previously been reported to vary in sequence , implying use of both Pro and Gln1 tRNAs as primers for reverse transcription [36 , 37] . Analysis of the endogenous nonecotropic MLV dataset showed a mix of PBSs corresponding to four types of tRNA ( Pro , Gln1 , Gln2 , and Thr ) dispersed within the maximum likelihood tree , indicating changes probably resulting from mispriming during reverse transcription ( Figure 1; Table S2 ) . Although an exact pattern of PBS replacement could not be inferred , the presence of common PBS types in the three different provirus types separated with moderate to high bootstrap supports implies that such mispriming must have been a frequent event ( Figure 1 ) . From the codon-adjusted gag , pol , and env alignments , all proviruses were analyzed for mutations relative to the consensus for their group . The Xmvs differed on average by 1 . 8% ( 0 . 3%–5 . 9% ) from the group consensus ( estimated from the alignment used for Figure 1 and extreme values excluded ) , compared to differences of 0 . 18% ( 0%–0 . 5% ) and 0 . 21% ( 0 . 1%–0 . 3% ) for Pmv and Mpmv from their respective consensuses . Between the groups , consensus sequences differed by 2 . 3% comparing Pmv to Mpmv , and 5 . 2% and 5 . 1% , respectively , comparing either to Xmv . The two proviruses excluded from the consensus analysis ( Pmv4 and Mpmv5 , Figure 1 ) exhibited a high frequency of G-to-A mutations compared to C-to-T mutations ( 123 versus two and 63 versus five , respectively; see below ) . In other retroviruses , such mutations are associated with the activity of cytosine deaminases , including human hA3G and hA3F , and mouse mA3 , on minus-strand DNA during reverse transcription [6 , 9 , 10 , 38] . We therefore performed a more detailed analysis of the mutation spectrum in all proviruses . Sites with G nucleotides in the consensus sequence and A in any provirus sequence were collected and analyzed . To control for mutations occurring after reverse transcription and integration , the same procedure was conducted for C-to-T mutation sites . A significant bias of G-to-A relative to C-to-T changes was seen in both Pmv and Mpmv proviruses ( Figure 2D ) , implying a strand specificity that can only have occurred prior to integration . Within these two groups , G-to-A mutations constituted a large fraction of total mutations with no preference for codon position in any of the genes ( unpublished data ) , and a significant fraction of these mutations led to introduction of stop codons and nonsynonymous changes in all genes ( Figure 2 ) , implying an absence of purifying selection following mutation consistent with the dN/dS ratios from the maximum likelihood tree ( Table 1 ) , with the caveat that the sequence differences in the Pmv and Mpmv subgroups were small ( Figure 1 ) , resulting in somewhat low analysis power . In fact , all but one of the nonsense mutations in these proviruses were the result of G-to-A mutations , and the high dN/dS ratios for Pmv and Mpmv ( Table 1 ) could be attributed entirely to the nonsynonymous G-to-A mutations ( Figure 2A and 2B ) . By contrast , the Xmv proviruses did not display the same clear mutational pattern as Pmv and Mpmv . Although the Xmvs had higher intragroup sequence diversity ( Figure 1 ) , which could have masked some G-to-A mutational bias , the predominance of purifying selection ( Table 1 ) , the lack of significant bias for G-to-A as compared to C-to-T mutations , and an almost complete lack of stop codons in all genes ( Figure 2C ) , implies that the Xmvs were less subject to editing than the other two groups . Analysis of the three Xmv clades separately also confirms the lack of an excess of G-to-A over C-to-T mutations ( unpublished data ) . To determine whether there was a preferred sequence context for the G-to-A mutations observed , alignments of observed nucleotide frequencies relative to expected nucleotide frequencies , ten nucleotides up- and downstream of the putative mA3 target C nucleotide in the viral ( − ) strand DNA were plotted ( Figure 3 ) . This analysis revealed a highly significant optimal sequence context for dC deamination , identical in both Pmv and Mpmv . This sequence , TTC , was consistent with the preferred sequence for mA3 [39] , but differed slightly from the TCC ( however , possibly also TTC ) reported earlier for mA3 [9] . The sequence context may be extended somewhat ( to TTTCTW ) by combining the Pmv and Mpmv results ( Figure 3 ) . No significant consensus was seen in the Xmv subgroup ( Figure 3 ) , again consistent with a lack of effect of mA3 on these proviruses . The preference of A3G editing for single-stranded DNA and the mechanism of reverse transcription lead to a gradient of mutation frequency in proviral DNA , increasing from 5′ to 3′ between the sites of priming [32 , 33] . To determine whether such a gradient could also be observed in the nonecotropic proviruses , we divided the genomes of each subgroup into ten equally large bins and plotted the 5′ to 3′ cumulative fraction of each G-to-A mutation relative to the consensus sequence ( Figure 4 ) . For comparison purposes , we performed simulations based on the total number of mutations ( Figure 4 , open symbols ) . The two simulation models plotted were based on: ( i ) equal random probability for a G-to-A mutation to occur for every alignment consensus G nucleotide , and ( ii ) a triangular skewed random probability model with a minimum probability for G-to-A mutations at the 5′ end and a maximum at the 3′ end . All plots were normalized for direct comparisons . Thus , the cumulative and normalized equal random simulation plot is linear and the cumulative normalized skewed random simulation plot would be expected to follow a power function . In the case of Pmv , the plot of G-to-A mutations was not distinguishable from the skewed distribution ( p = 0 . 83 , χ2 test ) and was significantly different from the equal random distribution ( p = 0 . 01 , Figure 4B ) , whereas the distribution of C-to-T mutations followed an equal random distribution ( unpublished data ) . This result suggests that G-to-A mutations were introduced at a rate corresponding to the persistence of ( − ) strand DNA during reverse transcription , in accordance with previous studies on lentiviruses [32–34] . The pattern with the Mpmvs ( Figure 4C ) , although also suggestive of a gradient of mA3 activity , was much less clear , due to a lower overall frequency of G-to-A changes , and a higher frequency of background mutations . Again , no evidence for A3 activity could be seen with the Xmvs ( Figure 4D ) . We further explored mA3 editing by comparing the total nucleotide compositions of the nonecotropic proviruses ( Figure 5 ) . For both Pmv and Mpmv subgroups , an increase of A was correlated with a depletion of G nucleotides ( Figure 5 , R2 = 0 . 90 ) . In each these two subgroups , one provirus exhibited a much greater degree of G-to-A mutations than the others . When compared to their respective subgroup consensus sequences , the Pmv4 provirus had more than 60 times as many G-to-A as C-to-T mutations ( 123 versus two ) , and the Mpmv5 provirus had about 12 times as many G-to-A mutations compared to C-to-T mutations ( 63 versus 5 ) , resulting in the skewed G and A nucleotide distributions observed in Figure 5 . Furthermore , the distribution of mutations across the Pmv4 genome followed the skewed random pattern ( Figure 4B ) . We conclude that this provirus had suffered a higher deamination rate during reverse transcription [32 , 33] . We could not conclude the same distribution for Mpmv5 due to higher background of mutations . Since Pmv4 and Mpmv5 each contributed roughly 40% of the observed G-to-A mutations to the totals for their respective subgroup , we were concerned that they might have biased the results for each subgroup as a whole . To examine the extent of the contribution from these two “hypermutated” proviruses in the mA3 target site analysis ( Figure 3 ) , all analyses were repeated after their removal . Clear signs of mA3 editing could still be observed for the remaining Pmv and Mpmv proviruses ( Figures S4–S7 ) . Two Xmv proviruses—9 and 42—also exhibited much longer terminal branch lengths than others in the same clade ( Figure 1 ) . In the case of Xmv42 , there were clear signs of recombination with a provirus of the Pmv group [40] . Closer examination ( Figure 5 ) reveals that the increased branch length was not associated with G-to-A hypermutation in either of these two proviruses .
Endogenous proviruses constitute a large fraction of the genomes of well-characterized animal species , and also contribute in important ways to the phenotypes of these organisms . In this study , we used the best-characterized dataset of recently integrated endogenous retroviruses [14 , 16 , 18] to study probable factors involved in their endogenization , genetic variation , and replicative silencing . For this purpose , we searched the C57BL/6J mouse genome sequence ( http://genome . ucsc . edu/ ) to extract the sequences corresponding to the previously described nonecotropic ( Pmv , Mpmv , and Xmv ) proviruses of inbred mice [18] . These closely related proviruses were initially grouped by their reactivity with oligonucleotide probes corresponding to a highly variable region in env and individual proviruses were identified by the size of provirus–host junction restriction fragments . They were localized on the mouse genome using classical genetic mapping techniques [14 , 41] . Using an analogous in silico approach , we were able to positively identify only 49 of the 54 known proviruses in the C57BL/6J strain . For several reasons , we believe that the discrepancy is the result of errors in the reported sequence , not of errors in initial identification of the proviruses or substrain differences . The five missing proviruses—Pmv3 ( Chromosome 12 ) , Xmv6 ( Chromosome 6 ) , Xmv14 and Xmv44 ( both Chromosome 4 ) , and Xmv45 ( Chromosome 5 ) —are all present in more than one mouse strain; all loci are inherited within the AXB , BXA , BXH , and BXD collections of recombinant inbred mice confirming their presence in the C57BL/6J substrain [18] . Flanking sequences from Xmv14 and Xmv44 have been determined and their genetic linkage to one another ( as well as Xmv 8 and 9 ) on distal mouse Chromosome 4 was confirmed in a large genetic cross [42] . BLAST analyses with these flanking sequences demonstrate the presence of sequences corresponding to these provirus host junctions within the pool of sequences assembled to generate the whole mouse genome sequence . However , they are present only on very small contigs that have not been assigned within the whole assembly . Moreover , the flanking sequences in Xmv44 also yield multiple high similarity hits within a cluster of zinc finger repeat genes present on mouse Chromosome 4 . The Xmv14 flank also yields a multitude of high similarity hits but with different genomic regions . We speculate that the absence of Xmv14 and 44 , and by extension the other missing proviral loci , results from difficulties in assembling final genome sequence in regions containing lengthy repeats . Phylogenetic analysis of the complete coding regions of the nonecotropic proviruses revealed a grouping into clades partially consistent with that inferred from the use of a single probe . The Pmv and Mpmv proviruses each form a single well-supported clade , and share a common ancestor relative to the Xmvs . The Xmvs , by contrast , form three well-supported clades , one of which appears to have given rise to the other two groups . Thus , the common ancestor for the whole group was most likely an Xmv-like provirus , possibly with some additional recombination involving the env genes ( our unpublished data ) and there appear to have been considerably more cycles of viral replication separating the Xmv proviruses than the other two groups , a conclusion supported by their greater diversity and relatively low dN/dS ratios , particularly in the internal branches of the phylogenetic tree . The low dN/dS ratios in the terminal branches of the Xmv proviruses imply that these branches represent both repeated cycles of virus replication as well as events proximal to and following integration of each individual provirus . Of the three groups , only the Xmvs have been seen to give rise to infectious virus , although functional Pmv and Mpmv env genes have been recovered in polytropic viruses derived by recombination with ecotropic MLV [22] . Examination of the sequences of the recovered proviruses implies that , at least in part , this difference is due to much higher rates of nonsynonymous mutation in the latter groups: more than half ( 20/35 ) of the undeleted Pmv and Mpmv proviruses have one or more G-to-A mutations leading to stop codons , while only one of eight undeleted Xmv proviruses has been so affected ( Figure 2; Table S1 ) . Of the remaining Pmvs and Mpmvs , all have suffered G-to-A mutations relative to the likely ancestor , ( an average of seven and five nonsynonymous changes per provirus , respectively ) . While the effects of each of these mutations on the function of the virus genes is unknown , it is likely that the net effect is to reduce or eliminate the ability of most or all of these proviruses to yield replication-competent virus . The high frequencies of G-to-A changes in the Pmv and Mpmv groups led us to consider a possible role for mA3-mediated deamination in the generation of genetic diversity among the proviruses . Previous studies of A3 editing have been done mainly in lentiviruses [6 , 9 , 43] , and mostly with hA3 ( for a recent review see Holmes et al . [44] ) . mA3 has been shown to restrict retrotransposition of endogenous MusD and IAP mobile elements in mouse cells in culture , although there is less evidence for its action on the corresponding endogenous proviruses [11] . mA3 activity has also recently been shown to partially restrict infection with mouse mammary tumor virus [12] . Thus , there is evidence for mA3 editing of murine betaretrovirus-like elements . In the present study , we observed mutation patterns indicative of mA3 editing in some gammaretroviruses , as well; specifically , the nonecotropic Pmv and Mpmv subgroups . Several lines of evidence support the conclusion that a large fraction of the mutations that distinguish the individual proviruses from their consensus were caused by mA3 editing . First , the high ratios ( 9:1 and 3:1 ) of G-to-A relative to C-to-T changes and the absence of purifying selection subsequent to mutation imply that most of the mutations arose during the last cycle of reverse transcription prior to integration of each provirus , and that , like human and mouse A3 , deamination was specific for single-stranded DNA . Second , the inferred consensus sequence for C deamination ( on the minus strand ) , TTC , is identical to that observed for mA3 in more direct experiments [39] . Third , at least in the Pmv group , there is a clear 5′–3′ gradient of G-to-A ( but not C-to-T ) mutations across the provirus . As has been pointed out before [32] , such a gradient reflects the facts that A3 can only deaminate single-stranded DNA , and that minus-strand DNA near the 3′ end of the genome remains single stranded for a longer time than 5′ DNA during reverse transcription . We should note that , although we consider mA3 to be the most likely mediator of the G-to-A mutations observed , we cannot exclude participation of other cytidine deaminases , such as APOBEC1 [45] in these modifications . Experiments to examine the expression of the various APOBECs in germ line cells may help to resolve this issue . The Xmv proviruses , with at least one infectious member , exhibit none of the mutational characteristics suggestive of mA3 editing and have evolved differently from Pmv and Mpmv ( Figure 1; Table 1 ) . Indeed , in contrast to the other two groups , Xmv proviruses exhibit a significantly higher ratio of C-to-T relative to G-to-A changes ( Figure 2 ) , possibly reflecting effects of purifying selection during their replication as viruses . This difference is not due to masking of G-to-A mutation by the higher overall diversity in the Xmv group . Thus , it appears that either ( i ) the xenotropic MLVs evolved a function to block the activity of mA3 , perhaps by exclusion from virions , or ( ii ) the Pmv and Mpmv have lost this function . Given that the Xmv proviruses represent the ancestral group , the latter possibility seems much more likely . Loss of such a function might also provide a partial explanation for previous failures to isolate infectious Pmvs/Mpmvs from mouse cells by coculture despite the presence of multiple ERVs with a full complement of ORFs . A third possibility , given the complex origin of inbred mice and of their coevolution with murine retroviruses [1] , is that germline integration of the Pmv and Mpmv proviruses occurred in a subspecies that expressed mA3 in the germline , while the host for the Xmvs did not . We are initiating studies to examine these possibilities , as well as the possibility that deaminase independent effects [46] might also have played a role in endogenous provirus formation . In other retroviral genera , evasion of A3 activity is related to the ability of the virus to prevent incorporation of A3 into virions . For example , at least some lentiviruses and spumaviruses encode proteins ( Vif and Bet ) for this purpose , and deltaretroviruses , such as HTLV-1 , use a C-terminal extension of NC to prevent interaction of APOBEC and RNA [47] . Variation of mA3 packaging into MLV virions has been proposed as a probable cause of observed variation in editing [43] , but these results are controversial , since other studies showed no inhibition of MLV by mA3 [9 , 48 , 49] . This effect has been attributed to both its exclusion from the virion and proteolytic processing of the APOBEC that does get incorporated . The evasion of A3 deamination by MLV is specific for mA3 , since MLV is not resistant to hA3G [48 , 49] , analogous to the sensitivity of HIV to mA3 [9] . In an attempt to identify differences that might contribute to the variation of mA3 editing among the provirus groups , we parsed the NC region of the alignment used to construct the maximum likelihood tree ( Figure 1 ) . Variable positions in Gag , particularly in NC , are being evaluated for possible roles in preventing mA3 activity . In summary , to our knowledge , we have shown here for the first time mA3 editing immediately preceding the integration event of endogenous gammaretroviruses . This activity is likely to have contributed to the inactivation of infectivity of two of the three nonecotropic MLV subgroups .
The National Center for Biotechnology Information ( NCBI ) Entrez database ( http://www . ncbi . nlm . nih . gov/sites/gquery ? itool=toolbar ) for the reference sequences discussed in this paper are MoMLV , NC_001501; MLV-Ecotropic , DQ366147; and HuXmv , EF185282 .
|
Vertebrate genomes are littered with remnants from earlier retroviral infections , in the form of endogenous retroviruses ( ERVs ) . Cellular host defenses against retroviruses , including the APOBEC3 family of cytidine deaminases , have been described previously . APOBEC3 proteins have been shown to edit some retroviruses and other retrotransposing elements during their replication by deamination of C to U during negative-strand synthesis , resulting in G-to-A mutations in the sense strand . Here , we studied the possible effects that the APOBEC-protein family might have had in the establishing ERVs . We identified 49 endogenous ( nonecotropic ) murine leukemia viruses , divided into three groups; polytropic , modified polytropic , and xenotropic , in the sequenced C57BL/6J mouse genome . We analyzed genetic variation within and among subgroups and found mutation patterns consistent with APOBEC3 editing of Pmv and Mpmv , but not Xmv proviruses . Evidence such as ( i ) significantly higher G-to-A mutation frequencies compared to controls and large fractions leading to inactivating stop mutations , ( ii ) optimal sequence contexts surrounding the mutation positions , and ( iii ) editing gradient following the time course of retroviral replication , implicate APOBEC3 as a factor contributing to inactivation of these ERVs in the mouse genome .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion",
"Supporting",
"Information"
] |
[
"viruses",
"mammals",
"virology",
"microbiology",
"evolutionary",
"biology",
"genetics",
"and",
"genomics",
"mus",
"(mouse)"
] |
2007
|
Role of APOBEC3 in Genetic Diversity among Endogenous Murine Leukemia Viruses
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The sequence of a promoter within a genome does not uniquely determine gene expression levels and their variability; rather , promoter sequence can additionally interact with its location in the genome , or genomic context , to shape eukaryotic gene expression . Retroviruses , such as human immunodeficiency virus-1 ( HIV ) , integrate their genomes into those of their host and thereby provide a biomedically-relevant model system to quantitatively explore the relationship between promoter sequence , genomic context , and noise-driven variability on viral gene expression . Using an in vitro model of the HIV Tat-mediated positive-feedback loop , we previously demonstrated that fluctuations in viral Tat-transactivating protein levels generate integration-site-dependent , stochastically-driven phenotypes , in which infected cells randomly ‘switch’ between high and low expressing states in a manner that may be related to viral latency . Here we extended this model and designed a forward genetic screen to systematically identify genetic elements in the HIV LTR promoter that modulate the fraction of genomic integrations that specify ‘Switching’ phenotypes . Our screen identified mutations in core promoter regions , including Sp1 and TATA transcription factor binding sites , which increased the Switching fraction several fold . By integrating single-cell experiments with computational modeling , we further investigated the mechanism of Switching-fraction enhancement for a selected Sp1 mutation . Our experimental observations demonstrated that the Sp1 mutation both impaired Tat-transactivated expression and also altered basal expression in the absence of Tat . Computational analysis demonstrated that the observed change in basal expression could contribute significantly to the observed increase in viral integrations that specify a Switching phenotype , provided that the selected mutation affected Tat-mediated noise amplification differentially across genomic contexts . Our study thus demonstrates a methodology to identify and characterize promoter elements that affect the distribution of stochastic phenotypes over genomic contexts , and advances our understanding of how promoter mutations may control the frequency of latent HIV infection .
Non-genetic heterogeneity is a ubiquitous feature of cellular gene expression that can significantly impact the genotype–phenotype relationship . Even under highly controlled culture conditions , a clonal population of cells may demonstrate a broad range of expression levels for a given gene [1]–[4] . At least some of this variability , often termed ‘noise’ , is believed to arise from the intrinsically stochastic nature of the biochemical processes involved in gene expression [5] , [6] . Studies that couple quantitative experimentation with mathematical modeling have begun to reveal the mechanisms by which non-genetic variability is generated and moderated [7] , finding that noise: differentially impacts the expression of functional classes of genes [8] , [9]; can be propagated , amplified , or attenuated by gene regulatory circuits [10] , [11]; and is subject to selective pressure [12]–[15] . Stochastically-generated expression variability is increasingly appreciated to have important phenotypic consequences in diverse cellular settings , including bacterial evasion of antibiotic treatment [16] , multi-cellular development [17] , cancer development and progression [18] , and viral latency [19] , [20] . Recent evidence demonstrates that the chromosomal position of a gene , or its genomic context , affects both its mean expression level and expression noise [21]–[24] . One mechanism by which genomic context modulates gene expression is by specifying the dynamics of the local chromatin state , which can impact multiple neighboring genes [3] , [25] , [26] . Additionally , endogenous genes can sample different genomic environments through translocation and recombination , impacting diverse biological processes including species evolution , organism development , and cancer [27] , [28] . Human retroviruses , such as human immunodeficiency virus-1 ( HIV ) , also sample genomic environments through semi-random integration into the host genome , which in turn affects viral replication [29] . Thus , genomic context impacts cellular phenotypes and offers additional dimensions of selectable variation that shape the architecture and evolution of eukaryotic genomes , as well as the retroviruses that invade them . Stochastic gene expression phenotypes that are modulated by genomic context present new challenges for quantifying the genotype–phenotype relationship . In particular , understanding how genomic context and gene sequence cooperate to alter gene expression dynamics requires quantifying how the sequences of regulatory elements alter the distribution of expression phenotypes over the set of genomic environments sampled by a gene . Gene regulatory networks may further alter gene expression phenotypes by amplifying or minimizing noise in gene expression through positive and negative feedback . Thus , when a genetic mutation is linked to a change in the distribution of stochastic phenotypes over genomic contexts , a further challenge is to identify the underlying mechanism that drives this change . In this study , we identify promoter mutations that modulate context-dependent stochastic phenotypes in a lentiviral human immunodeficiency virus-1 ( HIV ) model system and investigate the mechanisms by which they impact viral gene expression . HIV exhibits a high degree of genetic variability due to its high replication rates [30] and the error-prone nature of reverse transcription [31] , [32] . Following semi-random integration into the genome of host CD4+ T cells [29] , HIV usually establishes a productive infection , but in rare cases can adopt a non-replicating but reversible latent phenotype , such as when an infected activated T cell transitions to a memory T cell [33] , [34] . Latently infected cells do not express virus and thus cannot be effectively targeted by current therapeutics [35]; however , latent HIV can reactivate after long delays , leading to renewed viral spread [36] . Consequently , latent infection represents the single greatest obstacle to fully eradicating HIV in patients [37] . Importantly , a number of studies have demonstrated that genomic context and non-genetic variability play important roles in determining the replication-versus-latency decision of integrated HIV within a cell [19] , [21] , [22] , [26] . Thus , HIV provides an ideal system for studying the interplay between gene sequence , genomic environment , and stochastic gene expression . The virally encoded transcriptional activator Tat plays an essential role in HIV expression dynamics and the replication-versus-latency decision . The nascent HIV transcript forms a RNA hairpin , termed the HIV transactivation response element ( TAR loop ) , that causes RNA polymerase II ( RNAPII ) to stall [38] . Tat binds to the TAR loop and in turn recruits the positive elongation factor b ( p-TEFb ) , which phosphorylates RNAPII to relieve the stall and complete a cycle of transcription [39] . Transcript processing and translation then results in production of viral proteins , including more Tat . Thus , Tat enhances HIV transcriptional efficiency in a strong positive-feedback loop [40] that is necessary for viral gene expression from proviruses that immediately initiate replication or from latent infections that reactivate [41] , [42] . We have previously demonstrated that an in vitro model of the HIV Tat positive feedback loop can generate a diverse range of stochastic phenotypes by sampling genomic contexts . These stochastic phenotypes include bimodal expression behaviors where non-expressing and highly expressing cells co-exist in a single clonal population [20] , [43] and random switching between these two expression states occurs with significant delays . Noise in basal viral gene expression in the absence of Tat varies systematically over genomic integrations [21] , [22] , and its amplification by Tat feedback provides a possible mechanism to explain the diverse phenotypes generated in the presence of Tat . We have hypothesized that stochastically-driven delays in activation for some viral integrations are an intrinsic property of Tat positive feedback , and that these delays may provide a sufficient time window to establish latent infections in vivo when coupled to host-cell dynamics such as the transition to a memory T cell [20] , [43] . Thus , HIV sequence mutations that affect the frequency of stochastic phenotypes in vitro may affect the frequency of latent infections in vivo . While isolated examples of promoter mutations that control context-dependent stochastic phenotypes have been investigated for HIV [43] , no study has yet systematically identified such mutations or analyzed the mechanisms by which the distribution of phenotypes is modulated . Here , we designed a forward genetic screen to select for HIV promoter mutations that increase the fraction of genomic integrations that result in stochastic gene expression phenotypes . Our screen identified important mutations in a number of core promoter regions , including Sp1 and TATA transcription factor binding sites . Through single-cell experiments , we confirmed that our strongest hits – point mutations in Sp1 site III and in the TATA box – increased the frequency of stochastic phenotypes several fold . We further demonstrated experimentally that the Sp1 mutation altered basal expression dynamics in the absence of Tat , and also impaired transactivated gene expression in the presence of Tat . Computational analysis demonstrated that the changes in basal expression observed for the Sp1 mutant could contribute significantly to the enrichment in stochastic phenotypes in the presence of impaired Tat feedback , if the mutation affected Tat-mediated amplification differentially across genomic contexts . Our analysis thus demonstrates a methodology for identifying genetic elements that affect the distribution of context-dependent stochastic phenotypes and the mechanisms by which they function . Our findings may also contribute to understanding how mutational selection could alter the frequency of latent HIV infection .
To quantitatively study stochastic gene expression of HIV infections as a function of genomic context , we adapted a full-length HIV NL4-3-based LTR lentiviral packaging platform [44] by introducing stop codons into all viral proteins except Tat and by replacing Nef with GFP ( sLTR-Tat-GFP; Figure 1A ) . This minimal viral system , referred to in this study as wild type ( WT ) , is similar to a model vector used previously in which Tat and GFP are expressed from a bicistronic lentiviral vector under control of the same LTR promoter [20] , [43] . However , the new sLTR-Tat-GFP vector more closely mimics HIV gene expression , with Tat produced as a splice product of two exons as in natural HIV infection . The leukemic Jurkat T cell line was infected with sLTR-Tat-GFP at a low multiplicity of infection ( MOI<0 . 1 ) , such that the majority of infected cells ( >95% ) contained a single integrated provirus . The infected , GFP+ cells were then isolated by fluorescence activated cell sorting ( FACS ) after stimulation with tumor necrosis factor-α ( TNFα ) and cultured for ten days so that the population relaxed to a steady-state GFP expression profile . The resulting polyclonal or “bulk-infected” cell population showed bimodal gene expression , which indicated the presence and absence of Tat positive feedback in different cellular infections ( Figure 1B ) , as observed with the previously studied bicistronic lentiviral vector [20] , [43] . Bimodal Tat–GFP expression in the bulk-infected population arises from a mixture of integration events that result in either high or low gene expression , as well as individual integrations that result in variable or stochastic gene expression . To separate these contributions to the overall bulk distribution , we sorted individual cells – each containing a single ( different ) genomic integration of the provirus – from low , mid , or high ranges of GFP expression ( Figure 1B ) . We then expanded these individual sorted cells to yield 125 single-integration clonal populations and subsequently quantified their GFP expression phenotypes by flow cytometry . Consistent with earlier studies [20] , [43] , a diverse spectrum of clonal GFP expression phenotypes was observed , including narrow single peaks of low or high GFP expression ( referred to here as Dim and Bright distributions , respectively ) , as well as wide and/or bimodal distributions ( Figure 1C ) . The wide/bimodal clonal distributions occurred with higher frequency within populations sorted from the mid-GFP range ( Figure S1 ) and included both cells that are Bright , representing Tat-transactivated expression that would support viral replication , and cells that are Dim , representing low levels of basal expression that may be related to viral latency . Analogously , earlier work showed that when Dim cells are sorted from the bulk multi-integration population , a fraction eventually activated and migrated into the Bright range , and vice-versa [20] , [22] , [43] . We collectively refer to these stochastic viral gene expression phenotypes as “Switching” and consider them to be a model for latent infections that can randomly “switch” from an inactive state to a productive state . Given HIV's rapid mutation rate [30]–[32] , an interesting question is how changes in the viral promoter could affect the relative frequency of different expression phenotypes over the set of genomic environments that are sampled through infection and viral integration , and in particular whether specific mutations could increase the frequency of Switching phenotypes . As a first step in addressing this question , we developed objective , feature-based clustering criteria to classify gene expression behavior for a clonal population as Switching , Dim , or Bright . In this classification , cut-off values were manually selected for nine GFP-distribution measures that reflect expression heterogeneity , such as bimodality , width , and skewness ( Table S1 and Figure S2 ) . Distributions with a value exceeding the cut-off for any one of these features were labeled as Switching ( details of methods described in Text S1 ) . By applying these criteria uniformly to our initial collection of single-integration clones ( Figure 1C ) , we estimated the fraction of integrations in our system that led to a Switching phenotype to be 8 . 2% ( Figure 1D ) . We developed an alternate estimate of the Switching fraction based on sampling single-integration clones sorted only from the mid-GFP range and extrapolating to the full population ( see Text S1 ) . This method resulted in a similar Switching fraction estimate of 8% ( Figure 1D ) , and was thus used in the remainder of our study for increased experimental efficiency . We next developed a stochastic model of HIV transcription and amplification by the Tat positive feedback loop to aid our intuition concerning the underlying gene expression dynamics that may account for the observed variation in HIV expression phenotypes ( Figure 2A ) . We previously built a model of basal LTR promoter-driven gene expression in the absence of Tat , which probabilistically described the processes of gene activation , transcription , and translation [22] . Our analysis suggested that basal transcription from the LTR occurs in short , infrequent bursts , and we found that the size of these transcriptional bursts strongly correlated with mean gene expression from different viral integration positions [22] . Here , we extended this basic model to include Tat expression from the LTR , and Tat positive feedback on transcription from the LTR , by assuming a Michaelis-Menten-like dependence of transcriptional burst size and burst frequency on Tat concentration ( full model description included in Text S1 ) . The assumption that Tat positive feedback enhances the frequency of transcriptional bursts from the LTR is consistent with observations that Tat interacts with transcription factors involved in gene activation [45] , [46] , and the assumption that Tat increases transcriptional burst size is based on observations that Tat enhances elongation by recruiting p-TEFb [39] . The model is specified by two basal transcription parameters , which set the average size and frequency of transcriptional bursts that occur in the absence of Tat , and three feedback parameters that describe transcriptional amplification in the presence of Tat . Two of these feedback parameters , which specify the average size and frequency of transcriptional bursts at saturating Tat concentrations ( full transactivation ) , were set to give approximately a 100-fold increase in transcription rate at saturating Tat concentrations [40] . The third feedback parameter , which specifies the Tat concentration at half maximal binding , was set to approximately the top of the mid range of our bulk expression distributions ( Figure 1B ) . The remaining model parameters ( including degradation and translation rates ) were set as in previous work [22] . The model , which was solved numerically for steady-state protein distributions , reproduced each of our major experimental expression phenotypes over different ranges of parameter values ( Dim , Bright , and Switching ( Figure 2B ) . We qualitatively analyzed the relationship between transcriptional dynamics and expression phenotype in our model by generating a series of phase diagrams . These phase diagrams fix the Tat feedback parameters in our model as described above , and then systematically scan over basal transcription parameters , which are known to vary over genomic integrations [21] , [22] . By applying our experimental criteria for Dim , Bright , and Switching phenotypes to our simulated distributions , we drew boundaries separating combinations of basal transcription parameters that lead to distinct expression phenotypes in our model ( Figure 2C ) . Interestingly , near the range of model parameters that generate Switching phenotypes , small changes in basal transcription that occur in the absence of Tat result in large changes in phenotype when amplified by Tat feedback ( Figure 2B ) . Additionally , we found that Switching phenotypes exhibit delayed activation of gene expression . That is , if a simulated population of cells with model parameters corresponding to a Switching phenotype is initialized in the Dim state , a time-scale of one to many weeks is required for half of the population to cross a threshold of gene expression intermediate between Dim and Bright states ( Figure 2B ) . This is in contrast to a Bright steady-state phenotype initialized in the Dim state , which will cross an intermediate expression threshold on a time scale of days ( corresponding to the time scale of protein dilution in our cells ) . The delayed activation observed for the Switching phenotype is approximately the time scale over which an activated CD4+ T cell may transition to a memory state , and memory T cells are a primary reservoir of latent HIV infection in vivo [33] , [34] . Thus , the delayed transcriptional activation exhibited by a Switching phenotype could substantially increase the opportunity for the memory state transition to occur in an infected T cell before viral production , and may therefore increase the probability of a latent infection . The general relationship between Switching phenotypes and delayed activation is highlighted by superimposing a measure of distribution activation time on the phenotypic information in our phase diagrams ( Figure 2C ) . Delayed activation results when transactivation depends on the probabilistic ( infrequent ) occurrence of multiple transcriptional bursts that are larger and/or more closely spaced than occur on average . In our model , such behavior occurs at intermediate values of basal transcriptional burst size and frequency , which are typically the same values that specify Switching phenotypes ( additional discussion in Text S1 ) . Our model thus supports the hypothesis that Switching phenotypes also exhibit delayed activation , which may underlie the establishment of latent HIV infections [20] , [22] , [43] . Finally , we note that Switching phenotypes also exhibit delayed deactivation of gene expression as compared to Dim clones when initiated in a Bright state . Although delayed deactivation is not relevant to the establishment of latent infections in vivo ( due to the fact that viral replication would kill the host cell and block any possible memory state transition before deactivation could occur ) , it is possible to observe this behavior in our in vitro model . Thus , we hypothesized that probabilistic delays in both activation and deactivation can be used to select for Switching phenotypes in our in vitro system . We exploited the delayed activation/deactivation of gene expression associated with Switching phenotypes to design a forward genetic screen to identify LTR promoter mutations that increase the prevalence of Switching phenotypes , and which could thus potentially influence the fraction of latent infections . We prepared a library of HIV-1 vectors in which the WT LTR promoter was subjected to random point mutations via error-prone PCR ( Figure 3A ) [47] . The ∼105 member library had an average mutation rate of 0 . 6% , such that each position of the 634 base-pair promoter was mutated hundreds of times across the library . We packaged the library into our model vector , infected Jurkat cells , and isolated cell populations containing single viral integrations as described for the WT vector above . The resulting bulk population of singly infected cells , which was heterogeneous in both LTR sequence and viral integration position , was subjected to two alternate phenotypic screens . First , we implemented an ‘activation’ screen , in which infected cells with low GFP expression ( low GFP gate ) were isolated by FACS and allowed to grow for 5 days , at which point cells that had switched to high GFP expression ( high GFP gate ) were selected again by FACS . Second , a ‘deactivation’ screen reversed the order , selecting for high GFP expression first and low second ( Figure 3A ) . We refer to the fraction of cells selected in these screens as the activating and deactivating fraction , respectively . To confirm that our activation screen effectively selected for clones with a Switching phenotype , we applied the activation screen to the WT virus and randomly selected a sample of single cells from the activating fraction , which were then expanded to clonal populations for analysis . Remarkably , nearly 54% of these clones ( 22 out of 42 ) showed Switching phenotypes , as compared to only 8% from the original population and 19% from the mid-sorted population ( Figure S1 ) , confirming the effectiveness of the screen . We thus implemented a larger scale analysis to identify viral promoter mutations that favor Switching phenotypes . Specifically , we performed multiple rounds of infection and FACS-based screening as described above to average the behavior of promoter sequences across different integration positions and thus identify genotypes that give rise to a higher fraction of Switching phenotypes across genomic contexts . After each round of infection , we recovered the viral LTRs from the genomic DNA of the selected populations ( by PCR ) , re-cloned them into the sLTR vector , repackaged virus to produce a new library of selected promoters , and infected a new population of Jurkat cells ( Figure 3A ) . After four rounds of selection , the fraction of activating cells increased 6-fold compared to the original library ( p<0 . 001 , t-test on triplicate measurements ) and 2-fold compared to the WT promoter ( p<0 . 01; Figure 3B ) . The fraction of deactivating cells increased by a factor of 1 . 7 compared to the original library ( p<0 . 04 ) and by a factor of 3 relative to WT ( p<0 . 002; Figure 3C ) . Interestingly , the median GFP expression of the Tat-transactivated population ( Bright peak in the bulk GFP histogram ) was significantly lower for the unselected library than for WT , and it continued to decrease with each round of selection in both screens ( Figure 3D–E ) . Importantly , the bulk gene expression distributions of the selected promoters also displayed an increased weight in the mid range of GFP expression ( Figure 3F–G ) , which we had found to be enriched in integrations that demonstrate a Switching phenotype for the WT promoter . Altogether , these results indicate that our dynamic screens for activation and deactivation effectively selected for mutations that increased the fractions of activating and deactivating cells , which is a hallmark of the Switching phenotype . To analyze the LTR promoter mutations that were enriched by the activation and deactivation screens , approximately 90 clones were sequenced from each selected library and compared to a control set of promoters from the unselected library . The average mutation frequency per position in the selected libraries was approximately 1 . 1% ( as compared to 0 . 6% for the unselected library ) , but the distribution of mutation frequencies was long-tailed , with some positions mutated in as many as 20% of the promoters for a given screen ( Figure 4A ) . We first analyzed how mutations were distributed across the LTR for the combined screens by comparing the mutation frequency for each regulatory region of the LTR with the average mutation frequency over the whole promoter [48] ( Figure 4A ) . For both screens , mutations were most significantly enriched in the 78 base-pair core promoter region ( p<0 . 0001 , Chi-squared test ) , which includes transcription factor binding sites required for efficient promoter activation [48] . In contrast , mutation rates were not increased above those in the initial library in the enhancer region , the U5 region downstream of TAR , and in the TAR region itself , possibly reflecting the essential role of the TAR loop secondary structure to enable efficient gene expression [49] . The remaining regions displayed increased mutation frequencies that did not differ significantly from the average increase across the entire promoter for both selected libraries . We next compared the mutation frequency at each position in the core promoter to the mutation frequency for the same base type in the unselected library ( Figure 4B ) . We identified two positions in Sp1 site III , one position in Sp1 site II , and two positions in the TATA box with significant mutation rates in both screens ( Table S2 ) , with additional Sp1 and TATA positions significantly mutated in one of the two screens . The top hit was in Sp1 site III ( position 4 of the 10 bp site , p<0 . 0001 ) . Selection for this mutation is consistent with our previous results demonstrating that simultaneous mutation of positions 3 and 4 in Sp1 site III , which had been shown to eliminate binding of Sp1 [50] , also increased delayed activation and deactivation in infected Jurkat cell populations [43] . The next strongest hit was in the TATA box ( position 2 of the 8 bp site , p = 0 . 0005 ) . The A to G mutation observed most frequently in our selected libraries has been previously shown to reduce the affinity of the TATA binding protein ( TBP ) for the TATA box [51] . Notably , mutations at positions 3 and 4 of the TATA box , which are considered critical for TBP binding and thus TATA function [51] , [52] , were not enriched in either screen . Altogether , for the activation screen we found that 40% of the sampled sequences had mutations in Sp1 site III , and 25% had TATA mutations; for the deactivation screen , 20% had mutations in Sp1 site III , and 20% had TATA mutations ( Figure 4C ) . All of these mutation frequencies were well above those for the same regions in the unselected library . Together , these results suggest the importance of Sp1 site III ( and to a lesser extent the TATA box ) in controlling stochastic gene expression and Switching fractions . To directly analyze how the point mutations identified in our screen affect gene expression , we generated vectors with a single point mutation at position 4 of the Sp1 site III ( Sp1 mutant ) or at position 2 of the TATA box ( TATA mutant ) ( Table S3 ) , and infected Jurkat T cells as previously described . The TATA mutation increased both the activating and deactivating fractions of the infected population by approximately 2 . 5-fold relative to WT ( p<0 . 01; Figure 5A–B ) , and the Sp1 mutation increased the activating fraction 1 . 5-fold ( p<0 . 03; Figure 5A ) and the deactivating fraction almost 7-fold relative to WT ( Figure 5B , p<0 . 001 ) . Both point mutations also significantly decreased Tat-mediated gene expression and increased expression in the mid-range of fluorescence ( Figure 5C ) , mirroring the bulk expression phenotype of the full library after selection , and consistent with previous studies [43] , [53] , [54] . We next quantified Switching fractions for both mutants by sorting approximately 80 single-integration clones from the mid-range of GFP in the bulk populations as previously described for the WT virus ( Figure 1 ) . The Switching fractions increased from 8% for the WT virus to 25% for the TATA mutant and 46% for the Sp1 mutant ( Figure 5D ) . These results confirm that increased activation and deactivation in the bulk infection for these mutants reflect an increased frequency of single-integration clonal Switching phenotypes ( p<0 . 01 , bootstrap method ) . We next considered how promoter mutations might alter transcriptional dynamics to increase the fraction of infections that generate Switching phenotypes . For this analysis , we chose to focus on the Sp1 point mutation , because this point mutation exists in naturally occurring HIV isolates , while the TATA mutation was not found ( as determined by searching the Los Alamos HIV sequence database , http://www . hiv . lanl . gov ) . Furthermore , our previous work also demonstrated a role for Sp1 site III in regulating Switching phenotypes [43] . Our earlier work demonstrated that basal transcription ( i . e . in the absence of Tat ) varies significantly with integration position of the LTR [22] . Therefore , we hypothesized that Sp1 may modulate phenotypic distributions by directly affecting basal transcription . To test this hypothesis , we introduced stop codons into the first Tat exon of the lentiviral vector backbones of the WT and the Sp1 mutant promoter and infected Jurkats as described above ( Figure 6A ) . Bulk-infection expression distributions for both Tat-null vectors demonstrated substantial overlap with autofluorescence controls , but with a strong right skew towards higher fluorescence . Notably , a small but significant decrease in mean GFP expression was observed for the Sp1 mutant promoter compared to WT ( p<0 . 05 ) , consistent with previous studies [53] , [54] . Additionally , clonal cell populations expanded from each bulk population had monomodal , wide , right-skewed distributions ( Figure S3 ) and displayed high levels of noise across clonal expression means ( Figure 6B ) , consistent with previous results for the WT LTR promoter [22] . To infer the underlying transcriptional dynamics of our Tat-null clones , we systematically fit their GFP distributions using our model ( Figure 2A with transactivation removed ) , following our earlier analysis of WT basal expression dynamics [22] . The sets of clonal WT and Sp1 distributions were all best accounted for by a bursting dynamic , whereby short infrequent transcriptional bursts generate large basal expression heterogeneities ( see Text S1 and [22] for further discussion ) . The basal transcription dynamics for each clonal population were fully quantified by a best-fit basal transcriptional burst size and burst frequency . Transcriptional burst sizes were found to vary from a few to tens of transcripts , and to be strongly positively correlated with mean expression level across different integration positions for both the mutant and for the WT vector ( Figure 6C ) . In contrast , typical transcriptional burst frequencies were on the order of a few events per cell division time , and demonstrated little correlation with mean gene expression levels over integration positions ( Figure 6D ) . These findings are consistent with our earlier analysis of the WT promoter [22] . Although the Sp1 mutant and WT promoters share the same qualitative basal expression dynamics , regression analysis revealed that the Sp1 mutant demonstrated an increased positive correlation between basal burst frequency and clonal expression mean , with burst frequencies decreased for Dim clones ( Figure 6D; p = 0 . 04 ) . Thus , the selected Sp1 mutation does not change the qualitative bursting mode of transcription from the HIV LTR , but it does appear to modestly alter how the dynamics vary quantitatively across integration positions . We returned to our model to explore if the small changes in basal transcriptional dynamics quantified experimentally with our Tat-null vector could contribute significantly to the increased Switching fraction observed for the Sp1 mutant in the presence of Tat ( Figure 5D ) . The phase diagrams developed for the WT promoter ( Figure 2C ) specify the predicted expression phenotype for every combination of basal transcriptional burst size and burst frequency parameters for fixed Tat feedback . Thus , model phase diagrams can be used to predict the Switching fraction that would result from a given probability density with which the virus samples basal transcriptional parameters through its sampling genomic locations via infection and integration , under the assumption of fixed Tat feedback . We used our experimental data to estimate the probability density with which the WT and Sp1 mutant promoters sampled combinations of basal transcription parameters ( see Text S1 for details ) , and then calculated model-predicted Switching fractions by integrating this sampling density over the Switching region of the phase diagram ( Figure 7A ) . We found that the changes in basal transcriptional dynamics observed for the Sp1 mutant – particularly the increased sampling of lower transcriptional burst frequencies , which specify noisier basal transcription – indeed resulted in higher model-predicted Switching fractions compared to WT for all sets of feedback parameters analyzed . In particular , for a set of feedback parameters that specify a model-predicted Switching fraction of 12% for the WT basal parameter sampling density , the model predicted a Switching fraction of 22% for the Sp1 mutant sampling density ( Figure 7B ) . Thus , we conclude that changes in Sp1 basal transcription dynamics can result in a substantial increase in the fraction of genomic integrations that lead to a Switching phenotype in the presence of Tat feedback . In addition to altering basal expression , mutations in Sp1 site III weaken Tat positive feedback , as demonstrated in our experiments ( Figure 5C ) and in previous work [53]; however our model had not yet accounted for this observation . We therefore explored if weakening Tat positive feedback in the model would maintain the predicted Switching fraction enrichment that arises from altered basal transcription , or even enhance it to more fully account for the nearly 6-fold enrichment observed in our experiments . In contrast to these expectations , we found that decreasing Tat-driven fold-amplification of basal transcription in the model typically decreased predicted Switching fractions ( Figure 7B ) , a result which can be explained by our model phase diagrams ( Figure 7A ) . Notably , weakening feedback shifts phenotypic boundaries to the right ( towards larger basal transcriptional burst sizes ) , transforming Bright integrations to Switching , and Switching to Dim . The resulting Switching region typically enclosed a smaller fraction of the viral basal parameter sampling density , which is highly right skewed and heavily weighted at lower basal transcriptional burst sizes . Thus , our analysis suggests that the Sp1 site mutation specifies a more complex perturbation of the Tat positive feedback loop that differentially affects Bright and Dim integrations , rather than one that uniformly attenuates expression amplification over genomic integrations . A biological mechanism by which the Sp1 site mutation could differentially affect Bright and Dim integrations is by impairing transcriptional reinitiation . In the bursting model of transcription , each gene activation event can drive multiple cycles of transcription , requiring multiple rounds of RNAPII binding and transcription-complex formation ( i . e . reinitiation ) . In the absence of Tat , the rate-limiting step in HIV-LTR transcription is RNAPII stalling at the TAR hairpin that forms after transcriptional initiation [38] . Therefore , moderate impairment of the reinitiation rate via mutation would be masked during basal transcription , or for integrations that inefficiently activate Tat feedback . However , at higher concentrations of Tat , when the TAR-loop stall is no longer rate limiting , impaired reinitiation would significantly attenuate full Tat transactivation , and the effect would be more pronounced for Brighter genomic integrations . Because Sp1 and p-TEFb interact in vivo to activate HIV transcription [46] , [55] , [56] , a mutation in the Sp1 site could plausibly alter transcriptional reinitiation if it disrupted recruitment of p-TEFb . To investigate this possibility , we extended our model to include a ‘reinitiation’ step between each transcript production event ( rescaled model parameters included in Figure 7 legend and full model description and equations included in Text S1 ) . The effective transcript production rate in this extended model depends on both an elongation rate , which varies over genomic integrations , and a reinitiation rate , which is fixed ( but may be altered through mutation ) . The elongation rate specifies the variation of the basal and transactivated transcription rates over genomic integrations , while the reinitiation rate specifies the maximal value at which the transcription rate saturates as a function of elongation rate . In this extended model , we found that a moderate decrease in the transcriptional reinitiation rate had little effect on the phenotypic boundaries of our phase diagrams ( Figure 7B ) , but significantly weakened Tat-transactivated expression from Bright integrations ( Figure 7C ) , consistent with our experimental observations ( Figure 5C ) . As a result , predicted Switching fractions were preserved , though they were not further enhanced to the level observed experimentally . Thus , moderate impairment of transcriptional reinitiation could account for the observed attenuation in Tat-mediated gene expression ( Figure 7B ) , while preserving ( but not increasing ) the Switching fraction enhancement that was predicted for the observed changes in Sp1 mutant sampling of basal transcription parameters .
Our study was enabled by the development of a computational model that described how promoter-driven expression fluctuations are propagated via Tat positive feedback to generate the wide range of expression phenotypes in our system . We used this model to investigate features of Tat feedback that generate stochastic phenotypes , to formulate hypotheses concerning the mechanisms by which these features may be varied through mutation , and to study the implications and consistency of these hypotheses with our experimental data . The Tat transactivation circuit – an essential and conserved feature of the HIV virus across clades – is characterized in our model by positive feedback loops that enhance both the size and frequency of transcriptional bursts . HIV gene expression phenotypes range from Dim to Bright as the kinetic parameters of the circuit are varied , with intermediate parameter values generating the stochastic Switching phenotypes that our screen was designed to select . These Switching phenotypes , which we have suggested may serve as a model for latent infection [20] , [22] , [43] , are characterized by Tat-amplified transcriptional fluctuations that drive stochastic switching between quiescent and highly expressing states ( Figure 2 ) . Importantly , all of the transcriptional and regulatory processes described in our model – and their underlying kinetic parameters – can be modulated by genomic environment . Thus , a viral sampling of genomic environments that range from repressive to permissive can tune the steady-state behavior of Tat positive feedback circuit to generate a distribution of expression phenotypes that span from Dim to Bright , with intermediate integrations generating Switching phenotypes [41] . In this way , the possibility of stochastically-generated latent phenotypes at a subset of viral integrations may be an intrinsic feature of the Tat circuit and its sampling of host-cell genomic environments , and the virus may tune the fraction of integrations that specify this phenotype through mutation . Guided by our model analysis both here and in previous work [22] , we hypothesized that the Sp1 mutation may alter the prevalence of Switching phenotypes by modulating basal transcription dynamics . Although the underlying basal bursting dynamic of the WT promoter was essentially preserved in the Sp1 mutant ( Figure 6 ) , we were able to detect modest quantitative differences in the sampling of basal expression dynamics over integration positions . Our computational analysis confirmed that these small differences in basal expression for the Sp1 mutant could be amplified in the presence of Tat feedback to produce substantial increases in the Switching fraction ( Figure 7B ) . The selected Sp1 mutant also demonstrated weaker Tat-transactivated expression , and we further used our model to investigate how this could affect the Switching fraction . Our model analysis demonstrated that weakening Tat feedback proportionately for all integrations would decrease , rather than increase , Switching fractions ( Figure 7B ) . Thus , accounting for an increased Switching fraction in the presence of weaker Tat feedback required a mechanism by which the selected mutation could differentially affect basal and transactivated expression , which we suggested could be accomplished through impaired transcriptional reinitiation . A revised computational model that included impaired transcriptional reinitiation could thus account qualitatively for both trends observed experimentally for the Sp1 mutant: an enhanced Switching fraction accompanied by attenuated Tat-transactivated expression ( Figure 7B–C ) . However , we note that our model still does not quantitatively account for the full increase in Switching fraction observed experimentally for the Sp1 mutant ( Figure 5D ) . A complete explanation might thus require identification of additional mechanisms that differentially affect Tat transactivation across genomic integrations and a more detailed characterization of how the selected mutations perturb the transcription parameters sampled by the virus over genomic integrations . Multiple studies have demonstrated that mutations in the Sp1 sites of the HIV LTR can significantly reduce HIV Tat-mediated transactivation , while minimally affecting basal expression ( for those cases in which it was measured ) [53] , [55] , [58] , [59] . Although the detailed mechanisms by which Sp1 regulates HIV expression remain unknown , there is evidence that Sp1 recruits P-TEFb in vivo to release the stalled RNAPII from the promoter proximal region and activate transcriptional elongation of HIV [46] , [55] , [56] . To our knowledge , a role for Sp1 in transcriptional reinitiation has not been directly tested . However , if Sp1 participates in recruitment of P-TEFb , then lower affinity Sp1 binding ( caused by promoter mutation ) may destabilize the P-TEFb complex in the promoter active state and thus lower the rate of transcriptional reinitiation ( κr in our model ) . Interestingly , TATA mutations in the HIV LTR also substantially reduce Tat-mediated transactivation without affecting mean basal expression from the HIV LTR [53] , [54] , [60] , [61] , similar to observations by others and us for Sp1 mutation . Although we did not explore the mechanisms underlying mutation of the TATA box , an increase in the half-time of transcriptional reinitiation ( 1/κr in our model ) has been measured directly for a mutation at site 2 of the TATA box [62] . Furthermore , TATA box mutations that decreased reinitiation also correlated with decreased stability of the TBP:TFIIA ( general transcription factor ) complex on the DNA , suggesting that retention of general transcription factors at the promoter is a primary determinant of the reinitiation rate [63] . Our results motivate a future experimental study that directly measures if reduced transcriptional reinitiation provides a mechanistic explanation for the differential effect of Sp1 and TATA box mutations on basal and Tat-transactivated HIV transcription , as observed here and in many previous studies [53] , [55] , [58] , [59] . In vivo , infected CD4+ T cells that have transitioned to a memory state form a primary reservoir of latent infection [33] , [34] . However , HIV does not efficiently establish infection in resting memory CD4+ T cells [64] , [65] , and activated CD4+ T cells typically die within days after infection [30] . Therefore , we hypothesize that transcriptional delays , such as those associated the Switching phenotype in our in vitro system and that occur on a similar time scale to the memory-state transition , could delay viral production and thus increase the time window during which the memory-state transition could occur post-infection . Thus , viral mutations such as the Sp1 and TATA mutations identified in our study , which result in an increased fraction of viral integrations demonstrating transcriptional delays , could lead to an increase in the fraction of memory T cells that harbor a latent infection . If this in vitro model of latency has in vivo implications , then our results suggest that there may be enrichment for viruses with an Sp1 and/or TATA box mutation in the latent reservoir . Although we are unaware of any direct evidence of enrichment for either Sp1 or TATA box mutations in the latent pool , there is evidence that viruses with an Sp1 site III mutation are enriched during the course of disease progression [66] and that viruses with impaired Tat activity are enriched in latent reservoirs [67] . These studies are suggestive that some viral mutations , particularly ones affecting Tat transactivation as demonstrated in our study , may create favorable conditions for establishing latent infections . Interestingly , these studies suggested that lower transcriptional activity may underlie the propensity of these viruses to establish a latent infection , but our results suggest it is instead the increased probability for transcriptional delay that potentiates latent infection . A related and testable hypothesis is that the three HIV subtypes ( D , F and H ) with mutations in Sp1 site III may demonstrate an increased propensity for latency and thus give rise to larger latent reservoirs relative to subtype B infection . To our knowledge , there is no study that has examined the relative sizes of the latent viral reservoirs for different HIV subtypes , and therefore this may be an important translational study that is motivated by our work . In conclusion , our study provides an integrated experimental and computational framework for identifying genetic sequences that alter the distribution of stochastic expression phenotypes over genomic locations and for characterizing their mechanisms of regulation . Our results also may yield further insights into the mechanisms by which HIV sequence evolution can alter the propensity for latent infections .
HEK293T cells ( ATCC ) were cultured in IMDM ( Mediatech ) and Jurkat clone E6 cells ( ATCC ) were cultured in RPMI ( Mediatech ) . All media was supplemented with 10% FBS ( Gibco ) and 100 U/ml penicillin+100 mg/ml streptomycin ( Gibco ) . Jurkat cell concentrations were maintained between 2×105 and 2×106 cells/ml in 5% CO2 at 37°C . We modified a full-length single-LTR packaging platform described previously in which HIV Nef was replaced with GFP [44] . Multiple stop codons were introduced into all viral proteins except Tat ( psLTR-Tat-GFP; Table S4 ) using Quickchange site-directed mutagenesis ( Stratagene ) . To generate Tat-null sequences , additional stop codons were introduced into the first exon of Tat ( psLTR-TatKO-GFP ) . The LTR promoter library was amplified in an error-prone PCR reaction described previously [47] using Taq DNA polymerase with 2% MnCl2 . The resulting promoter library was cloned into the psLTR-Tat-GFP by restriction digest with PmeI and KasI . Following each round of selection , the genomic DNA from the selected cells was isolated using a QiaAMP DNA Micro Kit ( Qiagen ) and the LTR promoters of the integrated proviruses were amplified with primers that retained the PmeI and KasI restriction digest sites for cloning . Single point mutations in the LTR were introduced with Quickchange site-directed mutagenesis ( see Table S3 for sequences ) and each mutant LTR was sequenced and subcloned back into the parental plasmid to avoid unintended mutations . All psLTR-Tat-GFP and psLTR-TatKO-GFP plasmids were packaged and harvested in HEK 293T cells with helper plasmids ( pcDNA3 IVS VSV-G , pMDLg/pRRE , pRSV Rev , and pCLPIT-tat mCherry ) as previously described [20] , [68] . Harvested lentivirus was concentrated by ultracentrifugation to yield between 107 and 108 infectious units/ml . To titer , Jurkat cells were infected with a range of vector concentrations and six days post infection , gene expression of infected cells was transactivated by stimulation with 20 ng/ml PMA ( Sigma ) and 400 nM TSA ( Sigma ) . After stimulation for 18–24 hours , GFP expression was measured by flow cytometry , and titering curves were constructed by determining the percentages of cells that exhibited GFP fluorescence greater than background levels . Jurkat cells were infected with the sLTR-Tat-GFP virus at an MOI of ≤0 . 1 and cultured for 7–10 days . Cells were stimulated with 20 ng/ml TNF-α ( Peprotech ) for 18–24 hours and GFP+ cells were sorted on a MoFlo Cell Sorter ( Cytomation ) . Sorted cells were cultured for 10 days . For the activation screen , cells were sorted from the off peak ( bottom third of the full range of GFP expression ) , cultured for 5 days , and then selected as positive for enrichment if the cells activated above the mid-point of the expression range . For the inactivation screen , cells were sorted from the bright peak ( top third of the full range of GFP expression ) , cultured for 5 days , and then selected as positive for enrichment if the cells inactivated below the mid-point of the expression range . Flow cytometry data analysis was performed with FlowJo ( Tree Star , Inc . ) . For LTR-Tat-GFP infections , single cells were selected from the region of interest ( bottom third of the expression distribution for off cells , mid third of the expression distribution for bimodal cells , and top third of the expression distribution for bright cells ) . For the LTR-Tat-null vector , single cells were selected from either the top 10% or 18% of the GFP expression distribution and sorted into each well of a 96-well plate on a MoFlo Cell Sorter ( Cytomation ) . Clonal cells were cultured for 2–3 weeks and then analyzed on an FC500 flow cytometer ( Becton Dickenson ) . Fluorescence histograms for single-integration clonal sLTR-Tat-GFP infections were labeled as Switching if they exceeded specified cut-offs in any of the following distribution features: inter-quartile range , cube root of 3rd central moment , peak separation and dip for bimodal distributions , and the product of distribution weight in approximately the lower third and upper half of our cytometer log fluorescence range . Feature cut-offs were specified by visualizing the full set of clonal distributions using k-means clustering based on 8 distribution features normalized by inter-quartile range ( those mentioned , and mean log fluorescence , distribution weight in the lower 3rd of the bulk fluorescence range , and distribution 4th central moment ) using 20 clusters and a Euclidean distance , implemented in Matlab ( The Mathworks ) . Sorting clusters separately by each feature centroid allowed identification by eye of features and cut-off values beyond which all distributions could be labeled as Switching . This approach extended our by-eye intuition from distributions whose phenotype could be unambiguously scored by eye to those whose phenotype was ambiguous ( see Text S1 for further details ) . Key results , such as Switching fraction enrichment for our analyzed mutants , were robust to variation of feature cut-off values . Switching fractions , over the full set of genomic integrations , were estimated from mid-sorted sub-samples , via an application of Bayes theorem:where S is the event that an infected cell contains a Switching integration and M is the event that the cellular fluorescence is in the range of the sorting gate ( i . e . mid range ) . The conditional probability , , was estimated as the fraction of clones from a given mid sort that were labeled as Switching ( where is the total number of clones analyzed from the mid sort and is the number that were labeled as Switching ) . The probability that a cell expresses fluorescence in the range of the sort , , was estimated by the distribution weight of the bulk multi-integration population in the sort range . , the distribution weight in the sort region for the full population of Switching integrations , was estimated from our mid-sorted set of Switching clones as:where the are individual distribution weights of the mid-sorted Switching clones in the sort region . Uncertainties in Switching-fraction estimation were calculated based on a bootstrap approach [69] . Further details are provided in Text S1 . Our model of the transactivation circuit considers each reaction as a Markov process , proceeding with fixed probability per unit time ( full model details in Text S1 ) . For any fixed set of parameter values , the model was solved to obtain predicted steady-state protein distributions across a clonal population of cells by approximating and numerically integrating the master equation for the system [70] in time until a stationary distribution was achieved . Protein numbers were convert to cytometer RFU by scaling , and distributions were convolved with a measured autofluorescence profile for comparison with experimental distributions , following [22] . Tat-null distributions were fit to the transactivation model with feedback from Tat removed based on the first 6 distribution moments ( see Supplemental text for further details ) . Transcriptional bursting was assumed , so that transcriptional burst size ( ) and burst frequency ( ) were the only model fit parameters , with the remaining model parameters calibrated following [22] . The assumption of transcriptional bursting was checked by systematically varying the active-state duration ( ) and refitting the model at each value . Consistent with [22] , the best fits were always found in the transcriptional bursting regime ( ) . All analysis was done using in-house code written in Matlab ( The Mathworks ) . Statistical significance of differences between means in triplicate experiments was assessed using a 2-sided t-test . Pearson Chi-squared statistics were calculated for the appropriate contingency tables to assess differences in mutation rates between libraries marginally and by regulatory region , and at individual positions along the promoter , after controlling for base type in the WT ( parent ) sequence . All quoted raw p-values for post-hoc analysis remain significant at the level for Type I error after Bonferroni correction , and corresponding global tests were always significant at least at this level . Equality of regression coefficients was assessed by partial F-test , and differences between individual regression parameters were assessed by t-test in post-hoc analysis . Confidence intervals for experimental Switching fraction estimates , and p-values for their differences , were estimated using a bootstrap procedure . Contingency table analysis was conducted using SAS/STAT software version 9 . 1 for Windows , Copyright 2012 SAS Institute Inc . All other computational analysis was performed using Matlab ( The Mathworks ) .
|
The sequence of a gene within a cellular genome does not uniquely determine its expression level , even for a single type of cell under fixed conditions . Numerous other factors , including gene location on the chromosome and random gene-expression “noise , ” can alter expression patterns and cause differences between otherwise identical cells . This poses new challenges for characterizing the genotype–phenotype relationship . Infection by the human immunodeficiency virus-1 ( HIV-1 ) provides a biomedically important example in which transcriptional noise and viral genomic location impact the decision between viral replication and latency , a quiescent but reversible state that cannot be eliminated by anti-viral therapies . Here , we designed a forward genetic screen to systematically identify mutations in the HIV promoter that alter the fraction of genomic integrations that specify noisy/reactivating expression phenotypes . The mechanisms by which the selected mutations specify the observed phenotypic enrichments are investigated through a combination of single-cell experiments and computational modeling . Our study provides a framework for identifying genetic sequences that alter the distribution of stochastic expression phenotypes over genomic locations and for characterizing their mechanisms of regulation . Our results also may yield further insights into the mechanisms by which HIV sequence evolution can alter the propensity for latent infections .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"immunodeficiency",
"viruses",
"biochemical",
"simulations",
"viral",
"latency",
"systems",
"biology",
"genetic",
"screens",
"virology",
"gene",
"expression",
"genetics",
"biology",
"computational",
"biology",
"microbiology",
"viral",
"replication",
"gene",
"networks"
] |
2013
|
Genetic Selection for Context-Dependent Stochastic Phenotypes: Sp1 and TATA Mutations Increase Phenotypic Noise in HIV-1 Gene Expression
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The bacteriophage ΦCD27 is capable of lysing Clostridium difficile , a pathogenic bacterium that is a major cause for nosocomial infection . A recombinant CD27L endolysin lyses C . difficile in vitro , and represents a promising alternative as a bactericide . To better understand the lysis mechanism , we have determined the crystal structure of an autoproteolytic fragment of the CD27L endolysin . The structure covers the C-terminal domain of the endolysin , and represents a novel fold that is identified in a number of lysins that target Clostridia bacteria . The structure indicates endolysin cleavage occurs at the stem of the linker connecting the catalytic domain with the C-terminal domain . We also solved the crystal structure of the C-terminal domain of a slow cleaving mutant of the CTP1L endolysin that targets C . tyrobutyricum . Two distinct dimerization modes are observed in the crystal structures for both endolysins , despite a sequence identity of only 22% between the domains . The dimers are validated to be present for the full length protein in solution by right angle light scattering , small angle X-ray scattering and cross-linking experiments using the cross-linking amino acid p-benzoyl-L-phenylalanine ( pBpa ) . Mutagenesis on residues contributing to the dimer interfaces indicates that there is a link between the dimerization modes and the autocleavage mechanism . We show that for the CTP1L endolysin , there is a reduction in lysis efficiency that is proportional to the cleavage efficiency . We propose a model for endolysin triggering , where the extended dimer presents the inactive state , and a switch to the side-by-side dimer triggers the cleavage of the C-terminal domain . This leads to the release of the catalytic portion of the endolysin , enabling the efficient digestion of the bacterial cell wall .
The increasing emergence of antibiotic resistance has led to a renaissance in the use of bacteriophage therapy as an alternative to eradicate pathogenic bacteria [1] . These bacterial viruses are potentially effective bactericides , with the additional advantage that they only affect a small portion of the human microbiome , in contrast to the broad spectrum antibiotics in use [2] , [3] . Many antibiotics have an effect on a large portion of the microbiome , leading to a shift in bacterial populations after treatment . A striking example is the emergence of Clostridium difficile as a causative agent of antibiotic-associated diarrhea . C . difficile is resistant to many of the antibiotics used in hospitals , and it colonizes the gut after antibiotic treatment [4] . In search of an alternative treatment , a bacteriophage named ΦCD27 was isolated from a strain of C . difficile [5] . The genome of the ΦCD27 phage revealed the presence of a canonical holin/endolysin system . Endolysins are produced by many double stranded DNA bacteriophages to effect the release of new virions from an infected cell by degrading the bacterial cell wall [6] . The recombinant endolysin CD27L was shown to lyse C . difficile in vitro [5] . We have also shown that the N-terminal domain of CD27L consisting of a zinc dependent N-acetylmuramoyl-L-alanine amidase alone is effective in lysis , and that the host range of the endolysin can be affected by a mutation in the substrate binding pocket [7] . Bacteriophages co-evolve with their bacterial hosts , and the continuous adaptation of the phage may limit its lethality . Many bacteriophages isolated from the host environment are therefore not efficient in the rapid eradication of pathogenic hosts , as is the case with ΦCD27 . The potential to engineer more potent bacteriophages requires knowledge of the most important components of the lysis machinery [8] . Cell lysis is tightly regulated by the phage which only triggers cell lysis once it has finished the production of new viral particles inside the cell [9] . The endolysin is sequestered in the cytoplasm until it can penetrate the peptidoglycan layer following the formation of lesions in the cell membrane that are created by holin , another phage encoded protein [10] . Endolysins typically consist of a peptidoglycan hydrolase domain and a C-terminal domain that is often termed as a cell wall binding domain . The efficient use of endolysins as bactericides is limited by a poor understanding in most systems of the mechanisms that relate catalytic activity to the role of the C-terminal domain [8] . Many recombinantly produced endolysins can lyse a population of bacteria efficiently only after the protein has been incubated or converted with cell wall material from the host [11] , [12] . For some endolysins , the catalytic domain expressed in isolation is more effective than the full-length protein [7] , [13] , and for other endolysins , the catalytic domain alone shows reduced or no lytic activity at all [12] , [14] . For a pinholin-dependent phage , endolysin activation was shown to depend on disulphide isomerisation that triggers cleavage of the enzyme from the bacterial membrane [15] . For the highly efficient endolysin PlyC active against streptococcal species , it was found that two catalytic components are tethered in a non-covalent way to eight components of the cell wall binding domain [16] . However , for the classical endolysin/holin system , it is not clear how the endolysins are activated . Here , we present the crystal structures of autoproteolytic fragments of the CD27L and CTP1L endolysins , covering the C-terminal domain . Structure-based mutagenesis allowed us to manipulate autolytic cleavage , and we show that the rate of cleavage is proportional to lysis efficiency for the CTP1L endolysin .
When crystallization trials for full length CD27L endolysin were set up , crystals appeared overnight from freshly purified protein . Any delay in the purification or crystal tray setup would prevent crystallization , and the crystals dissolved after three weeks . An X-ray data set to 2 . 3 Ångstrom was collected from a fresh crystal , and it was realized that the crystal most likely contained the C-terminal portion of the endolysin , because molecular replacement with the previously determined crystal structure of the catalytic domain [7] was not successful . To determine the structure , the C-terminal portion of CD27L was also independently cloned , expressed and purified . This N-terminal deletion construct ( N-CD27L ) was crystallized , and the crystal structure was determined by single wavelength anomalous diffraction ( SAD ) using a mercury derivative ( See Table 1 for details ) . The structure was used as a model to solve the structure of the full-length CD27L crystals by molecular replacement . It was found that the “full length” construct had been proteolyzed and the crystal contained six copies of the C-terminal portion of CD27L alone . The refined structure shows clear electron density for all six monomers of the C-terminal domain with a Matthews coefficient of 2 . 3 , and there is no space in the crystal lattice for an additional N-terminal domain . The C-terminal portion of CD27L consists of a platform of four parallel beta strands , flanked by an alpha helix with two additional alpha helices mounted on top ( Figure 1A , B ) . The N-terminus contains beta strand β1 at the center of the beta sheet , connected to alpha helix α1 . This is followed by beta strand β2 at the outer side of the sheet that is connected through an extended loop including a single 310 helical turn ( η1 ) to beta strand β3 at the center of the beta sheet . The α2 alpha helix connects beta strands β3 and β4 and the fold ends with an alpha helix α3 at the C-terminus of the protein . A DALI search of the PDB for domains with a similar fold did not identify a structure with significant similarity [17] . A BLAST search was done with the sequence covering the proteolytic fragment to identify other proteins that may have a similar domain , and 14 unique sequences were found with an E value <0 . 01 . All these proteins are lysins that target Clostridia species , and the sequence variation is too large to identify residues that define the fold ( Figure 2A ) . Expression of the full-length CD27L endolysin was hampered by severe and continuous proteolysis that could not be diminished by protease inhibitors . An SDS-PAGE gel of freshly purified material typically showed a protein band for the full length protein and a second band with a molecular weight that corresponds to the C-terminal domain ( Figure 3A , B ) . Proteolytic products isolated from an SDS-PAGE gel of CD27L were analyzed by mass spectrometry following tryptic digestion , and this confirmed that the fragments were the intact N-terminal catalytic domain and the C-terminal domain respectively . The proteolytic fragment covering the C-terminal domain was also observed in liquid chromatography coupled to an electrospray mass spectrometry system , and the N-terminal residue was identified as methionine M186 ( Figure 3C ) . These observations are not unprecedented , and similar proteolytic processes can be uncovered from studies on other unrelated endolysins . For instance , crystallization of several endolysins was achieved only after a substantial incubation period [11] , [18] , or the individual domains had to be cloned and crystallized separately due to the degradation of the full-length protein [13] . By investigating the structures of full-length endolysins that underwent these treatments ( PDB codes 1XOV [18] and 2IXU [19] ) we observed that the linker between the domains is always extended and exposed to the solvent . In addition , the catalytic domain and the C-terminal domain are expressed as separate components in PlyC , the most efficient endolysin isolated to date [16] . This raised the possibility that the autoproteolytic cleavage of the catalytic domain in CD27L has a functional role . In an attempt to find the residues involved in the cleavage of the endolysin , we investigated the N-terminus of the proteolytic fragment of CD27L . The catalytic domain precedes the C-terminal domain , and when the two crystal structures are concatenated , there is a seven residue linker between the domains ( Figure 1C ) . The autolytic fragment of the C-terminal domain starts at the end of the linker at methionine M186 , which is still integrated in the C-terminal domain . Among the six copies of the C-terminal domain , there are no consistent contacts between M186 and other residues within the C-terminal domain . The methionine side chain only forms a hydrogen bond with the main chain nitrogen of threonine T227 in two out of six molecules . Since there is no clear candidate among the adjacent residues to be involved in protein cleavage , we decided to mutate methionine M186 to a proline . The M186P mutant will strengthen the main chain at the cleavage point , and would alter the mechanics of the linker at the hinge close to the C-terminal domain . Indeed , the M186P mutant abolishes the cleavage of the endolysin as observed by SDS-PAGE ( Figure 3A ) . In addition , we mutated the amino acid that precedes the methionine ( glutamine Q185 ) to a proline . This residue forms part of the linker and is fully exposed to the solvent . In this case , endolysin cleavage was not affected . This indicates methionine M186 is critical in the cleavage process , and it forms an integral part of the C-terminal domain that is not accessible for external proteolytic cleavage . Another previously characterized phage endolysin that targets Clostridia is CTP1L , which lyses C . tyrobutyricum [14] . This endolysin also contains a C-terminal domain that is approximately 80 residues long , but the sequence identity with the C-terminal domain of CD27L is low ( 22% ) . SDS-PAGE analysis confirmed that purified CTP1L wild type endolysin undergoes cleavage of the C-terminal domain ( Figure 3D ) . We then transferred the critical mutation that affected CD27L cleavage , involving the stem of the linker of the C-terminal domain of CTP1L ( V195P ) . The SDS-PAGE analysis of purified recombinant CTP1L shows a reduction in cleavage for the V195P mutant ( Figure 3D ) . We attempted to crystallize the CTP1L V195P mutant to see if this slowly cleaving mutant would yield crystals of the full length protein . After 2 weeks , crystals appeared , an X-ray data set was collected to 2 . 1 Ångstrom and the structure of the C-terminal domain was solved by molecular replacement using the C-terminal domain of CD27L as a search model . As with CD27L , there was no N-terminal domain present in the crystal lattice . The C-terminal domain is truncated at Pro195 , and there is only one molecule present in the asymmetric unit . The fold of the C-terminal domain of CTP1L is very similar to that of CD27L , except that the second alpha helix α2 is deleted , and the alpha helix α3 is extended in CTP1L ( Figure 2B ) . A superimposition of the two domains based on secondary structure elements using Coot [20] gives an RMSD of 1 . 5 Å for 75 aligned residues , even though the domains have a low sequence identity ( Figure 1D ) . A BLAST search for other proteins that align to the C-terminal domain of CTP1L reveal a separate set of amino acid sequences of lysins targeting Clostridia ( Figure 2C ) . It is not possible to come up with conserved amino acids that define the fold . The only conserved residues are an aspartate on helix α1 ( Asp 206 in CTP1L and Asp198 in CD27L ) , a threonine on helix α3 ( Thr 262 in CTP1L and Thr 261 in CD27L ) , and an arginine ( Arg 259 in CTP1L and Arg 258 in CD27L ) . The conserved aspartate/threonine form a hydrogen bond through a water molecule in both structures , connecting the outer alpha helices , but this is not sufficient to keep the fold together . The proteolytic fragments of CD27L form a mixture of dimers within the crystal lattice . All six molecules are engaged in one common dimerization mode , where the alpha helices α1 and α3 from one molecule stack on their symmetry mate from a second molecule . The α1 and α3 helices run parallel and in the same direction , forming a platform with a concave surface ( Figure 4A ) . The dimerization is such that the N-termini of both monomers are pointing away from the dimer interface , and we term this dimerization mode a ‘head-on’ dimer . The buried surface area is between 1200 and 1300 Å2 for the three head-on dimers found in the asymmetric unit , as determined by the PISA server [21] . The docking for the three head-on dimers observed in the crystal lattice is very similar , and superimposition of the Cα atoms with LSQKAB [22] using both protomers gave RMSDs of 0 . 71 Å and 0 . 84 Å respectively . There is a 2-fold symmetry axis running perpendicular to the parallel alpha helices , with a hydrophobic core at the center consisting of residues valine V204 , leucine L261 and leucine L265 . Further along the rim , there are additional aromatic residues ( tryptophan W207 , phenylalanine F258 and tyrosine Y262 ) whose symmetry mates are involved in dimerization . The strong hydrophobic component , combined with the stacking of aromatic rings indicates this is a stable dimerization mode . The head-on dimer is also present in the crystal lattice of the C-terminal domain of CTP1L . In fact , it is possible to superimpose the whole dimer unit based on secondary structure elements in Coot , with an RMSD for the Ca backbone of 2 . 1 A for 146 residues out of a total of 160 ( Figure 4B ) . None of the residues in the head-on dimer face is conserved between CTP1L and CD27L . To test the significance of the head-on dimer , we performed mutagenesis on two of the aromatic residues involved in the dimer interface of CD27L ( W207A/W207R and Y262A ) , as well as an aspartic acid situated at the edge of this dimer in CTP1L ( D215A ) . These mutants had a surprising effect on the autolytic cleavage , since a decrease was observed in the cleavage product present on an SDS-PAGE gel ( Figure 3B , E ) . These mutants are situated at the opposite site of the linker that connects the C-terminal domain to the catalytic domain . An alternative dimerization mode is found among the six C-terminal domains present in the crystal structure of the proteolytic fragment of CD27L endolysin , between two molecules that are each involved in separate head-on dimers as well ( Figure 4C ) . The α2 helices of the opposing monomers stack against each other and the buried surface area is 1216 Å2 , similar to the values found for the head-on dimer . The side chains of cysteine C238 of the symmetry mates face each other at the center of this dimer , with a sulphur-sulphur distance of 3 . 4 Å ( Figure 4D ) . This distance is too large to qualify for a covalent bond . The cysteine is in close proximity to methionine M251 ( Figure 4D ) , with a sulphur-sulphur distance of 3 . 7 Å ( 4 . 1 Å for the symmetry mate ) . Moreover , lysine K253 forms a hydrogen bond between the NZ atom and the sulphur with a distance of 2 . 8 Å ( 3 . 1 Å for the symmetry mate ) . Together , M251 and L253 seem to destabilize the formation of the disulphide bond . Although the closely related phage endolysins contain cysteine C238 ( Figure 2A ) , the surrounding residues seem to vary . To test the significance of this side-by-side dimer , we mutated cysteine C238 to a serine ( C238S ) or an arginine ( C238R ) , eliminating a potential disulphide bond . The arginine mutant had the strongest effect on the autoproteolytic cleavage , similar to the head-on dimer mutant ( W207A ) , showing a significant reduction in the production of the proteolytic product ( Figure 3A ) . The cleavage site M186 is approximately 20 Ångstrom away from cysteine C238 , indicating that disruption of both dimer interfaces have an effect on the autocleavage of the endolysin . There is a similar side-by-side dimer present in the crystal lattice of the CTP1L C-terminal fragment , but the domains point in opposite directions compared to the same dimer observed in CD27L . There is no cysteine present in the CTP1L side-by-side dimer interface , and the residue that is closest positioned is threonine T221 ( Figure 2B ) . We mutated threonine T221 to a cysteine as well as an arginine , to see if we could emulate the effects observed for the C238R mutant in CD27L . We observed that both the T221C and the T221R mutant reduce autocleavage to almost undetectable levels , and that these mutants have a stronger effect than the V195P mutant ( Figure 3D ) . This provides further support for the role of an oligomeric switch in the autocleavage of these endolysins . To determine the low resolution shape and the oligomeric state of CD27L endolysin in solution , small-angle X-ray scattering ( SAXS ) experiments were conducted using freshly purified material ( Table 2 ) . We used the crystal structures of the catalytic domain of CD27L ( PDB code 3QAY ) and the crystal structure of the C-terminal domain presented here to make a composite model of the full length CD27L endolysin using the structure of the intact PlyPSA amidase ( PDB code 1XOV ) to place the two domains . This model was employed to test the presence in solution of the two dimeric states of the C-terminal domain observed in the crystal structure . The molecular mass of the solute of wild type full length CD27L , estimated from the forward scattering intensity was 42±4 kDa , significantly lower than expected for a 64 kDa dimer and indicative of a possible equilibrium of the dimers with dissociation products . SAXS curves calculated from both the head-on and side-by-side dimers using CRYSOL [23] produced poor fits ( discrepancy χ = 1 . 8 and 4 . 0 ) to the experimental data from the wide type protein ( Figure 5A ) . For the C238R mutant , however , the experimental data fit a scattering curve calculated from the head-on dimer configuration of the composite model ( discrepancy χ = 1 . 0 ) ( Figure 5A ) . The side-by-side dimer is not compatible with the scattering curve for this mutant ( χ = 3 . 3 ) . This is an indication that this mutation has driven the equilibrium of the oligomeric states towards the head-on dimer . The distance distribution function p ( r ) of the C238R mutant ( Figure 5A , insert ) displays two distinct peaks , the one at larger distance ( about 70 Å ) matching the distance between the centers of the catalytic domains in the head-on dimer . The p ( r ) function of the wild type lacks this feature and displays a smaller maximum size , again suggesting an equilibrium of dimers and dissociation products . Low resolution shape reconstruction from the SAXS data for the wild-type and C238R mutant yields compact and extended structures , respectively . These models represent an average of the conformations of all particles present in solution . The volumes of the models constructed from the wild-type ( Vex = 94000±10000 Å3 ) and C238R ( Vex = 123000±10000 Å3 ) data are consistent with that of a mixture and of a dimeric CD27L structure , respectively ( Table 2 ) . The extended shape reconstructed for the C238R mutant overlays well with the head-on dimer model , providing a good low resolution representation of the solution structure ( Figure 5C ) . We then performed gel exclusion chromatography coupled with right-angle light scattering and refractive index/UV measurements to assess the molecular weights of each endolysin species , comparing the separation profiles of the wild type CD27L with the C238R and M186P mutants . The elution profile of the wild type CD27L endolysin is rather complicated ( Figure 6A ) . We interpret the peak with a molecular weight mass of 68±4 kDa as predominantly containing endolysin dimers ( expected MW is 64 kDa ) , and the peak with a molecular weight mass of 33±7 as a CD27L monomer . In between , there is a peak at 43±2 kDa molecular weight that we interpret as a mixture of CBD-cleaved monomer in complex with full length protein ( expected MW , 42 kDa ) . We conclude that the wild-type protein exists in different oligomeric states in solution that are affected by autoproteolytic cleavage . The C238R mutation produces an elution profile with a single peak corresponding to the MW of a dimer ( 61±3 kDa ) ( Figure 6B ) . The M186P mutant also appears to exist predominantly as a dimer ( MW , 63±3 kDa , Figure 6C ) , but it has a tendency to form aggregates . It is interesting to note that both mutations force the CD27L endolysin to adopt a dimeric state , even though only one mutation , M186P , is incorporated directly at the autoproteolytic cleavage site . A more distinct elution profile is obtained for wild-type CTP1L , which exclusively forms a dimer with an average molecular weight of 66±5 kDa ( Figure 6D ) . A CTP1L mutant in the head-on dimer interface ( D215A ) that reduces cleavage behaves predominantly as a monomer ( average molecular weight 33±1 kDa ) , with a small portion present as a dimer . The results of the size exclusion analysis of CD27L wild-type and mutants suggest that both the integrity of the internal cleavage site combined with how endolysin self-associates are key factors that dictate the final auto-cleavage event . M186P abolishes cleavage , as indicated by the disappearance of the intermediate 43 kDa species as well as the monomer peak from the elution profile . Abolishing side-by-side dimer formation via the introduction of a C238R mutation produces a dimeric state that is less-prone to aggregation . In addition , autoproteolytic cleavage has ceased , and the sole presence of the head-on dimer leads to an elution profile with a single dimer peak . Consequently , auto-proteolytic cleavage appears to be a spatially controlled trans event that occurs between endolysin monomers but only when these monomers associate to adopt the appropriate dimeric – or oligomeric – state . To further investigate oligomerisation states and potential degradation of the CD27L samples in solution , the experimental data was analyzed in terms of possible mixtures using OLIGOMER [24] . The extended head-on and compact side-by-side dimer models and their individual domains were used to generate form-factor files for a fitting procedure , where volume fractions of each component present were determined that minimize the discrepancy between the theoretical scattering of the components and the experimental data ( Table 3 ) . The contribution from the potential degraded products including the lysed side-by-side dimer with a missing catalytic domain , dimers of C-terminal domains and the individual domains were pooled together as an additional component . The C238R data is described exclusively by the extended head-on dimer component scattering . The head-on dimer is also the dominant component in solution for wild-type , but the other components show noticeable contributions providing the best description of the wild-type data . This result further explains the low apparent molecular mass determined from the wild-type SAXS data , and also why the individual structures show such poor fits to the wild-type data ( Figure 5A ) . To independently verify oligomerization of the CTP1L endolysin , we cloned the C-terminal domain alone and expressed it in E . coli . We introduced an amber stop codon at position Y212 which sits on alpha helix α1 ( Y212pBpa ) . We also introduced an amber stop codon at position Y260 , which is situated on alpha helix α3 ( Y260pBpa ) . Both alpha helix α1 and α3 are involved in head-on dimerization ( Figure 2B and 4A , B ) . We then expressed both amber mutants in the presence of the cross-linkable amino acid p-benzoyl-L-phenylalanine ( pBpa ) together with a pBpa specific tRNA and a tRNA synthetase that are capable of placing the pBpa at the position of the amber stop codon . In this way , a light sensitive cross-linker is introduced with a specific radius of interaction of approximately 10 Ångstrom [25] . The incorporation of the unnatural amino acid was confirmed for both mutants by tryptic digest , followed by mass spectrometry . We show that upon exposure to UV light , both the Y212pBpa and the Y260pBpa mutants show an additional band on an SDS-PAGE gel at double the molecular weight of the C-terminal domain alone ( Figure 7A ) , whereas the unexposed and the wild-type protein do not show any cross-linked material . A mutant ( D215A ) that affects the head-on dimer in CTP1L does also cross-link when combined with the Y212pBpa mutant , indicating that the cross-linking process captures transient oligomeric states over an extended period of time ( 2 hours ) . The band with elevated molecular weight was treated with trypsin and analyzed by mass spectrometry and it was confirmed that it contained the C-terminal domain of CTP1L . We also see faint larger bands that could consist of the trimer and tetramer . Since the pBpa cross-linking is quite specific , we conclude that the head-on dimer is also formed by the C-terminal domain of CTP1L in solution . Finally , we introduced an amber stop codon in the full length CTP1L endolysin replacing Y212 , which showed a higher final yield of pBPA incorporation when compared to position Y260 . The full length CTP1L protein is cross-linked only upon exposure to UV light and forms a mixture of full length CTP1L dimers , as well as a species that based on the molecular weight consists of one full length CTP1L and a C-terminal domain fragment ( Figure 7B ) . The oligomerization states of the CTP1L fragments observed by cross-linking reinforce the interpretation of the size exclusion chromatography and light scattering experiments done on the CD27L endolysin , showing that both the cleavage and the oligomerization occur in both endolysins . To verify whether the autoproteolytic cleavage affects the activity of the endolysins containing the C-terminal domain , we performed cell lysis on C . difficile cultures with recombinant CD27L wild type and mutants using turbidity reduction assays ( Figure 7C , D ) . We observed no difference in lysis efficiency between the wild type protein and mutants which prevent/reduce cleavage either at the cleavage site ( M186P ) or by affecting the side-by side ( C238R and C238S ) or head-on dimers ( W207A , W207R and Y262A ) . This establishes at the least that these mutants are enzymatically active , but it does not resolve whether autocleavage plays a role in endolysin function . Introduction of mutations at the catalytic site ( H84A and E144A ) did abolish lytic activity ( Figure 7D ) but did not affect cleavage ( Figure 3B ) . It was previously established that when applied externally to C difficile cells , a construct that contains the enzymatic domain alone shows the same lytic efficiency as the full length protein [7] . Therefore , the lysis assay is insensitive to the trigger and release function of the C-terminal domain for CD27L . However , CTP1L is only active as an intact , full length protein , and the enzymatic domain alone does not lyse C . tyrobutyricum cultures [14] . Lysis of C . tyrobutyricum cultures by wild-type CTP1L is robust ( Figure 7D ) , leading to a drop in optical density ( OD ) at 600 nm . The mutants show a drop in lysis efficiency that is proportional to the reduction in autocleavage . The V195P mutant is still somewhat active , whereas the T221R and T221C mutants show no lysis at all . We verified that these mutants are similar in secondary structure to the wild-type ( Figure S1 ) . We conclude that in the context of an externally applied recombinant endolysin , CTP1L depends on the autoproteolytic cleavage of its C-terminal domain to lyse C . tyrobutyricum .
Bacteriophages release endolysins at the end of the phage life cycle to lyse the host bacterial cell following a well-timed trigger mechanism [9] . The molecular mechanisms underlying such a trigger are unknown , but it is thought that endolysins are activated after the formation of holin lesions in the bacterial cell membrane [10] . When the endolysins pass from the cytosol to the extra-cellular environment , they will undergo a substantial change in environment and this may activate the endolysin . The crystal structures presented here for the C-terminal domains of two endolysins that target Clostridia bacteria ( CD27L and CTP1L ) suggest that CD27L exists in two distinct dimeric states . We show indirectly that these dimeric states are associated with an autocleavage mechanism , because several mutations in the dimer interfaces reduce autocleavage . Endolysin dimerization has been shown for other bacteriophage species , and the dimerization seems to influence endolysin activity . The pneumococcal autolysin LytA does dimerize into a conformation resembling the side-by-side dimer presented here , and it was suggested that the dimer conformation may contribute to its activity [26] . The CPL-1 phage endolysin that targets Streptococcus pneumoniae was engineered to stabilize the ( what we call ) side-by-side dimerization mode , and this led to a ten fold increase in its activity [27] . We propose that the oligomeric switch can be described in terms of an Monod-Wyman-Changeux mechanism [28] , with a ‘tensed’ state that represents the inactive endolysin and a ‘relaxed’ state that represents the active endolysin . We propose that the ‘tensed’ state is related to the head-on dimer , where the two endolysin units are extended and the two autocleavage sites are far apart . The ‘relaxed’ state is the side-by-side dimer , which promotes autocleavage and the release of the catalytic domain from the C-terminal domain . Autocleavage increases the action radius of the catalytic module , and as previously suggested [29] , the small globular size of this enzyme may allow it to further penetrate the bacterial cell wall which may act as a sieve . Bacteriophages have been shown to use a mechanism of autocleavage and oligomerization when entering the bacterial cell wall upon infection [30] . Some bacterial toxins are activated upon autocleavage [31] , [32] . We have not been able to identify residues that catalyze the cleavage of the catalytic domain , but we managed to switch the cleavage off with a point mutation ( M186P ) at the hinge of the linker . The presence of a methionine at this position for CD27L seems to be of significance , as can be seen from the sequence alignment between lysins with a similar domain ( Figure 2A ) . According to the sequence alignment presented in Figure 2 , all lysins that have a cysteine present at position 238 , also have a methionine at the start of the domain . It is interesting to note in this respect that a chimera between the catalytic domain of CS74L and the C-terminal domain of CD27L ( CS74L1-177-CD27L180-270 ) [33] also cleaves off its C-terminal domain ( unpublished results ) . The C-terminal domain could therefore be involved in autonomous self-cleavage , but this needs to be further investigated . At this stage , we can only speculate about the role of the side-by-side dimer in the autocleavage mechanism . We believe that this dimerization mode will affect the conformation of the linker that connects the two domains , possibly bringing two linkers within close proximity . The methionine M186 of CD27L and the valine V195 of CTP1L may be involved in cis- ( within the linker itself ) or trans ( in an exchange between the two linkers ) autoproteolysis , such as is observed for other bacterial enzymes that undergo maturation [34] . This would represent a new form of protein splicing , involving two copies of the endolysin , rather than a single autonomous splicing unit such as is observed in inteins [35] . We are in the process of further investigating this splicing mechanism . We have shown that autocleavage is an intrinsic property of two endolysins targeting Clostridia , and we believe that this mechanism occurs in other endolysin systems as well . The most potent lysin identified to date ( PlyC ) consists of two components that are expressed independently [16] . Structural characterization revealed that one component provides dual catalytic activity , whereas the other component is an octomeric cell wall binding unit . The lack of a covalent link between the enzymatic portion and the cell wall binding domain is probably key for its increased potency . We therefore believe that the engineered clustering of endolysins through a controlled oligomerization of the C-terminal domains may lead to more efficient enzymes with high specificity . This opens new opportunities to produce recombinant phage or endolysins that can lyse specifically pathogenic bacteria without affecting the microbiome overall .
The nucleotide sequence of the full-length endolysins CD27L and CTP1L mutant V195P , as well as the C-terminal domain CD27L180-270 were inserted in pET15b , containing an N-terminal His tag and a thrombin cleavage site as described previously [7] . Constructs were expressed in E . coli BL21 ( DE3 ) grown in Lysogeny broth ( LB ) media until an OD600∼0 . 6 was induced with 1 mM isopropyl-β-D-thio-galactopyranoside for overnight expression at 21°C . Protein expressing cells were harvested by centrifugation ( 5500 rpm , 30 min ) and the supernatant discarded . Pelleted cells were lysed chemically in lysis buffer ( 50 mM Tris pH 8 . 0 , 300 mM NaCl , 1% Triton X-100 , 10 mM Imidazole , 1 mg/ml Lysozyme , 25 U/ml Benzonase nuclease ) for 30 min at 4°C . Lysed cell extract was centrifuged ( 18 , 000 rpm , 40 min ) and supernatant containing His-tagged endolysin purified by nickel-nitrilotriacetic acid ( Ni-NTA ) purification ( Qiagen ) . Protein was eluted in a final elution buffer of 50 mM Tris pH 8 . 0 , 150 mM NaCl , 200 mM Imidazole . Proteins were purified for crystallization by size exclusion chromatography using an Aekta liquid chromatography system ( Amersham Biosciences ) and S75 10/300 GL ( Tricorn ) column ( GE Healthcare ) in 20 mM HEPES , pH 7 . 4 . The protein was concentrated to 10 mg/mL as measured by UV absorption at 280 nm . Protein crystals for degraded CD27L , that ultimately only contained the C-terminal domain , were obtained from a mother liquor containing 10 – 20% PEG 4000 and 20 mM Tris pH 8 . 0 . Crystals of the construct containing the C-terminal domain of CD27L and an N-terminal His tag were obtained from a mother liquor of 10% PEG 20K and 20 mM Tris pH 8 . 0 . For the CTP1L V195P mutant , crystals were obtained from a mother liquor containing 20 mM TRIS pH 8 . 0 and 6% PEG 8000 . The C-terminal domain of CD27L was first solved by single-wavelength anomalous dispersion using a mercury derivative ( Table 1 ) . Crystals of the CD27L C-terminal domain construct alone with an N-terminal His tag were soaked in a cryo-protecting solution containing 15% PEG 20K , 100 mM Tris pH 8 , 10% ( v/v ) glycerol and the derivative 1 mM of Ethyl-mercury phosphate for a few minutes prior to freezing . A data set was collected on the X12 beamline at EMBL Hamburg , which is equipped with a MAR225 CCD detector . The crystal diffracted to a resolution of 3 . 5 Å , and the space group was P21 . All the X-ray data were indexed , merged and scaled with DENZO and Scalepack [36] . The crystal contained eight copies of the C-terminal domain in the asymmetric unit , and 8 mercury sites were identified with SHELXD [37] . Density modification was performed with PARROT , and an initial model was built with BUCCANEER [38] . This model was used in PHASER [39] to further improve the experimental phases and to find 5 additional mercury sites after several iterations . A native X-ray data set was collected on PROXIMA I at the Soleil Synchrotron ( Gif-sur-Yvette , France ) , using a Q315 CCD detector from ADSC . The crystal diffracted to 2 . 3 Å and belonged to space group P212121 . The initial model was then used in molecular replacement using MOLREP [40] to identify the contents of the crystals grown from initial full length CD27L . It was determined that these crystals contained six copies of the C-terminal domain in the asymmetric unit . The structure was refined with Refmac5 [41] to an R factor of 19 . 8% ( Rfree = 25 . 6% ) . The stereochemistry of the model contained 98 . 2% of the residues within the favored areas of the Ramachandran plot according to Molprobity [42] , and no residues in the disallowed regions . A native X-ray data set was collected on the EMBL beamline P14 at the PETRA3 synchrotron ( Hamburg , Germany ) using a MAR225 CCD detector . Although the crystal probably diffracted to at least 1 . 5 Ångstrom resolution , we were only able to collect usable data to a resolution of 2 . 1 Ångstrom due to a limited detector geometry . The crystal diffraction also suffered from ice rings , limiting the completeness of the data to 92% . Nevertheless , it was straightforward to solve the structure of the C-terminal domain of CTP1L by molecular replacement with MOLREP [40] using the C-terminal domain of CD27L as a search model , since there is only one copy of the molecule in the asymmetric unit . The structure was refined with Refmac5 to an R factor of 17 . 2% ( Rfree = 26 . 4% ) , and the electron density is of good quality . The stereochemistry of the refined model contained 98 . 8% of the residues within the favored areas of the Ramachandran plot according to Molprobity [42] , and no residues in the disallowed regions . The mutants of CD27L and CTP1L were created by PCR site-directed mutagenesis following the Quikchange method ( Stratagene ) . Plasmids pET15b-cd27l [5] and pET15b-ctp1l [14] were used as template DNA . Complementary primer pairs for each mutation ( Table S1 ) were used for whole plasmid mutagenesis PCR performed using Phusion polymerase ( NEB ) . Template DNA was digested by DpnI ( NEB ) before transformation into competent E . coli DH5α ( Invitrogen ) . Plasmid DNA was obtained by Miniprep ( Qiagen ) for sequence confirmation . Mutants were expressed and purified using the same method as wild-type CD27L . Samples of all constructs were mixed with reducing Laemmli buffer , heated for 5 minutes at 75°C and subjected to 15% SDS polyacrylamide gel electrophoresis . For Coomassie Blue staining , the SDS-PAGE gel was incubated respectively in Coomassie Blue staining solution ( 0 . 125% Coomassie Blue , 45% ethanol , 10% acetic acid ) , destaining solution ( 40% ethanol , 10% acetic acid ) and drying solution ( 2% glycerol , 20% ethanol ) . Protein samples ( around 2 mg/mL ) were acidified using 1% formic acid solution and transferred to vials prior to LC-MS analysis . Desalting and protein separation were carried out using an Acquity UPLC system ( Waters ) fitted with a C4 column ( 2 . 1 mm×15 cm , 5 µm particle size ) . The column was maintained at constant temperature ( 40°C ) throughout . The outlet of the column was coupled directly to a Q-Tof II mass spectrometer ( Waters ) using the standard ESI source in positive ion mode . Solvent A was water , 0 . 1% formic acid and solvent B was acetonitrile , 0 . 1% formic acid . The samples ( between 1 and 20 µL ) were loaded onto the column and desalted for 5 minutes at a flow rate of 0 . 2 mL/min , 4% B . The proteins were then eluted from the column with a constant flow of 0 . 2 mL/min . During the elution step , the percentage of solvent B increased in a linear fashion from 5% to 25% in 1 minute , then increased to 80% in a further 11 minutes . On the Q-Tof , a spray voltage of 3 . 5 kV was applied , with a cone voltage of 35 V and extraction cone at 10 V . A collision energy of 8 eV was used , with Argon in the collision cell . The desolvation temperature was set at 320°C , with a source temperature of 120°C . Data were acquired in continuum mode , over a mass range 500-3500 m/z with a scan time of 0 . 5 s and interscan delay of 0 . 1 s . Data were externally calibrated against a reference standard of intact myoglobin , acquired immediately after sample data acquisition . Spectra across the protein chromatographic peak ( s ) were summed and intact mass was calculated using the MaxEnt1 maximum entropy algorithm ( Waters/Micromass ) to give the zero charge deconvoluted molecular weight . Molecular weight estimates of several CD27L variants ( CD27L wild-type , CD27L C239R and CD27L M186P ) as well as CTP1L and CTP1L D215A were evaluated using size-exclusion chromatography in combination with right-angle light scattering ( RALS ) , refractive index ( RI ) and UV ( λ280 nm ) measurements ( Malvern Instruments Viscotek , RALS/RI/UV 305 TDA detector equipped with a 670 nm laser diode ) . All measurements were performed at room temperature . Samples were separately injected at their respective concentrations ( 75 µL at 6 . 37 , 7 . 15 and 6 . 17 mg . mL−1 ) onto a GE-Healthcare Tricorn S75 10/300 GL column equilibrated in 20 mM HEPES pH 7 . 4 , 500 mM NaCl at a flow rate of 0 . 4 mL . min−1 . The molecular weight ( MW ) of each species eluting from the SEC column were assessed using concentration ( c ) measurements derived from base-line corrected RI or UV measurements in combination with base-line corrected RALS intensities calibrated against a bovine serum albumin narrow ( monomeric ) standard ( RALS = c ( dn/dc ) 2 . MW . kRALS; RI = c ( dn/dc ) kRI and; UV = cεkUV , where dn/dc is the refractive index increment of unmodified protein , 0 . 185 mL . g−1 , kRI , kUV and kRALS are the TDA instrument calibration constants relative to a BSA and ε the λ280 nm E0 . 1% extinction coefficient of each protein in mg . mL−1 ) . The MW correlations across the selected range of each CD27L elution peak and the final MW estimates quoted in the text were calculated using OmniSEC Software ( Malvern Instruments ) . Synchrotron radiation X-ray scattering data were collected on the EMBL X33 and P12 beamlines of the storage rings DORIS III and PETRA III ( DESY , Hamburg ) , respectively , using PILATUS 1 M and 2 M pixel detectors ( DECTRIS , Switzerland ) . For the wild-type CD27L data were acquired at X33 , with 8 frames of 15 s exposure time collected . Samples were measured in a temperature controlled cell at 10°C in 20 mM HEPES buffer , 150 mM NaCl pH 7 . 4 at protein concentrations of 0 . 9–4 . 0 mg/mL . The sample-to-detector distance was 2 . 7 m , covering a range of momentum transfer 0 . 01≤s≥0 . 6 Å−1 ( s = 4π sinθ/λ , where 2θ is the scattering angle , and λ = 1 . 54 Å is the X-ray wavelength ) . For the C238R mutant data were acquired at P12 , with 20 frames of 0 . 05 s exposure time collected . Solutions were measured while flowing through a temperature controlled capillary at 10°C in 20 mM Tris buffer , 500 mM NaCl pH 7 . 4 at protein concentrations of 1 . 0–8 . 5 mg/mL . The sample-to-detector distance was 3 . 1 m , covering a range of momentum transfer 0 . 008≤s≥0 . 458 Å−1 ( s = 4π sinθ/λ , where 2θ is the scattering angle , and λ = 1 . 24 Å is the X-ray wavelength ) . Based on comparison of successive frames , no detectable radiation damage was observed . Data from the detector were normalised to the transmitted beam intensity , averaged and the scattering of buffer solutions subtracted . The difference curves were scaled for solute concentration and the 1 . 0 mg/mL ( low-s ) and 8 . 4 mg/mL ( high-s ) data sets merged for modeling . All data manipulations were performed using PRIMUS [43] . The forward scattering I ( 0 ) and radius of gyration , Rg were determined from Guinier analysis [44] , assuming that at very small angles ( s≤1 . 3/Rg ) the intensity is represented as I ( s ) = I ( 0 ) exp ( - ( sRg ) 2/3 ) ) . These parameters were also estimated from the full scattering curves using the indirect Fourier transform method implemented in the program GNOM [45] , along with the distance distribution function p ( r ) and the maximum particle dimension Dmax . Molecular masses ( MMs ) of solutes were estimated from SAXS data by comparing the extrapolated forward scattering with that of a reference solution of bovine serum albumin , and also from the hydrated-particle/Porod volume Vp , where molecular mass is estimated as 0 . 625 times Vp . Low-resolution shape envelopes for all constructs were determined using the ab initio bead-modelling program DAMMIF [46] , using both P1 and P2 symmetry . The results of 10 independent DAMMIF runs were analyzed using the program DAMAVER [47] to identify the most representative/typical models . Modeling using P2 symmetry was only attempted following the identification of excluded solvent volumes , Vex in models generated in P1 ( slow mode ) consistent with that expected for dimers ( see Table 2 ) . Molecular modelling was conducted using , as rigid bodies and where appropriate , the crystal structures of the catalytic and the C-terminal domains of CD27L determined in this study . Rigid-body models were generated using the program CORAL [24] and 10 independent runs assessed for convergence with DAMAVER . Additional fitting of PDB files to the SAXS data was conducted using CRYSOL [23] . Using the program OLIGOMER [24] , the SAXS data for both wild-type CD27L and the C238R mutant was used to model potential multicomponent mixtures of species in solution . Form factors of input PDB files were calculated using the program FFMAKER [24] . Form factors were also calculated for individual domains and substructures of the intact PDB files to represent products of autolysis and averaged . This averaging was performed as the identity of the exact solution composition of these lysis products could not be established . Volume fractions corresponding to each component ( eg . extended dimer , compact dimer and “degraded components” ) were determined by OLIGOMER utilising a non-negative least squares procedure . Following the principal method outlined by Farrell et al . [48] , the photo-activated amino acid p-benzoyl-L-phenylalanine ( pBPA ) ( BACHEM ) was incorporated into the full-length CTP1L endolysin and the truncated C-terminal domain of CTP1L . CTP1L was amplified from ctp1l-pET15b [14] and inserted into the pET21d vector . The amber codon ( TAG ) was incorporated at position Y212 or Y260 using complementary primer pairs ( Table S1 ) following the Quikchange method of PCR site-directed mutagenesis to generate Y260TAG-pET21d and Y212TAG-pET21d . Sub-cloning into pET21d introduced a C-terminal hexa histidine-tag to the construct , which permits selective Ni-NTA purification of full-length proteins that have only incorporated pBPA . C-terminal constructs containing Y260TAG and Y212TAG were generated by amplifying the C-terminal domains of Y212TAG-pET21d and Y260TAG-pET21d between positions V195 and K274 . The PCR products were inserted back into the pET21d vector to create C-termY260TAG-pET21d and C-termY212TAG-pET21d . As a control , the wild-type C-terminal domain was sub cloned into pET21d with no amber stop codon incorporated . The C-terminal domain double mutant combining Y212TAG and D215A was created with complementary primers containing both mutations and using the wild-type C-terminal domain construct as a template . E . coli BL21 ( AI ) cells were transformed with pEVOL-pBPA ( aminoacyl-tRNA synthetase/suppressor tRNA ) and one of the plasmids encoding an amber codon containing construct: Y212TAG-pET21d , C-termY212TAG-pET21d or C-termY260TAG-pET21d . Cells were grown in 500 ml Lysogeny broth ( LB ) media supplemented with 1 mM pBPA in the presence of ampicillin and chloramphenicol . When an OD600 of 0 . 6 had been reached the cultures were induced with Arabinose ( final concentration 0 . 02% ) and expressed at 21°C overnight . Cells were harvested by centrifugation ( 5500 rpm , 30 min ) and the supernatant discarded . The full length CTP1L mutant Y212pBPA was Ni-NTA purified as described above and dialyzed into 25 mM TRIS , pH 7 . 4 . The protein was concentrated to 0 . 5 mg/mL , as measured by UV absorption at 280 nm . A 1 ml aliquot of purified protein was pipetted into a single well of a 24-well clear polystyrene plate , typically used for protein crystallography . The lid was kept on to prevent sample evaporation , and placed inside an RPR-100 UV reactor equipped with 350 nm bulbs ( Rayonet ) . The reactor was kept at 4°C with the cooling fan on . Proteins were exposed to UV light for 15 minute intervals during which the solutions were stirred by gentle pipetting and samples taken for each time point for SDS-PAGE analysis . As a control , samples were also taken every 15 minutes from a parallel sample of Y212pBPA that was kept at 4°C in the dark with no UV exposure . Cross-linking was analyzed by SDS-PAGE by comparison of the pre-UV and the final post-UV exposed samples . The same photo-cross-linking experiment was performed for the C-terminal domain constructs . Ni-NTA purified C-termY212pBPA , C-termY260pBPA , C-termY212pBPA_D215A and C-terminal wild-type were dialyzed into 25 mM TRIS , pH 7 . 4 and concentrated to 2 mg/mL as measured by UV absorption at 280 nm . Following the same protocol as described above for the full-length Y212pBPA , 500 µl aliquots of each protein were pipetted into separate wells of a 24 well clear polystyrene plate and exposed to UV light for 120 minutes with stirring of the samples at 15 minute intervals . As a control SDS-PAGE samples were taken from parallel aliquots of each protein kept at 4°C in the dark with no UV exposure . Lysis assays were performed on fresh cells of C . difficile NCTC 11204 or C . tyrobutyricum NCIMB 9582 . Cells were cultured , harvested and resuspended in PBS pH 7 . 3 as described previously [5] , [14] . Lysis assays were performed on freshly harvested cells in 300 µl volumes with 10 µg Ni-NTA-purified protein or elution buffer . Results are the mean of duplicate assays +/− standard deviation .
|
Clostridium difficile infection is a common cause of hospital-acquired diarrhea , following broad-spectrum antibiotic treatment particularly in elderly patients . Bacteriophage therapy could provide an alternative treatment , but a better understanding of the viral components that lyse the bacterial cell is necessary . Here , we report on the activation of two endolysins from bacteriophages that lyse Clostridia . The structures of autoproteolytic fragments of two endolysins were determined by X-ray crystallography . Based on the structures , we introduced mutations that affect the autolytic cleavage of the enzymatic portion of the endolysins , and we show that two oligomeric states have an effect on the cleavage mechanism . Moreover , the lysis activity is affected when autocleavage is inhibited for one endolysin . We propose that the cleavage and oligomerization are linked , and they provide the endolysin with a trigger and release mechanism that leads to activation . The identification of a trigger and release factor may not only be relevant to Clostridia endolysins , but could be an important factor in the triggering of many bacteriophage endolysins . A fuller understanding of this activation mechanism will help in the design of recombinant endolysins or bacteriophages with a more efficient therapeutic potential .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"molecular",
"complexes",
"pathology",
"and",
"laboratory",
"medicine",
"enzymes",
"viral",
"enzymes",
"regulatory",
"proteins",
"enzymology",
"microbiology",
"biocatalysis",
"protein",
"structure",
"enzyme",
"kinetics",
"enzyme",
"chemistry",
"bacterial",
"pathogens",
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"medical",
"microbiology",
"enzyme",
"regulation",
"microbial",
"pathogens",
"pathogenesis",
"molecular",
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"biochemistry",
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"host-pathogen",
"interactions",
"virology",
"microbial",
"control",
"biology",
"and",
"life",
"sciences"
] |
2014
|
The CD27L and CTP1L Endolysins Targeting Clostridia Contain a Built-in Trigger and Release Factor
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The roles of histone demethylation in the regulation of plant flowering , disease resistance , rhythmical response , and seed germination have been elucidated recently; however , how histone demethylation affects leaf senescence remains largely unclear . In this study , we exploited yeast one-hybrid ( Y1H ) to screen for the upstream regulators of NONYELLOWING1 ( NYE1 ) , and identified RELATIVE OF EARLY FLOWERING6 ( REF6 ) , a histone H3 lysine 27 tri-methylation ( H3K27me3 ) demethylase , as a putative binding protein of NYE1 promoter . By in vivo and in vitro analyses , we demonstrated that REF6 directly binds to the motif CTCGYTY in NYE1/2 promoters through its zinc finger domain and positively regulates their expression . Loss-of-function of REF6 delayed chlorophyll ( Chl ) degradation , whereas overexpression of REF6 accelerated Chl degradation . Subsequently , we revealed that REF6 positively regulates the general senescence process by directly up-regulating ETHYLENE INSENSITIVE 2 ( EIN2 ) , ORESARA1 ( ORE1 ) , NAC-LIKE , ACTIVATED BY AP3/PI ( NAP ) , PYRUVATE ORTHOPHOSPHATE DIKINASE ( PPDK ) , PHYTOALEXIN DEFICIENT 4 ( PAD4 ) , LIPOXYGENASE 1 ( LOX1 ) , NAC DOMAIN CONTAINING PROTEIN 3 ( AtNAC3 ) , and NAC TRANSCRIPTION FACTOR-LIKE 9 ( NTL9 ) , the key regulatory and functional genes predominantly involved in the regulation of developmental leaf senescence . Importantly , loss-of-function of REF6 increased H3K27me3 levels at all the target Senescence associated genes ( SAGs ) . We therefore conclusively demonstrate that H3K27me3 methylation represents an epigenetic mechanism prohibiting the premature transcriptional activation of key developmentally up-regulated senescence regulatory as well as functional genes in Arabidopsis .
Histone methylation plays an essential role in diverse biological processes , ranging from transcriptional regulation to heterochromatin formation . Histone lysine methyltransferases ( “writers” ) and demethylases ( “eraser” ) dynamically regulate methylation levels , and in Arabidopsis , methylations at Lys4 ( K4 ) , Lys9 ( K9 ) , Lys27 ( K27 ) , and Lys36 ( K36 ) of histone H3 have been extensively studied [1] . In general , histone H3K9 and H3K27 methylation are associated with silenced regions , whereas H3K4 and H3K36 methylation with active genes [2] . H3K9me1/2 and H3K27me1 are enriched at constitutively silenced heterochromatin in Arabidopsis [3 , 4] . H3K27me3 repression of gene expression during development is a conserved mechanism in eukaryotes , and several thousand genes ( more than 15% of all transcribed genes ) in Arabidopsis are marked by the modification [5–7] . H3K27me3 is catalyzed by polycomb repressive complex 2 ( PRC2 ) , which consists of four parts: E ( Z ) , Su ( z ) 12 , p55 , and Esc . Sequence similarity and genetic analyses revealed that Arabidopsis EZH2 homologs , including curly leaf ( CLF ) , swinger ( SWN ) , and medea ( MEA ) , are H3K27me3 methyltransferases [1 , 5] . The Jumonji ( JMJ ) protein was first identified in mouse by a gene trap approach [8] . Then , JMJ domain-containing proteins were found to be able to remove histone methyl groups [9 , 10] . Arabidopsis JMJ homologs , RELATIVE OF EARLY FLOWERING 6 ( REF6 ) , EARLY FLOWERING 6 ( ELF6 ) and JMJ30 , were demonstrated as H3K27me3 demethylases [11–13] . ELF6 was first reported as a repressor of flowering in the photoperiod pathway , and REF6 , with the highest similarity to ELF6 , as a flowering locus C ( FLC ) repressor [14] . Loss-of-function mutations in REF6 lead to the ectopic accumulation of H3K27me3 at hundreds of genes in seedlings , suggesting that REF6 is a coordinator of multiple developmental programs in plants [11] . BRI1-EMS-suppressor 1 ( BES1 ) and Nuclear transcription factor Y ( NF-YA ) were reported to recruit REF6 to its target genes [15 , 16] . Notably , a new targeting mechanism of REF6 was recently revealed , i . e . by directly binding the CTCTGYTY motif in its target genes [17 , 18] . In senescing leaves , the reprogrammed distribution of H3K4me3 and H3K27me3 accompanies a decondensation of chromocenter heterochromatin in the interphase nuclei [19] . A further report showed that the senescence-associated global changes at chromatin organization can be inhibited by overexpressing SUVH2 , a gene encoding a methyltransferase [20] . The above reports reveal a close relationship between senescence and histone modification , but how histone modification precisely regulates leaf senescence remains unclear . Plants senesce typically in the modular manner and leaves are the major modular organ . Leaf senescence is an integral part of plant development , triggering characteristic degenerative processes as an adaptive mechanism , such as chlorophyll ( Chl ) degradation and macromolecule breakdown , and particularly recycling of released nutrients to nascent tissues or storage organs [21–23] . De-greening , reflecting a net loss of Chl , is the most obvious symptom of leaf senescence , which is closely associated with the degradation of light-harvesting complexes ( LHCs ) [24 , 25] . The biochemical pathway of de-greening has been largely elucidated by the identification of Chl catabolic genes ( CCGs ) or Chl catabolic enzymes ( CCEs ) in Arabidopsis as well as in rice [26] . During senescence , Chl b is converted to Chl a via the catalysis of Chl b reductase ( encoded by NYC1/NOL ) and 7-hydroxymethyl Chl a reductase ( HCAR ) [27–30] . NYEs/SGRs-catalyzed magnesium dechelation is the first step of Chl a degradation , generating pheophytin a [31] , which is then sequentially degraded/modified to: 1 ) pheophorbide a by pheophytin pheophorbide hydrolase ( PPH ) [32 , 33] , 2 ) to red chlorophyll catabolites ( RCC ) by pheophorbide a oxygenase ( PAO ) , 3 ) to primary fluorescent chlorophyll catabolites ( pFCC ) by red chlorophyll catabolite reductase ( RCCR ) [34] , and 4 ) finally to hydroxy-pFCC by TIC55 inside the chloroplast [35] . The conversion from pheophorbide a to RCC leads to the loss of green color of Chl catabolites [34] . The identification of NYE1/SGR1 , among other major CCEs , was an important event , not only because it is responsible for catalyzing the first step of Chl a degradation but more significantly because its mutation is responsible for Mendel’s green cotyledon trait [36–43] . A broad range of endogenous factors as well as environmental cues can modulate the initiation/progression of leaf senescence [22] , and ethylene ( ET ) has been shown to be a key promoter [44–47] . EIN2 and EIN3 predominantly mediate its signaling via a sophisticated regulatory hierarchy [46 , 48–51] . During leaf senescence , EIN3 directly activates the expression of ORE1/NAC2 and NAP , as well as CCGs , to accelerate leaf senescence [52] . EIN3 is also involved in a feed-forward regulation by directly suppressing the expression of miR164 , which targets ORE1 at the post-transcriptional level [46 , 49] . Moreover , ORE1 and NAP directly activate the expression of Senescence associated genes ( SAGs ) and CCGs to accelerate leaf Chl degradation and senescence in general [52–54] . It is important to note that ethylene can promote senescence only in the leaves that have reached a certain age , in which some age-related changes must have occurred [55 , 56] . Yet , the identities of these changes have not been clearly defined , and particularly , the mechanism ( s ) by which the transcription of the genes encoding major senescence regulatory as well as functional SAGs/CCGs is prematurely repressed remains elusive . In this study , we employed the yeast one-hybrid ( Y1H ) system to screen for the putative transcriptional regulator of NYE1 and interestingly , identified REF6 , a histone H3 lysine 27 demethylase , as a candidate . It was then confirmed that REF6 modulates Chl degradation by directly up-regulating the transcription of NYE1/2 . Subsequently , we demonstrated that REF6 also regulates general leaf senescence , and identified other eight senescence regulatory and functional genes ( EIN2 , ORE1 , NAP , PPDK , PAD4 , LOX1 , AtNAC3 , and NTL9 ) as its direct targets . Finally , we showed that REF6 regulates the expression of its ten target SAGs by reducing their H3K27me3 levels . Our study identifies that H3K27me3 methylation represents a kind of epigenetic mechanisms prohibiting the premature activation of leaf senescence in Arabidopsis .
NYE1 was initially identified as a crucial regulator of Chl degradation during green organ senescence in diversified species and particularly shown to be responsible for the green/yellow cotyledon trait of Mendel’s pea ( Pisum sativum ) [36–43] . To understand the transcriptional regulation of NYE1 , we exploited Y1H to screen for the putative trans-regulators of NYE1 . The core part of NYE1’s promoter ( -532 bp upstream of its ATG ) was used as the bait for the screening against a cDNA library generated from the senescing leaves of Arabidopsis plants [57] . To our surprise , a positive clone encoding a zinc finger structure protein with two JMJ domains was identified , which was previously demonstrated to be a histone H3K27me3 demethylase and named as REF6 ( AT3G48430 ) [11] . We then cloned the full-length coding region of REF6 into the vector pGAD-T7 and introduced the resultant construct along with PNYE1::Pabai into Y1H Gold . Y1H Gold grew well on a medium containing Aureobasidin A . To confirm this result , we generated a construct containing the reporter genes HIS3 and LacZ driven by the NYE1’s promoter and introduced it into the yeast YM4271 along with empty pGAD-T7 or REF6::pGAD-T7 . Compared with the yeast transformed with empty pGAD-T7 , the one with REF6::pGAD-T7 grew well in a 3-AT-containing medium and turned blue when transferred to X-Gal-containing medium ( Fig 1A ) . Remarkably , REF6’s targeting mechanism was recently revealed , i . e . REF6 directly binds to the CTCTGYTY motif of its target genes [17] . By scanning NYE1’s promoter and coding regions , we found three CTCTGYTY motifs , and interestingly , we also detected three CTCTGYTY motifs across the promoter and coding regions of NYE2 , a functional paralog of NYE1 [58] . To test whether REF6 protein could directly bind to these motifs in vivo , we carried out chromatin immuneprecipitation ( ChIP ) -qPCR assays with multiple pairs of primers designed accordingly . Chromatins isolated from ref6-1+PREF6::REF6-HA transgenic plants were immuneprecipitated with HA antibody , and RT-qPCR was then performed to quantify the enrichment of corresponding promoter and coding regions . We observed 2 . 6- to 3 . 7-fold enrichments in the NYE1’s promoter region ( NYE1-P3 , -P4 ) , where the first two CTCTGYTY motifs are located , and relatively less enrichment in the first exon ( NYE1-P5 ) , which is close to the third CTCTGYTY motif , in contrast to no enrichment at the end of the coding region ( NYE1-P6 ) , where no CTCTGYTY motifs were detected . An expected enrichment pattern was also observed within NYE2’s promoter and coding regions ( Fig 1B ) . Electrophoretic mobility shift assay ( EMSA ) was carried out to determine whether REF6 protein could directly bind to NYE1/2 promoters in vitro . The C2H2-ZnF domain of REF6 fused with a GST tag ( GST-REF6C 1 , 239–1 , 360 aa ) was expressed and purified as described previously [17]; and a 28 bp DNA fragment covering the first CTCTGYTY motif in NYE1 promoter was made a probe . We detected a shifted band when labeled probes were pre-incubated with GST-REF6C , and addition of excess unlabeled probes competed with the binding . In contrast , REF6 protein did not bind to the motif-mutated probes ( Fig 1C ) , indicating that REF6 specifically bound to the motif of NYE1 promoter in vitro . REF6 protein could also specifically bind to the motif of NYE2 promoter in vitro ( Fig 1D ) . These observations , along with the previous data , collectively suggest that REF6 may act as a transcriptional regulator of NYE1/2 by directly binding to their CTCTGYTY motif-containing regions . To verify the above assumption , we examined the characteristic changes of NYE1/2’s transcriptions in ref6-1 and ref6-1+PREF6::REF6-HA , as well as in Col-0 , during age-triggered and dark-induced leaf senescence . The ref6-1+PREF6::REF6-HA were the native promoter-driven REF6 overexpression plants with ~3 . 0-fold enhancement in its transcription ( S1 Fig ) . Loss-of-function of REF6 significantly reduced the transcription of NYE1/2 , whereas overexpression of REF6 enhanced the transcription of NYE1/2 during both of the scenarios of leaf senescence ( Fig 2A and 2B ) . To examine the role of REF6 in regulating Chl degradation , we first characterized the de-greening phenotype of ref6-1 and ref6-1+PREF6::REF6-HA during age-triggered senescence , with nye1 nye2 and Col-0 plants used as controls . As expected , the rosette leaves of ref6-1 showed an obvious stay-green phenotype as compared with Col-0 , whereas ref6-1+PREF6::REF6-HA exhibited a premature yellowing phenotype , in contrast to the most severe stay-green phenotype of nye1 nye2 ( Fig 2C ) . Measurements of Chl content were consistent with the visual phenotype ( Fig 2D ) . To confirm the regulatory role of REF6 in Chl degradation , their 3rd and 4th rosette leaves were incubated in darkness and characterized four days after dark treatment ( DAD ) . A stay-green phenotype was also observed on the leaves of ref6-1 , in contrast to an obvious premature yellowing on those of ref6-1+PREF6::REF6-HA ( Fig 2E ) . Their phenotypic observations were validated by their measurements of Chl content ( Fig 2F ) . Finally , we checked whether NYE1 overexpression could rescue the stay-green phenotype of ref6-1 by using an in situ transient expression system . We first infiltrated one half of a ref6-1’s leaf with Agrobacteria containing NYE1 expression vector and found that it turned yellow two days after infiltration while the other half of the leaf transfected with Agrobacteria containing the empty plasmid still stayed green ( Fig 2G ) . Then , we overexpressed REF6 in nye1-1 and nye1 nye2 in the same manner and detected similar stay-green phenotypes between the two halves of a leaf transfected ( Fig 2H ) . All the analyses convincingly demonstrate that REF6 requires NYE1/2 for promotion of Chl degradation during leaf senescence . To confirm the above transient expression results , we overexpressed NYE1 in ref6-1 plants ( ref6-1+PiDEX::NYE1 ) and vise versa , REF6 in nye1-1 plants ( nye1-1+PiDEX::REF6 ) by using a dexamethasone ( DEX ) induction system . After treated with 30 μM chemical inducer DEX , ref6-1+PiDEX::NYE1 transgenic plants exhibited a yellowing phenotype compared with the untreated control on 3 DAD ( Fig 3A and 3B and S2A Fig ) , whereas nye1-1+PiDEX::REF6 transgenic plants displayed a stay-green phenotype similar to that of nye1-1 ( Fig 3D and 3E and S2B Fig ) . These findings are consistent with the transient expression results . Interestingly , although Chl degradation was not affected , a significantly decreased Fv/Fm ratio was detected in nye1-1+PiDEX::REF6 transgenic plants compared with un-induced controls ( Fig 3C and 3F ) , which reminded us of that REF6 might also be involved in the regulation of other SAGs apart from NYEs . Subsequently , we examined the major senescence parameters of both REF6’s loss-of-function mutant ( ref6-1 ) and its native promoter-driven overexpression lines ( ref6-1+PREF6::REF6-HA ) during both dark-induced and age-triggered leaf senescence . It was found that , on 4 DAD , the Fv/Fm ratio remained higher while the ion leakage and H2O2 content lower in the 3rd and 4th detached rosette leaves of 25-day-old ref6-1 plants than those in Col-0 , and the exact opposite trends in the changes of the senescence parameters were observed in ref6-1+PREF6::REF6-HA ( Fig 3G–3I ) [59 , 60] . Consistently , 40 days after germination under long-day growth conditions , a significantly higher Fv/Fm ratio and a significantly lower Fv/Fm ratio were detected in the 3rd and 4th rosette leaves of ref6-1 and ref6-1+PREF6::REF6-HA plants , respectively ( S3 Fig ) . These results confirm that REF6 indeed regulates the general leaf senescence process . To examine the effect of loss-of-REF6 function on the global gene expression pattern , we did a comparative RNA-seq analysis of the 10-day-old seedlings and 40-day-old rosette leaves of both ref6-1 and Col-0 plants . In total , 25 , 044 expressed genes were identified from all the samples . PCA analysis showed that in the rosette leaves , the transcriptional difference between Col-0 and ref6-1 was much more significant in comparison to that in the seedlings ( Fig 4A ) : a total of 6 , 106 differentially expressed genes ( DEGs , those with more than 2 . 0-fold change in transcription ) were identified in the rosette leaves compared to 1 , 319 DEGs in the seedlings ( Fig 4B and S1 Table ) . The decline in leaf photosynthetic capacity is correlated with the progression of leaf senescence [61] , and expectedly , a large number of Chlorophyll biosynthesis- and photosynthesis-related genes were down-regulated much less rapidly in ref6-1 than those in Col-0 ( Fig 4C and S2 Table ) . By contrast , among the previously identified 74 SAGs [62] , 33 were up-regulated far less significantly in ref6-1 than those in Col-0 ( Fig 4D and S2 Table ) . These analyses provide evidence that REF6 significantly promotes the general leaf senescence process via up-regulating the transcription of major SAGs . Notably , by referring to the SAGs database set up by Liu et al . [62] , we found that the up-regulated transcription of quite a few of the SAGs relating to ethylene biosynthesis , signaling or response was significantly compromised in the 40-day-old rosette leaves of ref6-1 ( Fig 4E and S2 Table ) , implying that ethylene signaling and/or biosynthesis might be largely responsible for mediating REF6-regulated leaf senescence . To identify the SAGs directly activated by REF6 , we analyzed the overlap between REF6 target genes [11] and the SAGs significantly down-regulated in the 40-day-old rosette leaves of ref6-1 ( S3 Table ) , and revealed three ethylene signaling genes , EIN2 , ORE1 , and NAP [51 , 63] , as well as other five SAGs , PPDK , PAD4 , LOX1 , AtNAC3 , and NTL9 , as REF6 candidate target SAGs . The ethylene signaling pathway has been elucidated as a key hormonal signaling pathway in regulating age-triggered leaf senescence [49 , 51 , 52] , and we therefore focused on the analysis of EIN2 , ORE1 , and NAP’s involvement . To preliminarily examine the regulatory relationship of the three ethylene signaling genes with REF6 , we measured their transcript levels and found that their enhancements in transcription with aging were significantly reduced in ref6-1 while apparently increased in ref6-1+PREF6::REF6-HA compared to those in Col-0 ( Fig 5A ) . The in vivo association of REF6 with the three genes was examined by ChIP-qPCR assays , and it was found that REF6 was associated with all the three genes in their coding regions but not in their promoter regions , with 3 . 0- , 3 . 5- , and 3 . 0-fold enrichments in the coding regions of EIN2 ( EIN2-P17 ) , ORE1 ( ORE1-P22 ) , and NAP ( NAP-P24 ) , respectively ( Fig 5B ) . By scanning their genomic regions , CTCTGYTY motifs were identified mainly in their coding regions instead of their promoters . With the ChIP data as a reference , an EMSA was performed to examine whether REF6 protein could directly bind to the CTCTGYTY motifs in their coding regions . Indeed , bindings were detected within the coding regions of all the three genes ( Fig 5C ) . These results indicate that REF6 modulates leaf senescence likely by directly up-regulating EIN2 , ORE1 , and NAP , and presumably , other five candidate target SAGs as well ( CTCTGYTY motifs were also detected in their coding/promoter regions ( S4A Fig ) . Previous reports revealed that the expression of genes is tightly restricted by a high level of H3K27me3 [6] , and loss-of-function of REF6 caused a genome-wide H3K27me3 hypermethylation [11] . We then hypothesized that REF6 might directly promote the transcription of its target SAGs by reducing their H3K27me3 levels . To test this hypothesis , we measured H3K27me3 level at these SAGs in the 10-day-old seedlings ( Fig 6A and 6B and S4A and S4B Fig ) and 40-day-old leaves ( Fig 6A and 6C and S4A and S4C Fig ) of both ref6-1 and Col-0 plants . It was detected that H3K27me3 levels were significantly higher in ref6-1 than in Col-0 plants at all the ten SAGs , and notably , between ref6-1 and Col-0 , much bigger differences at H3K27me3 level were revealed at ORE1 , NAP , NAC3 , and NTL9 in both the 10-day-old seedlings and the 40-day-old leaves ( Fig 6 and S4 Fig ) . We also found that H3K27me3 levels at all the ten genes were lower in the 40-day-old leaves than those in the 10-day-old seedlings of both Col-0 and ref6-1 plants , suggesting that the H3K27me3 on these SAGs are gradually cleared up by REF6 as well as other related demethylases towards the initiation of leaf senescence . These data demonstrate that REF6 facilitates the expression of its ten target SAGs during the initiation/progression of leaf senescence by reducing their H3K27me3 levels . It has been found that early leaf senescence is always accompanied with early flowering , but delayed leaf senescence does not necessarily cause late flowering [64] . Some histone modification factors were reported to affect both flowering time and leaf senescence process [19 , 20 , 24] . Since ref6-1 is an obvious late-flowering mutant , we wondered whether its delayed leaf senescence is associated with a delayed plant development . To minimize a possible influence of plant development on leaf senescence , the detached rosette leaves of 40-day-old Col-0 and ref6-1 plants ( whole plants were still in vegetative growth ) under short day-growth conditions were dark treated for 4 days to examine their dark-induced senescence phenotypes . It was found that the leaves of ref6-1 plants exhibited an obvious stay-green phenotype compared to those of Col-0 ( Fig 7A ) . The measurements of Chl content ( Fig 7B ) and Fv/Fm ratio ( Fig 7C ) were consistent with the phenotypic observations . Similar results were obtained on 4 DAD with the rosette leaves of 14-day-old plants under long day-growth conditions ( Fig 7D–7F ) , implying that the stay-green phenotype of ref6-1 is independent of plant development . To manifest a direct role of REF6 in promoting leaf senescence , a dexamethasone ( DEX ) -induced expression of REF6 was designed and performed . Rosette leaves of 25-day-old ref6-1+PiDEX::REF6 transgenic plants ( under long day-growth conditions ) were treated with 30 μM chemical inducer DEX , and a stronger yellowing phenotype was observed on 3 DAD , as compared with those of un-induced controls ( Fig 7G–7I and S2C Fig ) . These results convincingly demonstrate that REF6 directly promotes leaf senescence independently of plant development .
Epigenetic modifications , especially histone methylations , have been implicated in the regulatory process of leaf senescence [19 , 65 , 66] . During leaf senescence , genes showing an increase in H3K4me3 mark are up-regulated , while those showing a decrease in H3K4me3 mark are down-regulated . Interestingly , for H3K27me3 modification , the trends are just opposite [65 , 66] . A previous report specifically revealed that the early-senescence activation of WRKY53 , a key regulatory gene of leaf senescence , occurs concomitantly with a significant increase in active H3K4 marks but without a significant change in inactive H3K27 marks at its 5’ end and coding regions [19] . Just immediately before our resubmission , an online paper showed that an H3K4 specific demethylase , JMJ16 , is apparently involved in this process by demethylating H3K4 at WRKY53 as well as SAG201 to prevent precocious leaf senescence in mature leaves [67] . Intriguingly , the ectopic overexpression of SUVH2 , a histone methyltransferase gene , significantly impaired the increase in H3K4 marks at both its 5’ end and coding regions but caused a significant increase in H3K27 marks at its 5’ end , repressing its transcription and consequently delaying leaf senescence [20] . These findings suggest a kind of the involvement of histone methylations in the regulation of leaf senescence . Nevertheless , very little is known about the in vivo details of their involvement in the regulation of developmental leaf senescence . In this study , we identified an H3K27me3 demethylase , REF6 , as a direct transcriptional regulator of NYE1/2 , which is responsible for catalyzing Chl degradation during leaf senescence . Further analyses demonstrated that REF6 is also directly involved in the transcriptional regulation of major senescence regulatory and functional genes , which mediate ethylene signaling ( EIN2 , ORE1 , and NAP ) [51] , abscisic acid/abiotic stress signaling ( AtNAC3 and NTL9 ) [68 , 69] , jasmonic acid ( JA ) biosynthesis ( LOX1 ) [70] , salicylic acid biosynthesis/signaling ( PAD4 ) [71] , and nitrogen remobilization ( PPDK ) [72] during leaf senescence . Consistently , as leaves age , REF6 reduces H3K27me3 level at all the ten genes . The loss-of-function mutation or ectopic overexpression of REF6 significantly alters the initiation/progression dynamics of both Chl degradation and the general leaf senescence . Notably , REF6-upreglated Chl degradation and leaf senescence is independent of the developmental process of the whole plant . Based on the above findings , we for the first time reveal the involvement of a methylation status regulator , REF6 , in the regulation of both Chl degradation and the general leaf senescence process . H3K27 methylation is an important epigenetic modification involved in the regulation of gene expressions . In Arabidopsis , a genome-wide profiling identified 10 to 20% of genes that are marked by H3K27me3 , depending on plant organs or their developmental states [2 , 73] . The absence of REF6 incurs a genome-wide H3K27me3 hypermethylation , implying that REF6 might regulate a variety of growth and developmental processes [11] . Compared with those in 10-day-old seedlings , significant decreases in H3K27me3 level at all the ten genes in the 40-day-old leaves of ref6-1 suggest that REF6 may not be the only demethylase responsible for H3K27me3 demethylation as leaves age . To check whether ELF6 , another identified H3K27me3 demethylase , is possibly involved in this process , the dark-induced senescence phenotypes of elf6-5 and ref6-1 elf6-5 were examined four days after treatment in darkness ( S5 Fig ) . It was found that the elf6-5 mutant showed a similar senescence phenotype to that of Col-0 , and no obvious differences were detected in their senescence phenotype between ref6-1 elf6-5 double mutant and ref6-1 single mutant . The observations suggest that ELF6 might not play a substantial role in regulating Chl degradation and leaf senescence , which is reminiscent of that ELF6 also plays a role different from that of REF6 in regulating flowering [14 , 74] . Further efforts are needed to identify additional demethylase ( s ) responsible for H3K27me3 demethylation during the aging of leaves . SAGs are generally identified by their elevated transcription during senescence . We measured the relative transcript levels of REF6 in the different tissues of Col-0 plants , and found that REF6 had a high transcript level in the flower and capsule ( S6A Fig ) . We also measured its relative transcript levels during the aging of leaves and during dark treatment . The transcription of REF6 increased but not greatly from day 10 to day 25 , even more gently from day 25 to day 40 ( S1 Fig ) ; similarly , during dark-induced senescence , the transcription of REF6 elevated only slightly ( S6B Fig ) . These measurements suggest that REF6 is not a typical kind of SAGs in this regard , but represents a new catalog of SAGs that act to up-regulate the expression of other SAGs , likely through enriching their protein abundance or enhancing their enzymatic activity during senescence . As an enzyme , REF6 was once proposed to be recruited by NF-YA and BES1 onto its targets [15 , 16] . Surprisingly , it was recently revealed that REF6 could directly bind to the CTCTGYTY motif of its targets via its zinc finger domain [17 , 18] , with CUC1 and PIN 1/3/7 being subsequently reported regulated as such [17 , 75] . Here we demonstrate that the binding of REF6 to its targets could be mediated by the CTCTGYTY motifs located not only in the promoters but also in the coding regions ( EIN2 , ORE1 , NAP , PPDK , PAD4 , LOX1 , and NTL9 ) of SAGs . Leaf senescence is initiated with a genome-wide transcriptional reprogramming [63 , 76] , and a large number of transcriptionally-enhanced SAGs have been identified over the last decade or so , some of which were found to function as modules [26 , 77–79] . Nevertheless , a fundamental question remains unanswered , i . e . by which mechanism ( s ) these SAGs are kept transcriptionally silenced before the initiation of senescence . In this study , we show that REF6 directly upregulates ten major SAGs and is responsible for their H3K27me3 demethylation during the aging process of leaves ( Fig 6 and S4 Fig ) , and importantly , loss-of-function of REF6 represses their transcription , whereas overexpression of REF6 enhances their transcription ( Figs 2A , 4D and 5A and S1–S3 Tables ) , consequently causing a change in the dynamics of leaf senescence initiation ( Fig 2C–2F ) . EIN2 , ORE1 , and NAP , interconnected with EIN3 and miR164 , form the framework of a core regulatory module ( Fig 8 ) primarily responsible for the regulation of developmental leaf senescence as well as Chl degradation [46 , 49 , 51 , 52] . Meanwhile , we found that ref6-1 mutant showed insensitivity to ethylene treatment compared with Col-0 ( S7 Fig ) . Our findings suggest that H3K27me3 methylation is an epigenetic mechanism hindering the premature transcriptional activation of key SAGs activated by major phytohormones’ and stresses’ signaling , ethylene signaling in particular . Our findings help to explain the “aging effect” on senescence induction [55 , 56] . The scope and extent of H3K27me3 methylation as a prohibiting mechanism to other SAGs’ premature transcriptions need to be further investigated .
All mutants and transgenic lines were derived from Arabidopsis thaliana ecotype Columbia ( Col-0 ) unless stated otherwise . Generation and identification of nye1 nye2 were carried out as described previously [39 , 58] . The ref6-1 , elf6-5 , ref6-3 ef6-5 mutants and ref6-1+PREF6::REF6-HA plants were kindly provided by Dr . Xiaofeng Cao ( Chinese Academy of Sciences , Beijing ) . To generate nye1-1+PiDEX::REF6 , ref6-1+PiDEX::REF6 , and ref6-1+PiDEX::NYE1 transgenic lines , the full-length coding sequences ( CDS ) of REF6 and NYE1 were PCR amplified and cloned into the vector pTA7002 , respectively . The resultant constructs were introduced into nye1-1 and ref6-1 mutant plants , respectively , by using the floral-dipping method . Seeds were germinated in soil and plants were grown at 23 °C under 16-h light/8-h dark for long day conditions or 16-h dark/8-h light for short day conditions in a growth room equipped with cool-white fluorescent lights ( 90–100 μmol m-2 s-1 ) unless indicated otherwise . The 3rd and 4th rosette leaves from 25-day-old soil-grown plants were incubated in complete darkness as described previously [39] . Y1H screening is performed with the Matchmaker Gold Yeast One-Hybrid Library Screening System ( Clontech ) . The bait fragment ( the -532 bp fragment of NYE1 promoter , [57] ) was amplified by PCR and cloned into the pAbAi vector . The resultant vector was subsequently linearized and introduced into the yeast strain Y1H Gold to generate a bait-reporter strain , which was then used to screen a cDNA library generated from detached leaves incubated in darkness for 12 h . Approximately 5×105 transformants were initially screened out on plates containing SD/-Leu media supplemented with 100 ng/ml Aureobasidin A . Prey fragments were identified from the positive colonies by DNA sequencing . For re-transformation assay , the full-length coding sequence of REF6 was amplified from Col-0 cDNA by use of gene-specific primers ( S4 Table ) . The PCR products were then cloned into the pGADT7 ( Clontech ) prey vector and the resultant vectors were subsequently transferred into the previously mentioned bait-reporter yeast strain . Total RNAs were extracted by using Trizol reagent ( Invitrogen ) , and DNA remnants were removed by Dnase I ( Takara ) treatment . First-strand cDNAs were synthesized with the PrimeScript RT reagent kit ( Takara ) and used as templates for quantitative RT-PCR ( qRT-PCR ) with SYBR Premix Ex Taq TM ( Takara ) . The qPCR analyses were carried out with the MyiQ5 Real Time PCR Detection System ( Bio-Rad , Hercules , CA ) . ACTIN2 ( AT3g18780 ) was used as an internal reference to normalize the qPCR data . Gene-specific primers are listed in S4 Table . Total RNA of the whole seedlings or detached leaves was extracted by using a Trizol kit ( Takara ) . Fifty bp single end RNA-sequencing was conducted by using the BGISEQ-500 platform established by Beijing Genomics Institute , and the reads were aligned by using Bowtie 2 instead of Bowtie [80] . A gene with a cut-off value of two-fold change and p-value less than 0 . 01 was defined as a differentially expressed gene . R program “princomp” was used to conduct PCA analysis . Heatmap . 2 in the ‘gplots’ package of R program was used for the construction of heat maps . An REF6C ( encoding amino acids 1 , 239–1 , 360 ) fragment was cloned into pGEX-6p-1 , and GST-REF6C recombinant fusion protein and GST protein were then expressed in Escherichia coli ( BL21 codon plus , Stratagene ) and purified with glutathione sepharose 4B beads ( GE Healthcare ) . Biotin-labeled DNA probes are listed in S4 Table . Unlabeled competitors were added in 400-fold molar excess . EMSA is carried out with the Light Shift Chemiluminescent EMSA Kit ( Thermo Scientific ) . 20 μl reaction mixture contained 2 μl binding buffer , 0 . 3 μl poly ( dI-dC ) , 4 μg purified fusion protein and 1 μl biotin-labeled annealed oligonucleotides . After incubation at room temperature for 30 min , the reaction mixture was electrophoresed on a 5% polyacrylamide mini-gel ( containing 3% glycerol ) , then transferred onto a positively charged nylon membrane ( Amersham Biosciences ) , which was illuminated by use of an ultraviolet lamp for cross-linking . Biotin-labeled DNA was detected with Pierce chemiluminescence kit ( Thermo Scientific ) . ChIP assay was performed as described previously [17] with slight modifications . For measuring REF6 enrichments: chromatins were isolated from about 2 g of formaldehyde cross-linked rosette leaves from 10-day-old transgenic plants and Col-0 plants . For the determination of H3K27me3 levels , chromatins were isolated from about 2 g of formaldehyde cross-linked rosette leaves of 10-day-old seedlings and 40-day old leaves of Col-0 and ref6-1 plants , respectively , which were then sonicated to produce 0 . 2- to 1-kb DNA fragments with a Branson sonicator ( 40-s bursts at -88 watts ) . The lysates were diluted 10-fold with ChIP dilution buffer to decrease the concentration of SDS to 0 . 1% , which was then cleared by centrifugation ( 16 , 000 g for 15 min at 4 °C ) . After 5% being taken out and used as input , the rest supernatant was incubated with the antibodies for HA ( Sigma , H6908 ) or H3K27me3 ( Millipore , 07–449 ) overnight at 4 °C . Chromatin was collected by using Protein A/G magnetic beads , then washed , eluted , and reverse cross-linked , and DNA purification was then performed . DNA fragments were purified by using the ChIP-qPCR purification kit ( Zymo Research ) . The purified DNA was re-suspended in double-distilled water , and enriched DNA fragments were quantified by qPCR with the primers listed in S4 Table . Input samples were reverse–cross-linked and used to normalize the qPCR data for each ChIP sample . Ethylene treatment was carried out principally as reported [81] . Ethephon was purchased from Shanghai Sangon Biotechnology Co . , and leaves were treated in an air-tight container ( desiccator ) . A1 Methephon stock solution was prepared with ethanol , and 86 . 5 μl of the stock solution were added to 200 ml of 5 mM Na2HPO3 placed in a 17 . 3 L desiccator to create the 5 μM air concentration of ethylene . The cover was closed immediately after addition , and the desiccator was placed under light or in the dark according to the requirement of the experimental design . Before or after a 4-day treatment in the dark , the detached leaves were incubated in deionized water for at least 4 h ( < 10 h ) , and small fractions of the elution water solutions were subsequently taken out for the initial value determination ( C1 ) . The leaf samples were then boiled in the same deionized water for 15 min . After cooling , the elution water solutions were determined again ( C2 ) [49] . The ratio of C1: C2 was calculated as the percentage of ion leakage . We used 3 ml deionized water for one leaf measurement in a 5-ml centrifuge tube . Chl contents were measured by using SPAD-502 PLUS , and maximal photochemical efficiencies of PSII ( Fv/Fm ) were measured with LI-COR6400 ( http://www . licor . com/env/products/photosynthesis ) according to manufacturer’s instructions . Data are given as mean ± SD and were analyzed by Student’s t test or one-way ANOVA . NYE1 ( AT4G22920 ) , NYE2 ( AT4G11910 ) , EIN2 ( AT5G03280 ) , ORE1/NAC2/NAC092 ( AT5G39610 ) , NAP ( AT1G69490 ) , REF6 ( AT3G48430 ) , ACTIN2 ( AT3G18780 ) , UBQ ( AT4G05320 ) , GPAT4 ( AT1G01610 ) , NAC004 ( AT1G02230 ) , TUB ( AT5G62690 ) , PAD4 ( AT3G52430 ) , PPDK ( AT4G15530 ) , LOX1 ( AT1G55020 ) , NAC3 ( AT3G15500 ) , NTL9 ( AT4G35580 ) . RNA-seq data obtained in this study were deposited at the NCBI short read archive under Bioproject identifier PRJNA518728 with accession number: SRR8518110 to SRR8518121 .
|
Leaves of higher plants start yellowing and subsequently die ( senescence ) at particular developmental stages as a result of both internal and external regulations . Leaf senescence is evolved to facilitate nutrient remobilization to young/important organs to meet their rapid development , and a large number of genes ( Senescence associated genes , SAGs ) are activated to regulate/facilitate the process . It has been intriguing how these genes are kept transcriptionally inactive to ensure an effective photosynthesis before the initiation of leaf senescence . Here , we reveal an epigenetic mechanism responsible for the prohibition of their premature transcription . We found that an H3K27me3 demethylase , RELATIVE OF EARLY FLOWERING 6 ( REF6 ) , directly promotes the expression of its ten target senescence regulatory and functional genes ( EIN2 , ORE1 , NAP , AtNAC3 , NTL9 , NYE1/2 , LOX1 , PAD4 , and PPDK ) , which are involved in major phytohormones’ signaling , biosynthesis , and chlorophyll degradation . Crucially , REF6 is substantially involved in promoting the H3K27me3 demethylation of both their promoter and/or coding regions during the aging process of leaves . We therefore provide conclusive evidence that H3K27me3 methylation is an epigenetic mechanism hindering the premature transcriptional activation of key SAGs , which helps to explain the “aging effect” of senescence initiation .
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2019
|
The H3K27me3 demethylase REF6 promotes leaf senescence through directly activating major senescence regulatory and functional genes in Arabidopsis
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Epidemiology and candidate gene studies indicate a shared genetic basis for celiac disease ( CD ) and rheumatoid arthritis ( RA ) , but the extent of this sharing has not been systematically explored . Previous studies demonstrate that 6 of the established non-HLA CD and RA risk loci ( out of 26 loci for each disease ) are shared between both diseases . We hypothesized that there are additional shared risk alleles and that combining genome-wide association study ( GWAS ) data from each disease would increase power to identify these shared risk alleles . We performed a meta-analysis of two published GWAS on CD ( 4 , 533 cases and 10 , 750 controls ) and RA ( 5 , 539 cases and 17 , 231 controls ) . After genotyping the top associated SNPs in 2 , 169 CD cases and 2 , 255 controls , and 2 , 845 RA cases and 4 , 944 controls , 8 additional SNPs demonstrated P<5×10−8 in a combined analysis of all 50 , 266 samples , including four SNPs that have not been previously confirmed in either disease: rs10892279 near the DDX6 gene ( Pcombined = 1 . 2×10−12 ) , rs864537 near CD247 ( Pcombined = 2 . 2×10−11 ) , rs2298428 near UBE2L3 ( Pcombined = 2 . 5×10−10 ) , and rs11203203 near UBASH3A ( Pcombined = 1 . 1×10−8 ) . We also confirmed that 4 gene loci previously established in either CD or RA are associated with the other autoimmune disease at combined P<5×10−8 ( SH2B3 , 8q24 , STAT4 , and TRAF1-C5 ) . From the 14 shared gene loci , 7 SNPs showed a genome-wide significant effect on expression of one or more transcripts in the linkage disequilibrium ( LD ) block around the SNP . These associations implicate antigen presentation and T-cell activation as a shared mechanism of disease pathogenesis and underscore the utility of cross-disease meta-analysis for identification of genetic risk factors with pleiotropic effects between two clinically distinct diseases .
Autoimmune disorders , including rheumatoid arthritis ( RA ) and celiac disease ( CD ) , affect about 5% of the population and have a complex genetic background . Family-based epidemiology studies suggest that there is a shared genetic basis between the two autoimmune diseases [1] . Recent genome-wide association studies ( GWAS ) have confirmed HLA and identified at least 26 other non-HLA genetic loci with common alleles associated to each disease ( Table S1 and S2 ) [2] , [3] . The strongest genetic risk factor is the HLA locus [2] , [3] , where different alleles confer risk of the two diseases . Six other risk loci outside of the HLA locus are shared between CD and RA and include MMEL/TNFRSF14 [2] , [4] , REL [2] , [5] , [6] , ICOS-CTLA4 [2] , [3] , [5] , [7] , IL2-IL21 [2] , [3] , [8] , [9] , [10] , TNFAIP3 [2] , [3] , [6] , [11] , and TAGAP [2] , [3] , [8] , ( Chen et al , submitted ) ( Table 1 and Figure 1 ) . These shared risk loci have emerged by simple cross-comparison across published studies , rather than a rigorous and systematic analysis of an integrated dataset . Because of the nature of these reports , it is unknown whether the other CD and RA risk alleles confer risk of both diseases . Moreover , it is unknown whether there are additional shared risk alleles that have not yet been discovered in any one disease . A major challenge in identifying common alleles of modest effect is the sample size required to have sufficient power to obtain associations at a stringent level of statistical significance . Recent studies of height [12] , lipids [13] and body mass index [14] have shown quite convincingly that very large sample sizes – more than 100 , 000 individuals – yield reproducible SNP associations for common alleles of modest effect size . For diseases such as CD and RA , which are relatively uncommon in the general population ( prevalence ∼0 . 5–1% for each disease ) , similar sized cohorts are difficult to ascertain . One solution to this problem is to combine two phenotypes to search for pleiotropic risk alleles . So far this approach has only been done for closely related phenotypes , such as the Crohn's disease and ulcerative colitis ( together known as inflammatory bowel disease ( IBD ) ) [15] , or for medical traits that are known risk factors for disease ( e . g . , lipids and coronary artery disease , obesity and type 2 diabetes ) [13] , [16] . Another challenge is how to interpret statistical significance of SNP associations in combined analysis of two clinically distinct phenotypes . In a GWAS of common variants for a single phenotype , most consider P<5×10−8 as statistically significant , as any SNP at random from the genome has the same probability of being associated with the phenotype and there are approximately 1 million uncorrelated common SNPs in the human genome [17] . However , this P-value threshold does not take into consideration that ( a ) many common SNPs , not just a single SNP , are associated with disease , and ( b ) the pleiotropy of risk alleles for related diseases should , in theory , increase the prior probability that an allele is a true-positive . In the case of autoimmunity , alleles often contribute to risk of more than one autoimmune disease [18] . Accordingly , a SNP with a confirmed association in one autoimmune disease has a higher prior probability of being associated with another autoimmune disease . This principle has been used to declare that SNPs are confirmed disease associations , if the SNP does not reach a stringent level of significance ( e . g . , P<5×10−8 ) in the other autoimmune disease [7] , [19] . Nonetheless , there are no formal criteria for assigning increased prior probabilities for SNPs across autoimmune diseases . In the current study , we hypothesized that there are additional alleles that influence risk of both CD and RA in a pleiotropic manner . To increase power to detect these alleles , we combined two previously published GWAS of each disease , followed by replication in both CD and RA . We use our GWAS data to arrive at an empirical threshold for declaring SNPs as shared risk alleles for the two diseases . In doing so , we identified fourteen shared CD-RA risk alleles , which point to T-cell receptor signaling as a key shared pathway of disease pathogenesis .
We first aimed to investigate the status of established CD and RA loci across these two diseases using genotype data from published GWAS datasets of CD ( 4 , 533 cases , 10 , 750 controls ) [2] and RA ( 5 , 539 autoantibody positive RA cases and 17 , 231 controls ) [3] ( See Materials and Methods for description of both cohorts ) . We considered only those reported loci with at least one risk allele associated at P<5×10−8 with confirmation in independent samples . There are 26 non-HLA loci from each disease that satisfy this stringent criterion , representing 46 distinct risk loci ( Tables S1 and S2 ) . We investigated the association of the 26 non-HLA CD SNPs in RA , and the 26 non-HLA RA SNPs in CD . Figure 1A and 1B show the OR and 95% CI of the 52 SNPs and the association statistics within the two diseases . Of the 26 CD SNPs , 11 are associated with risk of RA at P<0 . 05 ( Table S1 ) . Similarly , from 26 RA SNPs , 9 are associated with risk of CD at P<0 . 05 ( Table S2 ) . After excluding the six loci established in both diseases , this distribution remains non-random ( P<2×10−4 , Fisher's test ) , indicating additional sharing of risk loci between the two diseases . To provide additional evidence that there are shared risk alleles , we analyzed the distribution of moderately associated SNPs from the GWAS datasets ( i . e . , putative risk alleles ) across the two autoimmune diseases . We investigated whether the subset of SNPs associated with CD at P<0 . 001 in the CD-GWAS are randomly distributed in the RA GWAS results , and vice-versa . After removing the established CD and RA risk loci , we performed association analysis on a set of independent SNPs for each disease . In CD , 70 , 520 SNPs remained after pruning SNPs in linkage disequilibrium ( LD ) ( see Materials and Methods for details ) , of which 342 were associated with CD at P<0 . 001 . In RA , 70 , 812 SNPs remained after LD-pruning , of which 282 were associated with RA at P<0 . 001 . Using Fisher's test , we observed a non-random distribution of association with CD in the subset of P<0 . 001 RA GWAS SNPs , as well as a non-random distribution of association with RA in the subset of P<0 . 001 CD GWAS SNPs ( P<5×10−5 for both diseases; see Figure 2 and Table S3A ) . Similar results were obtained when we used the Wilcoxon rank sum and Kolmogorov-Smirnov tests to analyze the distributions of SNP associations across diseases ( Table S3B ) . From this analysis , we conclude that a SNP associated with risk of CD at P<0 . 001 has an increased prior probability of being associated with RA , and a SNP associated with risk of RA at P<0 . 001 has an increased prior probability of being associated with CD . While the analyses described above indicate that additional shared risk alleles remain to be discovered , these analyses do not identify which specific SNPs influence risk of both disease . To identify new shared risk alleles , we performed an inverse variance weighted meta-analysis [20] in which we assumed that the same allele confers risk of both diseases . A total of 472 , 854 SNPs outside the HLA ( Chr6: 20–40 MB ) overlapped between the two GWAS datasets and were included in the meta-analysis . We did not exclude the established CD and RA loci outside of the HLA region from the meta-analysis , as we considered the possibility that there may be novel risk alleles within these loci . The Q-Q plot of CD+RA meta-anlaysis P-values ( Pcombined ) shows an enrichment of non-HLA associated SNPs in the tail of the distribution ( Figure 3A ) , with no evidence for systematic bias across all SNPs ( λGC = 1 . 011 ) . A similar result was obtained after excluding known associated loci for both diseases ( Figure 3A ) . The Manhattan plot indicates loci where significance increased in the combined cohort ( Figure S1 ) . Sixty-five SNPs from 21 distinct genomic regions were associated with both CD and RA in the combined analysis with Pcombined<1×10−5 , and with disease-specific P<0 . 01 ( Tables S4 and S5 ) . Of these 21 loci , five are established in both diseases ( TNFAIP3 , CTLA4/ICOS , IL2/IL21 , REL and MMEL1/TNFRSF14 ) ; five are established CD loci ( SH2B3 , PTPN2 , 8q24 . 2 , SOCS1 , ICOSLG ) ; and four are established RA loci ( ANKRD55 , STAT4 , TRAF1/C5 and PRKCQ ) . The remaining 7 have not been previously confirmed in either disease ( Table 2 , Table S5 ) . To determine which of these loci are associated with both diseases – particularly those 7 loci not previously implicated in either disease and 9 loci established as risk alleles in either CD or RA alone – we selected from each of these 16 loci one most associated SNP for replication in additional 2 , 169 CD cases and 2 , 255 controls , and 2 , 845 autoantibody positive RA cases and 4 , 944 controls ( see Materials and Methods for sample information ) . Five out of 16 SNPs were previously genotyped in samples that overlapped with our replication samples [2] , [3] , and are included here for completeness . Two SNPs – rs7283760 in the CD-established ICOSLG locus and rs2181622 in the RA-established PRKCQ locus – were not genotyped in the replication samples for technical reasons . We did not attempt replication of SNPs from the five established loci associated with risk of both CD and RA . We conducted association tests of the 14 SNPs in the replication and combined cohorts with inverse variance weighted meta-analysis , where we analyzed CD-only samples [replication ( PCD-repl ) and GWAS+replication ( PCD ) ] , RA-only samples [replication ( PRA-repl ) and GWAS+replication ( PRA ) ] , and RA+CD samples [all GWAS+replication samples together ( Poverall ) ] . As shown in Table 2 , of the 4 established CD risk SNPs , two replicated in the RA samples with PRA-repl<0 . 05 and obtained PRA<0 . 001 in all available RA case-control samples ( SH2B3 ( 12q24 . 1 ) and an intergenic region on 8q24 . 2 , PRA = 1 . 5×10−5 and 9 . 1×10−5 respectively ) . Similarly , of the 3 established RA risk SNPs tested in our study , two replicated in the CD samples with PCD-rep<0 . 05 and obtained PCD<0 . 001 in all available CD case-control samples ( STAT4 ( 2q32 . 3 ) and TRAF1-C5 ( 9q33 . 2 ) , PCD = 9 . 7×10−4 and 9 . 3×10−4 respectively ) . All four of these SNPs have Poverall<5×10−8 in analysis of all 50 , 266 CD and RA samples . Of the 7 SNPs not previously established as genome-wide significant in either CD or RA , four were significantly replicated in both diseases at PCD-repl<0 . 05 and PRA-repl<0 . 05 , were associated to each disease with PCD<0 . 001 and PRA<0 . 001 and achieved Poverall<5×10−8 in the combined CD-RA cohort ( CD247 ( 1q24 . 2 ) , UBE2L3 ( 22q11 . 2 ) , DDX6 ( 11q23 . 3 ) and UBASH3A ( 21q22 . 3 ) ; see Table 2 ) . The strongest signal in the combined analysis was observed from the DDX6 locus ( rs10892279 , Poverall = 1 . 2×10−12 ) . This SNP achieved genome-wide significance PRA = 1 . 1×10−8 in the RA cohort alone , and PCD = 2 . 0×10−5 in the CD cohort . SNPs near CD247 and UBE2L3 were previously suggestively associated in both CD and RA [2] , [3] . The replication data presented here , together with the combined analysis of Poverall<5×10−8 , demonstrate that these SNPs are indeed true positive associations for CD and RA . Of note , SNPs in the UBE2L3 are also associated with risk of systemic lupus erythematosus [21] and Crohn's disease [22] , and the CD247 locus is associated with systemic sclerosis [23] . There is increasing evidence that alleles conferring risk of one autoimmune disease confer protection to another autoimmune disease [3] , [7] , [8] , [24] , [25] , [26] . We therefore performed an analysis of alleles that conferred risk in either CD or RA , but protection in the other disease ( Figure 3B ) , followed by independent testing in our replication cohort . Nine loci were identified using the same criteria as above ( Pcombined<1×10−5 , and disease-specific P<0 . 01; see Tables S6 and S7 ) . The strongest shared signal from this analysis was at the TAGAP locus ( 6q25 . 3 , rs212388 Pcombined = 5 . 4×10−12 ) , an established risk locus in both CD and RA [2] , [3] , [8] ( Chen , et al , submitted ) . Another locus that had an apparent opposite allelic effect was REL ( 2p16 . 1 ) , although it shows a more complex pattern of association . From the three SNPs in the REL locus that were associated to both diseases with Pcombined<1×10−5 , and disease-specific P<0 . 01 , two SNPs showed similar direction of association with CD and RA , whereas one SNP showed opposite directionality of association ( Tables S4 and Table S6 ) . Of the remaining SNPs , no single SNP replicated in both diseases at P<0 . 05 and achieved P<5×10−8 in an overall analysis of all data . We observed a trend of an association at the chromosome 2p23 . 1 ( near the LBH gene ) locus ( rs7579944 , PCD = 9 . 7×10−6 and PRA = 2 . 3×10−4 in the CD and RA cohorts , respectively; Poverall = 1 . 1×10−8 in the combined analysis , but no formal replication in RA cohort ( PRA-repl = 0 . 13 ) ) ( Table 3 ) . Although these data strongly suggest that chromosome 2p23 . 1 is a shared CD-RA risk locus , additional replication will be required . We used two methods to identify the most likely causal gene in the region of the 14 shared non-HLA risk loci . First , we used a computational algorithm , GRAIL , which systematically searches for gene relationships across risk loci using PubMed abstracts [27] . In total , 14 shared loci contain 51 genes; 16 of these scored P<0 . 1 by GRAIL ( Table S8 ) . Second , we analyzed each shared SNP for evidence of cis-acting gene expression in peripheral blood cells derived from 1 , 469 individuals ( Fehrmann et al , submitted ) . From 14 shared SNPs , 7 showed a significant ( genome-wide FDR corrected <0 . 05 ) effect on expression of one or more transcripts in the LD block around the SNP ( Table S9 , Figure S2A-S2P ) . It is interesting to note that of the four novel SNP associations identified from this study , three show convincing effects on the expression of nearby genes , in particular rs864537-CD247 ( P = 3 . 5×10−11 ) , rs2298428-UBE2L3 ( P = 2 . 0×10−99 ) and rs11203203-UBASH3A ( P = 8 . 7×10−10 ) ( Table S9 , Figure S2 ) . Based on these two methods , 23 genes located in the 14 shared loci were selected as plausible candidates for shared CD-RA pathogenesis ( Table 4 and Table S10 ) .
In this study we demonstrate that there are 14 loci that contribute to risk of both RA and CD: 6 previously established risk loci and 8 loci identified in our study . Of the 8 new loci , 4 had not been associated previously with either disease at genome-wide significance ( CD247 , UBE2L3 , DDX6 , and UBASH3A ) and 4 had been established in one but not the other autoimmune disease ( SH2B3 , 8q24 . 2 , STAT4 , and TRAF1-C5 ) . Our study represents the first systematic effort to compare the genetic basis of CD and RA in a very large sample set – more than 50 , 000 combined case-control samples – to identify risk alleles with pleiotropic effects on two clinically distinct autoimmune diseases . To identify the shared risk loci , we performed two types of analyses . First , we compared the distribution of established and putative risk alleles across both autoimmune diseases . Both distributions were non-random , providing empirical evidence that the genetic basis of the two autoimmune diseases overlaps . Second , we combined GWAS data and performed independent replication to search for specific SNPs associated with both diseases . We performed the GWAS meta-analysis under a genetic model in which the same allele conferred risk of both autoimmune diseases , as well as a model in which the same allele conferred risk to one disease and protection from the other disease . Of the newly identified 8 shared risk alleles , all 8 confer same risk direction on both CD and RA . Our study represents one of the first GWAS meta-analysis of clinically distinct but epidemiologically related diseases . This approach has appeal for diseases in which there is thought to be a shared genetic basis , as it adds power to detect alleles of modest effect size . A GWAS meta-analysis has been conducted on early onset inflammatory bowel disease ( IBD ) , which include Crohn's disease ( CrD ) and ulcerative colitis ( UC ) [15] . CrD and UC are clinically similar diseases both affecting bowel , and often can not be distinguished between each other ( presented as undifferentiated IBD ) , especially in children . In contrast to the IBD study , our GWAS meta-analysis combined phenotypes with different clinical presentations ( enteropathy and inflammatory arthritis ) . In combining GWAS data across clinically distinct phenotypes , an important question is how to interpret statistical significance and therefore how to declare a SNP as a confirmed association for each disease . In our study , we empirically demonstrated that SNPs associated with risk of either CD or RA have a higher probability of being associated with the other autoimmune – even if the SNP is not yet a confirmed association in either disease ( Figure 2 ) . We observed that a SNP associated with risk of CD at P<0 . 001 has an increased prior probability of being associated with RA , and a SNP associated with risk of RA at P<0 . 001 has an increased prior probability of being associated with CD . Based upon these analyses , we propose objective criteria for declaring a SNP as a shared CD – RA risk SNP in our study: it must achieve Poverall<5×10−8 in combined analysis of CD&RA , with the additional requirement of P<0 . 05 in an independent replication dataset and P<0 . 001 for each disease . Applying these criteria to our meta-analysis results we conclude that there are 14 non-HLA shared CD and RA risk loci ( Table 1 and Table 2 ) . We applied two methods to select the most likely causal gene in the region of the 14 shared non-HLA risk loci , and in doing so gain insight into shared RA-CD pathogenesis: ( 1 ) a computational algorithm , GRAIL , which systematically searches for gene relationships across risk loci using PubMed abstracts [27] and ( 2 ) a dataset of cis-acting gene expression in peripheral blood cells derived from 1 , 469 individuals [27] ( Fehrmann et al , submitted ) . Using these methods we prioritized 23 genes located in the 14 shared loci as plausible functional candidates . Interestingly , two out of four novel loci function in T-cell activation/signalling: CD247 , which encodes for the zeta chain of the T-cell receptor-CD3 complex , and UBASH3A , which is a suppressor of T-cell receptor signaling , underscoring antigen presentation to T-cells as a critical shared mechanism of disease pathogenesis [28] , [29] . This observation is consistent with the known functions of several of the other shared RA-CD risk loci which were highlighted in GRAIL and expression analysis ( CTLA4 , ICOS , TAGAP , SH2B3 , and STAT4 ) . These genes are known to modulate T-cell activation and/or differentiation: CTLA4 is a negative regulator of T-cell activation [30] , ICOS is a T-cell co-stimulator molecule [31] , TAGAP is up-regulated upon T-cell activation[32] , SH2B3 ( LNK ) is an adaptor protein involved in T-cell activation [33] , and STAT4 is transcription factor important in differentiation of T helper cells [34] . How might these 14 loci influence risk of two clinically distinct autoimmune diseases ? MHC class II alleles , the strongest risk factor in both diseases , are notably different between the two diseases: HLA-DQ*A1 and *B1 alleles in CD and HLA-DRB1 “shared epitope” alleles in RA . Under a model in which MHC class II molecules confer risk by preferentially presenting disease-specific antigens ( gluten in CD , most likely citrullinated antigens in RA ) to autoreactive T-cells , then disease specificity is determined in large part by the inheritance of specific HLA alleles and exposure to disease-specific antigens . Our genetic data extends this model to implicate downstream signaling events common to both diseases that may lead to altered T-cell activation and differentiation . Whether abnormal T-cell signaling occurs in the thymus ( where autoreactive T-cells undergo negative selection ) , in the peripheral circulation ( where autoreactive T-cells exert their effects ) , or in another manner remains to be determined . There are several limitations of our study . First , we did not search for loci in which an allele contributes to risk of one autoimmune disease and an independent allele contributes to risk of the other autoimmune disease . The REL locus provides an example in which the risk alleles for the two autoimmune diseases appear distinct [2] , [5] , [6] . Second , our study is underpowered to detect shared risk alleles of more modest effect size , despite a combined sample size of >50 , 000 case-control samples . As more samples and SNPs are genotyped between these diseases , additional risk alleles will be discovered . Third , we did not attempt to fine-map the 26 established risk loci for both autoimmune diseases to determine if a single allele is responsible for risk in both autoimmune diseases . And fourth , we made no attempt to search for low-frequency or rare variants that are shared between RA and CD . Implementation of newer sequencing technologies will be required to search for rare risk variants . In summary , our study adds four novel loci to established RA and CD risk loci ( CD247 , UBE2L3 , DDX6 , and UBASH3A ) . It also adds four loci previously established in one or the other disease to the list of shared CD-RA risk loci ( SH2B3 , 8q24 . 2 , STAT4 , and TRAF1-C5 ) . With six previously established CD-RA risk loci , there are now 14 shared CD-RA risk loci , out of 50 established loci for either of the two autoimmune diseases . We emphasize that these are conservative estimates of shared risk loci between the two diseases , as our study may be underpowered to detect common alleles of modest effect size , and we have not considered genetic models in which different alleles within one locus contribute to risk of the two diseases . In addition to the HLA associations , these shared risk loci clearly point to the critical role of antigen presentation via MHC class II molecules to the T-cell receptor , and subsequent activation and differentiation of T-cells in shared disease pathogenesis .
Institutional review boards at each collection site approved the study , and all individuals gave their informed consent . Replication analysis of 15 SNPs was performed on the Sequenom iPlex platform in three centers – ( 1 ) Broad institute ( all CD cases and controls , and RA replication cohorts R1 and R2 ) ; ( 2 ) Celera Diagnostics ( Alameda California , USA; RA replication cohort R3 and R4 ) ; and ( 3 ) National Institute of Arthritis and Musculoskeletal and Skin Diseases ( NIAMS , RA replication cohort R5 ) . ( See Table S11 for details ) . If the SNPs could not be designed into the iPLEX pool , then a proxy SNP was included . Information on the iPLEX design , proxies and cohorts genotyped in different centers is presented in Tables S11 and S12 . We excluded SNPs in each replication collection if they were missing >10% genotype data , <1% MAF and PHWE<10−3 . For 5 out of the 20 SNPs that satisfied the replication criteria in either the directional or opposite allelic effect analysis , replication results were already available for CD and RA samples from the studies Dubois et al [2] and Stahl et al[3] , respectively . For these 5 SNPs , we included genotype data from all replication samples available in these studies .
|
Celiac disease ( CD ) and rheumatoid arthritis ( RA ) are two autoimmune diseases characterized by distinct clinical features but increased co-occurrence in families and individuals . Genome-wide association studies ( GWAS ) performed in CD and RA have identified the HLA region and 26 non-HLA genetic risk loci in each disease . Of the 26 CD and 26 RA risk loci , previous studies have shown that six are shared between the two diseases . In this study we aimed to identify additional shared risk alleles and , in doing so , gain more insight into shared disease pathogenesis . We first empirically investigated the distribution of putative risk alleles from GWAS across both diseases ( after removing known risk loci for both diseases ) . We found that CD risk alleles are non-randomly distributed in the RA GWAS ( and vice versa ) , indicating that CD risk alleles have an increased prior probability of being associated with RA ( and vice versa ) . Next , we performed a GWAS meta-analysis to search for shared risk alleles by combing the RA and CD GWAS , performing both directional and opposite allelic effect analyses , followed by replication testing in independent case-control datasets in both diseases . In addition to the already established six non-HLA shared risk loci , we observed statistically robust associations at eight SNPs , thereby increasing the number of shared non-HLA risk loci to fourteen . Finally , we used gene expression studies and pathway analysis tools to identify the plausible candidate genes in the fourteen associated loci . We observed remarkable overrepresentation of T-cell signaling molecules among the shared genes .
|
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2011
|
Meta-Analysis of Genome-Wide Association Studies in Celiac Disease and Rheumatoid Arthritis Identifies Fourteen Non-HLA Shared Loci
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Vector-borne diseases are major public health concerns worldwide . For many of them , vector control is still key to primary prevention , with control actions planned and evaluated using vector occurrence records . Yet vectors can be difficult to detect , and vector occurrence indices will be biased whenever spurious detection/non-detection records arise during surveys . Here , we investigate the process of Chagas disease vector detection , assessing the performance of the surveillance method used in most control programs – active triatomine-bug searches by trained health agents . Control agents conducted triplicate vector searches in 414 man-made ecotopes of two rural localities . Ecotope-specific ‘detection histories’ ( vectors or their traces detected or not in each individual search ) were analyzed using ordinary methods that disregard detection failures and multiple detection-state site-occupancy models that accommodate false-negative and false-positive detections . Mean ( ±SE ) vector-search sensitivity was ∼0 . 283±0 . 057 . Vector-detection odds increased as bug colonies grew denser , and were lower in houses than in most peridomestic structures , particularly woodpiles . False-positive detections ( non-vector fecal streaks misidentified as signs of vector presence ) occurred with probability ∼0 . 011±0 . 008 . The model-averaged estimate of infestation ( 44 . 5±6 . 4% ) was ∼2 . 4–3 . 9 times higher than naïve indices computed assuming perfect detection after single vector searches ( 11 . 4–18 . 8% ) ; about 106–137 infestation foci went undetected during such standard searches . We illustrate a relatively straightforward approach to addressing vector detection uncertainty under realistic field survey conditions . Standard vector searches had low sensitivity except in certain singular circumstances . Our findings suggest that many infestation foci may go undetected during routine surveys , especially when vector density is low . Undetected foci can cause control failures and induce bias in entomological indices; this may confound disease risk assessment and mislead program managers into flawed decision making . By helping correct bias in naïve indices , the approach we illustrate has potential to critically strengthen vector-borne disease control-surveillance systems .
The primary prevention of most vector-borne diseases depends on averting contact between humans and pathogen vectors [1] . In turn , vector control often relies on the detection and elimination of infestation foci , particularly when the vectors occur in or around human residences . This is the case , for example , of the Aedes mosquito vectors of dengue and other arboviruses [1] , [2] or of the triatomine bug vectors of Trypanosoma cruzi , the agent of Chagas disease – the most important human parasitic disease in the Americas ( see refs . [1] , [3] , [4] and http://www . who . int/mediacentre/factsheets/fs340/en/ ) . Since undetected vector foci usually cannot be eliminated , the effectiveness of vector-detection methods can have a strong influence on our ability to prevent new disease cases . In addition , measures of vector occurrence in or around houses ( ‘infestation’ and related indices ) are among the principal indicators used in disease risk assessment and vector control program management – including intervention design , planning , implementation , operation , and evaluation [1]–[3] . Developing and running sound vector-borne disease prevention programs therefore demands a reliable understanding of the vector-detection process; however , few quantitative studies have fully addressed this issue in realistic field settings . Particularly critical is knowledge about the sensitivity and specificity of the methods used to determine infestation in control-surveillance systems [5]–[9] . In this context , sensitivity is defined as the probability of detecting at least one vector in a site ( e . g . , a house or any other discrete ‘ecotope’ such as a corral , henhouse , catch basin , or palm-tree ) that is actually infested; more generally , sensitivity is the probability of detection , conditioned on occurrence [5]–[9] . If sensitivity is less than 1 . 0 ( <100% ) , some sites will be classified as non-infested despite being , in fact , infested – i . e . , there will be some false-negative results in the record database and infestation indices will be biased low [7] , [8] . Specificity is , in turn , the probability of declaring non-infested a sampling unit where the vectors indeed do not occur; that is , the probability of non-detection , conditioned on non-occurrence . If this probability is less than 1 . 0 , some sites will be classified as infested when they are not – i . e . , there will be some false-positive results in the record database , which will induce positive bias in infestation indices [9] . The probability of obtaining a false-positive result equals 1− specificity . Although false-positive results are unlikely to be common in vector surveys , they may possibly arise because of taxonomic errors ( say , a non-triatomine reduviid nymph misidentified as a triatomine bug , or a non-vector sandfly species as a vector species ) or , more easily , when indirect signs of infestation are used as proxies of vector presence ( e . g . , triatomine bug fecal streaks , which may be confused with those of other arthropods [10]–[12] ) or when householders' reports of vector presence in dwellings are not confirmed by actually examining the insects ( e . g . , ref . [13] ) . In addition to estimating sensitivity and specificity , researchers and program managers may be interested in knowing how these key parameters vary in response to independent variables . For example , we may wish to know whether and to what extent the sensitivity of a vector-detection method is affected by the characteristics of vector hiding/breeding sites ( i . e . , ecotope traits ) , by the awareness of vector control agents , or by differences in vector abundance among ecotopes or over time . This latter possibility is particularly relevant in areas undergoing vector control , because the expected effect of control activities is to reduce infestation prevalence , with foci becoming rarer , and vector population density , with foci becoming smaller . In turn , these effects may be expected to reduce the sensitivity of any vector-detection method: rarer and smaller foci will probably be harder to detect [14]–[17] . Unfortunately , no gold-standard vector-detection method ( with 100% sensitivity and 100% specificity ) is currently available . In the case of Chagas disease vectors , demolition of houses or other man-made structures could perhaps reach near-perfect performance [14] , but this option has little practical relevance; as a rule , more sensitive methods are more costly [5] , [16] . In this paper , we adopt a different approach based on repeated-sampling of individual ecotopes and the hierarchical site-occupancy models developed by Miller et al . [9] , which explicitly accommodate false-negative and false-positive results . This allows us to investigate the sensitivity and specificity of active triatomine-bug searches by trained staff ( the standard method used in routine surveillance ) with unprecedented detail . We quantify how vector-search sensitivity varies with observed vector density and across ecotope types while adjusting for possible effects of our sampling scheme . We show that triatomine-search specificity is more than acceptable , but sensitivity is overall low and can vary widely , leading to negatively-biased naïve infestation indices that can seriously threaten vector control program management and , ultimately , disease prevention .
This study is part of a research program on Chagas disease eco-epidemiology approved by Fiocruz's Institutional Review Board ( CEP/Fiocruz protocol 139/01 ) and Committee for Animal Research ( CEUA/Fiocruz protocol P59-12-2 ) and by the Brazilian Environmental Agency ( IBAMA/Sisbio protocol 14323-6 ) . All householders provided informed consent prior to dwelling inspections . We studied two neighboring areas in the lower Jaguaribe valley ( state of Ceará , Brazil ) , where dwelling infestation by triatomine bugs is common and Chagas disease a significant public health concern [18]–[21] . These areas belong , respectively , to the municipalities of Russas and Jaguaruana; while geographically close and ecologically similar , our study localities have some contrasting characteristics . In Russas ( ∼4°56′S , 37°55 . 5′W ) we studied a rural area consisting of several dwelling clusters plus some isolated dwelling compounds; this area lies close to the main ( paved ) road and is 4 km from the municipality's main town . The landscape is heavily anthropogenic , with small agricultural plots and a few patches of Caatinga xeric shrubland . In Jaguaruana ( ∼4°52′S , 37°52′W ) , the study area is 8–10 km from the municipality's main town , the original Caatinga vegetation is overall better preserved , and dwelling compounds are more spatially scattered; a detailed description of this area can be found in ref . [21] . Our sampling units were all individual ecotopes within each dwelling compound . An ecotope was defined as any man-made discrete structure where triatomine bugs might find shelter; a typical dwelling compound had about 5–6 such ecotopes ( mean 5 . 75 , median 5 . 5 , range 2–12 ) including the house and several further structures ( see Table 1 and below ) . Overall , 414 ecotopes were sampled in 72 dwelling compounds; a few uncommon ecotopes ( three kennels , a dovecot , and a bird-cage ) , none of which appeared to be infested , were excluded from the analyses . Each ecotope was searched three times over a short period ( median 8 days , range 7–13 days ) by local vector control-surveillance staff , for a total effort of 1 , 242 individual vector searches . Vector-search teams were rotated and kept blind to the results of previous search rounds so that the outcomes of individual vector searches could be treated as independent . Field teams were instructed to stop searching in each ecotope as soon as the first triatomine bug was detected . All ecotopes were sprayed with a pyrethroid insecticide ( following Ceará state's Health Department standard procedures ) after the second vector search , regardless of whether or not vectors had been detected previously; the third vector search was conducted immediately after insecticide application , which might reveal cryptic infestation foci because of the irritant and ‘knock-down’ effects of pyrethroids [14] , [15] . All triatomines found in each ecotope were collected after the third search round . A more detailed description of our sampling scheme , including caveats , can be found in ref . [21]; one important difference between ref . [21] and our present analyses is that here we consider two types of evidence of ecotope infestation: ( i ) ‘certain’ evidence , represented by the finding and identification of triatomine bugs of any stage or their exuviae ( molted ‘skins’ ) , and ( ii ) ‘uncertain’ evidence , represented by the finding of only fecal streaks identified by field staff as triatomine bug feces – a proxy for triatomine bug presence used in vector surveillance in our study setting and elsewhere ( e . g . , [10]–[12] , [14]–[16] ) . Triatomine bug fecal streaks are relatively easy to distinguish from , but can still be confused with , those of cockroaches , ticks , flies , or bedbugs; hence , this proxy introduces the possibility of false-positive detections [11] , [12] . Individual vector-search results in each ecotope were recorded separately so that a three-entry ‘detection history’ including three ‘detection states’ was available for each ecotope: ‘certain’ detections ( coded as 2 ) , ‘uncertain’ detections ( coded 1 ) , or ‘non-detections’ ( coded 0 ) [9] . Table 2 presents the interpretation of the ‘detection histories’ observed in our survey . The focus of this paper is the sampling process governing vector detection/non-detection , not the biological processes governing vector presence/absence in individual ecotopes . Therefore , and for simplicity , we pool data across triatomine bug species ( Triatoma brasiliensis , T . pseudomaculata , and Rhodnius nasutus were detected; details not shown ) and do not investigate correlates of ecotope infestation ( for T . brasiliensis , such analyses are provided in ref . [21] ) . Rather , we ask what are the sensitivity and specificity of vector searches , what covariates may induce vector-detection heterogeneity , and how sampling-process uncertainty may affect infestation estimates . In short , this report is an attempt at shedding light on the process of vector detection , and consequently emphasizes practical issues critical to entomological surveillance [16] . We analyzed our detection/non-detection records in two steps . First , we used simple descriptive statistics , considering the results of each vector-search round separately and those of all rounds combined ( Tables 1–3 ) . Importantly , these analyses ignore any possible detection errors; this mimics standard practice and yields the naïve ‘infestation indices’ recommended by the World Health Organization [3] – which are used , as far as we are aware , in all Chagas disease control programs . The naïve infestation index is simply IInaïve = x/n , where x is the number of infested sampling units ( here , ecotopes with ≥1 detection of vectors or their traces ) and n is the number of units sampled [3]; for example , with x = 50 and n = 100 , IInaïve = 50/100 = 0 . 50 ( or 50% ) . Although this is routinely interpreted as the proportion ( or percent ) of sampling units that were infested , we emphasize that it is , in reality , the proportion of sampling units where evidence of infestation was detected , usually after a single search . Both quantities would only be equal if evidence of infestation were ascertained without error; they will differ , for example , whenever the sensitivity of the method used to detect infestation is p<1 . 0 . For p = 0 . 75 , an adjusted estimator of infestation would be IIadjusted = x/ ( n×p ) = 50/ ( 100×0 . 75 ) ≈0 . 67 . Hence , IInaïve will be biased low whenever p<1 . 0 , which is probably always [7] , [8] . In the second phase of our analyses , we adopt the ‘multiple detection-state’ modeling framework of Miller et al . [9] to explicitly account for possible false-negative ( detection failures ) and false-positive results ( misidentified fecal streaks ) . We focus on estimating ( i ) the sensitivity of active vector searches by trained staff ( denoted p11 ) ; ( ii ) the effects of a suite of selected covariates on p11; and ( iii ) the probability that an ecotope is incorrectly classified as infested when it is not ( p10 , possibly induced by misidentification of fecal streaks ) and its complement , 1 – p10 , which estimates vector-search specificity ( denoted s ) . Our covariates on p11 reflect a series of hypotheses about what might affect vector-search sensitivity; after preliminary analyses and prior results from a Jaguaruana data subset ( see ref . [21] ) , we considered three major possibilities: We evaluated these covariates on p11 as additive terms using the logit link function [9] , and used the second-order version of Akaike's information criterion ( AICc , with n = 414 ecotopes ) to rank the models and assess the relative support for each model , given the data [6]–[9] , [22] . We fitted 44 models , including a ‘null’ model estimating only intercepts; after preliminary analyses , all models except the ‘null’ included the “Number of bugs” covariate , which clearly improved AICc scores . Models with non-zero Akaike weights ( wi ) are presented in Table 4 , and the full model set in Table S1 . Apart from sensitivity ( p11 ) and covariate effects , our models also estimate ( i ) a site-occupancy parameter ( denoted Ψ ) that expresses the mean probability that an ecotope is infested ( or , equivalently , overall infestation prevalence ) ; ( ii ) the probability of false-positive detections , p10; and ( iii ) the probability that a detection is classified as ‘certain’ , given the ecotope is infested and a detection occurred ( denoted b ) [9] . For simplicity , Ψ was held constant in our current models , which as mentioned above focus on the vector-detection process and especially on the sensitivity of active vector searches ( p11 ) . Covariate effects were allowed to modify p11 , p10 and b , so that detection parameters had different intercepts but common slopes; we tested alternative parameterizations , either with p10 fixed at zero ( i . e . , assuming no false-positive results ) or with p10 and b varying only with observed bug density and sampling-scheme covariates ( “Search 1” and “SDEc” ) , but the models had larger AICc scores ( details not shown ) . We calculated model-averaged estimates of Ψ and covariate effects on p11 , with unconditional standard errors ( SEs ) , using equations 4 . 1 and 4 . 9 in ref . [22] . For detection parameters p11 , p10 , and b ( and their SEs ) , we calculated model-weighted averages of individual results ( i . e . , model-predicted values and SEs for each individual ecotope and search round , weighted by each model's wi ) and provide summary statistics ( see Table S2 ) . For consistency with our AIC-based approach , we present parameter and covariate-effect estimates with approximate 85% confidence intervals ( CIs ) ( see ref . [23] ) , although we also comment on the more conventional 95%CIs in some instances . Models were fit via maximum likelihood as implemented in Presence 6 . 4 [24] . We finally compared the results of naïve and model-based analyses in the epidemiologically- and operationally-relevant terms of ( i ) estimates of infestation prevalence ( IInaïve vs . model-averaged Ψ ) and ( ii ) estimates of the number of infestation foci that likely went undetected during standard , active vector searches .
Naïve infestation indices for each vector-search round and all rounds combined are presented in Table 1 ( see raw data in Dataset S1 ) . Overall , more detections occurred during the first than during the second and third vector-search rounds ( see also [21] ) ; a similar trend was apparent when considering ‘certain’ detections only . Importantly , naïve infestation indices were higher in almost all ecotope types when the results of the three vector-search rounds were combined than when considering each single round in isolation ( Table 1 ) . Over all ecotope types , combined-search naïve infestation indices were from 1 . 24 to 2 . 06 times higher than single-search indices for all detection data , and from 1 . 30 to 1 . 98 times higher than single-search indices for ‘certain’ detection data . Table 2 summarizes ‘detection histories’ for the 414 ecotopes surveyed . Evidence of infestation was detected at least once in 19 ecotopes where the first vector search had yielded no detections . In 21 ecotopes only the first vector search yielded evidence of infestation , with ‘certain’ detections ( history “200” ) in 16 ecotopes . Evidence of vector presence was consistently found across all search rounds in only 30 of the 97 ecotopes where such evidence was found at least once; ‘certain’ detections consistently occurred in 22 of those ecotopes . Thus , many observed infestation foci went undetected during single vector-search rounds: at least 19 foci in the first , 36 in the second , and 50 in the third round . Considering only the 87 ‘certain’ observed foci , 20 were missed in the first , 42 in the second , and 43 in the third search round . We finally note that observed infestation was markedly different in our two study localities , with triatomine bug foci apparently more common and denser in Jaguaruana than in Russas ( Table 3 ) . Site-occupancy models with non-zero Akaike weights ( Σwi = 1 . 0 ) are presented in Table 4 . Model-averaged , adjusted estimates of covariate effects on vector-search sensitivity ( p11 ) , along with their unconditional SEs and 85%CIs , are presented in Table 5; the associated odds ratios are shown in Figure 1 . The sensitivity of active vector searches was higher in ecotopes harboring denser bug colonies; the odds of vector detection were 2 . 77 ( 85%CI 2 . 02–3 . 80 ) times higher for each unit increase in the standardized number of bugs caught in a given ecotope ( 1 SD increase ≈9 bugs ) ( Fig . 1 and inset in Fig . 2A ) . Vectors were also easier to detect in peridomestic woodpiles ( odds ratio 3 . 00 , 85%CI 1 . 72–5 . 23 ) , in goat/sheep corrals ( 2 . 15 , 85%CI 1 . 31–3 . 54 ) , and during the first search round ( 1 . 91 , 85%CI 1 . 42–2 . 57 ) ; the positive effect of storerooms ( odds ratio 2 . 46 ) was associated with larger uncertainty ( 85%CI 1 . 12–5 . 42 , with the 95%CI encompassing 1 ) ( Fig . 1 ) . Vector-search sensitivity was somewhat lower in houses ( odds ratio 0 . 47 , 85%CI 0 . 27–0 . 83; upper limit of the 95%CI = 1 . 02 ) and henhouses ( 0 . 56 , 85%CI 0 . 34–0 . 93; upper limit of the 95%CI = 1 . 12 ) , and substantially lower in brick piles , albeit uncertainty about this latter estimate was large ( odds ratio 0 . 12 , 85%CI 0 . 04–0 . 40 ) ( Fig . 1 ) . Other covariates , including those grouping buildings , animal enclosures and piles , had no discernible effects on p11 ( Tables 5 and S1 , Fig . 1 ) . With our parameterization , models including detection covariates estimate ecotope-specific values for p11 , p10 , and b [9]; we therefore provide summary statistics of model-averaged estimates for each parameter and its variation . Figure 2 shows model-averaged p11 estimates for different ecotopes; sensitivity was overall low ( mean across ecotopes and vector-search rounds , p11≈0 . 283±0 . 057; median = 0 . 231 , inter-quartile range 0 . 123–0 . 384 ) , and particularly so in brick piles ( mean p11≈0 . 042±0 . 032 ) and houses ( mean p11≈0 . 143±0 . 043 ) . Overall , sensitivity was lower in the lightly-infested ( mean p11-Russas≈0 . 200±0 . 054; Fig . 2B ) than in the heavily-infested locality ( mean p11-Jaguaruana≈0 . 367±0 . 060; Fig . 2C ) . Sensitivity was estimated at p11≈1 . 00 for a single tile pile where 122 triatomine bugs were collected after the third search round . See Table S2 for further details about p11 values . An ecotope can be incorrectly classified as infested , with probability p10 , when infestation status is determined based on the detection of fecal streaks . Our models suggest that this event was , on average , very unlikely: the mean of model-averaged values across ecotopes and vector-search rounds was p10≈0 . 011±0 . 008 ( median = 0 . 0015 , inter-quartile range 0 . 0007–0 . 0030 ) , reaching high values ( >0 . 90 ) in the few ecotopes where p11 was also very high . This reflects the fact that the detection of only fecal streaks in ecotopes where sensitivity is close to 100% almost surely represents a false-positive result . Hence , with a few exceptions , vector-search specificity ( s = 1 – p10 ) was reassuringly high , with a mean value of ∼0 . 989 . The probability that a detection was classified as ‘certain’ , given the ecotope was infested and at least one detection occurred , was moderately high ( mean across ecotopes and vector-search rounds , b≈0 . 637±0 . 073 ) and varied from 0 . 116 in 19 brick piles to ∼1 . 0 in the tile pile where p11 was also ∼1 . 0 . Model-averaged infestation prevalence ( or mean ecotope-occupancy rate ) was estimated as Ψaverage≈0 . 445 ( unconditional SE = 0 . 064; 85%CI 0 . 353–0 . 537 ) ; this estimate is nearly twice as high as the naïve infestation index calculated with the combined results of three vector-search rounds: IInaïve = 97/414 = 0 . 234 ( Fig . 3 ) . Our model-based site-occupancy estimate suggests that the number of infested ecotopes was x′ = Ψaverage × n = 0 . 445×414≈184; therefore , and despite triplicate search effort , as many as ∼87 infestation foci most likely went undetected during active vector searches . Considering the results of single vector-search rounds separately ( which is standard practice in vector surveillance and research ) , we estimate that about 106 , 123 , and 137 infestation foci went undetected during the first , second , and third search rounds , respectively ( Fig . 3 ) . Importantly , our analyses suggest that vector-search sensitivity was especially poor in the more lightly-infested locality of Russas ( Fig . 2 ) , where observed infestation prevalence ( IInaïve-Russas = 0 . 048; Table 3 ) was therefore likely to be particularly biased low .
We have presented a detailed investigation of major sources of detection heterogeneity in Chagas disease vector surveys . To our knowledge , this is the first attempt at quantifying vector sampling uncertainty when survey methods can yield spurious detections and non-detections . Our results are far from encouraging: they suggest that discounting sampling-process uncertainty , and particularly false-negative results , can lead to serious , overoptimistic misrepresentations of both disease transmission risk and vector control performance . Reliable measures of disease vector ( or pathogen ) presence/absence are essential for disease prevention; while it is unfortunate that available triatomine-detection tools perform poorly , with sensitivity typically below 50% , ignoring this critical problem will not solve it . Instead , we must develop a sound understanding of how the vector-detection process works and incorporate the associated uncertainties into our operational indicators . The approach we used here can help do so . We expect that , sometime in the near future , the crucial issue of sampling-process uncertainty will be widely acknowledged , and formally accounted for , in routine-surveillance systems . Otherwise , many human beings will continue to suffer vector presence and disease transmission while researchers , control managers and international-agency officials , misled by imperfect data , celebrate public health ‘achievements’ that may well glister but are not gold [28] , [29] .
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Vector-borne disease prevention often relies on health agents inspecting dwellings and eliminating the vector infestation foci they detect . The effectiveness of prevention programs thus depends on vector-detection performance . Unfortunately , detection failures can be common , particularly when infestation is rare and vector foci small . Although this can threaten vector control , the actual performance of vector searches has seldom been investigated in detail . Here , we assess Chagas disease vector detection by trained control-surveillance agents . We used models that explicitly account for detection errors to analyze triplicate vector detection/non-detection records from 414 man-made ‘ecotopes’ ( houses , henhouses , woodpiles , etc . ) in two rural localities . On average , a single round of vector searches correctly identified about 28% of the infested ecotopes; detection was more challenging in lightly-infested ecotopes and in some ecotope types , particularly houses and brick piles . After correcting detection errors , we estimated that ∼45% of the ecotopes were most likely infested , while observed rates were ∼11–19%; standard , single-round vector searches therefore missed many infestation foci . Our findings underscore the importance of taking detection failures into account when assessing infestation by disease vectors , and illustrate a straightforward approach to tackle the major but still underappreciated problem of imperfect vector detection .
|
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"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
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2014
|
All That Glisters Is Not Gold: Sampling-Process Uncertainty in Disease-Vector Surveys with False-Negative and False-Positive Detections
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Intron gigantism , where genes contain megabase-sized introns , is observed across species , yet little is known about its purpose or regulation . Here we identify a unique gene expression program utilized for the proper expression of genes with intron gigantism . We find that two Drosophila genes with intron gigantism , kl-3 and kl-5 , are transcribed in a spatiotemporal manner over the course of spermatocyte differentiation , which spans ~90 hours . The introns of these genes contain megabases of simple satellite DNA repeats that comprise over 99% of the gene loci , and these satellite-DNA containing introns are transcribed . We identify two RNA-binding proteins that specifically localize to kl-3 and kl-5 transcripts and are needed for the successful transcription or processing of these genes . We propose that genes with intron gigantism require a unique gene expression program , which may serve as a platform to regulate gene expression during cellular differentiation .
Introns , non-coding elements of eukaryotic genes , often contain important regulatory sequences and allow for the production of diverse proteins from a single gene , adding critical regulatory layers to gene expression [1] . Curiously , some genes contain introns so large that more than 99% of the gene locus is non-coding . In humans , neuronal and muscle genes are enriched amongst those with the largest introns [2] . One of the best-studied large genes , Dystrophin , a causative gene for Duchenne Muscular Dystrophy , spans 2 . 2Mb , only 11kb of which is coding . A large portion of the remaining non-coding sequence is comprised of introns rich in repetitive DNA [3] . While intron size ( ‘gigantism’ ) is conserved between mouse and human , there is little sequence conservation within the introns , implying the functionality of intron gigantism [4] . The Drosophila Y chromosome provides an excellent model for studying intron gigantism . Approximately 80% of the 40Mb Y chromosome is comprised of repetitive sequences , primarily satellite DNAs , which are short tandem repeats , such as ( AATAT ) n ( Fig 1A ) [5–8] . The Drosophila Y chromosome encodes fewer than 20 genes [9] , six of which are classically known as the ‘fertility factors’ [10–13] . One of these fertility factors , kl-3 , which encodes an axonemal dynein heavy chain [14–16] , spans at least 4 . 3Mb [11 , 17 , 18] , while its coding sequence is only ~14kb ( Fig 1A ) . This is due to the large satellite DNA rich-introns , some of which are megabases in size , that comprise more than 99% of the kl-3 locus . The other five fertility factors ( kl-1 , kl-2 , kl-5 , ks-1 , ks-2 ) , have a similar gene structure , possessing large introns of repetitive satellite DNAs [11] . These six large Y chromosome genes are solely expressed during spermatogenesis [14 , 19 , 20] . In the Drosophila testis , germ cells undergoing differentiation are arranged in a spatiotemporal manner , where the germline stem cells ( GSCs ) reside at the very apical tip and differentiating cells are gradually displaced distally ( Fig 1B ) [21] . GSC division gives rise to spermatogonia ( SG ) , which undergo four mitotic divisions with incomplete cytokinesis to become a cyst of 16 SGs . 16-cell SG cysts enter meiotic S phase , at which point they become known as spermatocytes ( SCs ) . SCs have an extended G2 phase , spanning 80–90 hours , prior to initiation of the meiotic divisions [22] . During this G2 phase , the cells increase approximately 25 times in volume and the homologous chromosomes pair and partition into individual chromosome territories ( Fig 1C ) [23 , 24] . During this period , SCs transcribe the majority of genes whose protein products will be needed for meiotic division and spermiogenesis [25–27] . Gene expression in SCs is thus tightly regulated to allow for timely expression of meiotic and spermiogenesis genes [28] . It has long been known that three of the Y-chromosome-associated genes that contain gigantic introns ( kl-5 , kl-3 and ks-1 , Fig 1A ) form lampbrush-like nucleoplasmic structures in SCs , named Y-loops [denoted as loops A ( kl-5 ) , B ( kl-3 ) , and C ( ks-1 ) , ( Fig 1C and 1D ) ] [17] . Y-loop structures reflect the robust transcription of underlying genes , and have been observed across Drosophilids , including D . simulans , D . yakuba , D . pseudoobscura , D . hydei and D . littoralis [29 , 30] . Much of the fundamental knowledge about Y-loops comes from D . hydei , which forms large , cytologically distinct Y-loops [31] , leading to the discovery that these structures are formed by the transcription of large loci comprised of repetitive DNAs [32–37] . Interestingly , in D . pseudoobscura , which contains a ‘neo-Y’ ( not homologous to the ancestral Y chromosome ) , Y-loops are thought to be formed by Y-linked genes instead of by the kl-3 , kl-5 and ks-1 homologs , which are autosomal [38] , suggesting that Y-loop formation is a unique characteristic of Y-linked genes , instead of being a gene-specific phenomenon . The transcription/processing of such gigantic genes/RNA transcripts , in which exons are separated by megabase-sized introns , must pose a significant challenge for cells . However , how genes with intron gigantism are expressed and whether intron gigantism plays any regulatory roles in gene expression remain largely unknown . In this study , we began addressing these questions by using the Y-loop genes as a model , and describe the unusual nature of the gene expression program associated with intron gigantism . We find that transcription of Y-loop genes progresses in a strictly spatiotemporal manner , encompassing the entire ~90 hours of SC development: the initiation of transcription occurs in early SCs , followed by the robust transcription of the satellite DNA from the introns , with cytoplasmic mRNA becoming detectable only in late SCs . We identify two RNA-binding proteins , Blanks and Hephaestus ( Heph ) , which specifically localize to the Y-loops , and show that they are required for robust transcription and/or proper processing of the Y-loop gene transcripts . Mutation of the blanks or heph genes leads to sterility due to the loss of Y-loop gene products . Our study demonstrates that genes with intron gigantism require specialized RNA-binding proteins for proper expression . We propose that such unique processing may be utilized as an additional regulatory mechanism to control gene expression during differentiation .
To start to investigate how the expression of Y-loop genes may be regulated , we sought to monitor their expression during SC development . In previous studies using D . hydei , when two differentially-labeled probes against two intronic repeats of the Y-loop gene DhDhc7 ( y ) ( homologous to D . melanogaster kl-5 ) were used for RNA fluorescent in situ hybridization ( FISH ) , expression of the earlier repeat preceded that of the later repeat [39 , 40] , leading to the idea that Y-loop genes might be transcribed as single , multi-megabase , transcripts . Consistently , Miller spreading of SC Y chromosomes , in which transcripts can be seen still bound to DNA , showed the long Y-loop transcripts [41 , 42] . However , transcription of the exons was not visualized and extensive secondary structures were present in the Miller spreads , leaving it unclear whether the entire gene region is transcribed as a single transcript . By using differentially-labeled probe sets designed for RNA FISH to visualize 1 ) the first exon , 2 ) the satellite DNA ( AATAT ) n repeats found in multiple introns including the first [5 , 43] , and 3 ) exon 14 ( of 16 ) of kl-3 ( Fig 1A , S1 File ) , we found that kl-3 transcription is organized in a spatiotemporal manner: transcript from the first exon becomes detectable in early SCs , followed by the expression of the ( AATAT ) n satellite from the introns , then finally by the transcript from exon 14 in more mature SCs ( Fig 1D ) . These results suggest that transcription of kl-3 takes the entirety of SC development , spanning ~90 hours . The pattern of transcription is consistent with the model proposed for Y-loop gene expression in D . hydei: the gene is likely transcribed as a single transcript that contains the exons and gigantic introns , although we cannot exclude the possibility of other mechanisms , such as the trans-splicing of multiple individually transcribed exons [44] . Based on the expression pattern of early exon , ( AATAT ) n satellite-containing introns , and late exon , SC development can be subdivided into four distinct stages ( Fig 1E–1H ) . In stage 1 , only exon 1 transcript is apparent ( Fig 1E ) . In stage 2 , the expression of intron transcript is detectable , and the signal from exon 1 remains strong ( Fig 1F ) . Stage 3 is defined by the addition of late exon signal in addition to the continued presence of exon 1 and intron transcripts , indicating that transcription is nearly complete ( Fig 1G ) . Stage 4 is characterized by the presence of exon probe signals in granule-like structures in the cytoplasm ( Fig 1H ) , which likely reflect kl-3 mRNA localizing to ribonucleoprotein ( RNP ) granules , as they never contain intron probe signal . These granules are absent following RNAi-mediated knockdown of kl-3 ( bam-gal4>UAS-kl-3TRiP . HMC03546 , Fig 1I ) , confirming that they reflect kl-3 mRNA . The same pattern of expression was seen for the Y-loop gene kl-5 ( see below ) , suggesting that transcription of the other Y-loop genes proceeds in a similar manner . Together , these results show that the gigantic Y-loop genes , including megabases of intronic satellite DNA repeats , are transcribed continuously in a process that spans the entirety of SC development , culminating in the formation of mRNA granules in the cytoplasm near the end of the 80–90 hour meiotic G2 phase . While transcription elongation is believed to be quite stable [45] , the presence of tandem arrays [46] or repeat expansions ( as seen in trinucleotide expansion diseases ) [47–49] can greatly slow an elongating polymerase and/or lead to premature dissociation [50] . Therefore , Y-loop gene transcription may require precise regulation . Considering the size of the Y-loop gene loci and their satellite DNA-rich introns , transcription of the Y-loop genes likely utilizes unique regulatory mechanisms . To start to understand such a genetic program , we performed a screen ( See Methods and S2 File ) . Briefly , a list of candidates was curated using a combination of gene ontology ( GO ) terms , expression analysis , predicted functionality and reagent availability , resulting in a final list of 67 candidate genes ( S2 File ) . Candidates were screened for several criteria including protein localization , fertility , and Y-loop gene expression . Among these , two genes , blanks and hephaestus ( heph ) , exhibit localization patterns and phenotypes that reveal critical aspects of Y-loop gene regulation and were further studied . Several proteins , including Boule [51] , Hrb98DE [52] , Pasilla [52 , 53] and Rb97D [54] , were previously shown to localize to the Y-loops but displayed no detectable phenotypes in Y-loop gene expression in SCs using RNAi-mediated knockdown and/or available mutants ( S2 File ) , and were not further pursued in this study . Blanks , a RNA-binding protein with multiple dsRNA binding domains , is primarily expressed in SCs . Blanks has been shown to be important for post-meiotic sperm development and male fertility [55 , 56] , and Blanks’ ability to bind RNA was found to be necessary for fertility [55] . In order to assess Blanks’ localization within the SC nucleus , testes expressing GFP-Blanks were processed for RNA FISH with probes against the intronic satellite DNA transcripts [ ( AATAT ) n for Y-loop B/kl-3 , ( AAGAC ) n for Y-loops A/kl-5 and C/ks-1 [57]] . ( AATAT ) n is the only satellite DNA found in Y-loop B [5 , 58] and while ( AAGAC ) n is not the only satellite DNA found in Y-loops A & C , its expression from these loci was previously characterized [57] . We found that GFP-Blanks exhibits strong localization to Y-loop B ( Fig 2A ) . Heph , a heterogeneous nuclear ribonucleoprotein ( hnRNP ) homologous to mammalian polypyrimidine track binding protein ( PTB ) , is a RNA-binding protein with multiple RNA recognition motifs ( RRMs ) that is expressed in the testis [59] . Heph has also been implicated in post-meiotic sperm development and male fertility [60 , 61] . By using a Heph-GFP protein trap ( p ( PTT-GC ) hephCC00664 ) combined with RNA FISH to visualize the Y-loop gene intronic transcripts , we found that Heph-GFP localizes to Y-loops A and C ( Fig 2B ) . It should be noted that the heph locus encodes 25 isoforms and the Heph-GFP protein trap likely represents only a subset of heph gene products . A summary of Y-loop designation , gene , intronic satellite DNA repeat , and binding protein is provided in Fig 2C . We confirmed previous reports that blanks is required for male fertility [55 , 56] . By examining the seminal vesicles for the presence of motile sperm , we found that seminal vesicles from control siblings contain abundant motile sperm ( Fig 2D , 13% empty , 87% normal , n = 46 ) while seminal vesicles from blanks mutants ( blanksKG00084/Df ( 3L ) BSC371 ) lack motile sperm ( Fig 2E , 96% = empty , 4% greatly reduced , n = 58 ) . We also confirmed previous reports that heph is required for fertility [60 , 62] . Seminal vesicles from heph mutants ( heph2/Df ( 3R ) BSC687 ) also lack motile sperm ( Fig 2G , 100% empty , n = 21 ) , while those from control siblings contain motile sperm ( Fig 2F , 5% empty , 95% normal , n = 57 ) . Previous studies [55 , 56 , 60] reported that blanks and heph mutants are defective in sperm individualization , one of the final steps in sperm maturation , where 64 interconnected spermatids are separated by individualization complexes ( ICs ) that form around the sperm nuclei and migrate in unison along the sperm tails , removing excess cytoplasm and encompassing each cell with its own plasma membrane ( Fig 2J ) [63] . When the F-actin cones of IC were visualized by Phalloidin staining , it became clear that ICs form properly in all genotypes ( Fig 2K–2N ) , but become disorganized in the late ICs in blanks and heph mutants , a hallmark of axoneme formation defects [64] , preventing completion of individualization ( Fig 2Q–2T ) . The sterility and individualization defects observed in blanks and heph mutants are reminiscent of the phenotypes observed in flies lacking axonemal dynein genes including kl-5 and kl-3 , the Y-loop A and B genes [14–16 , 64–66] . Upon RNAi mediated knockdown of kl-3 and kl-5 ( bam-gal4>UAS-kl-3TRiP . HMC03546 or bam-gal4>UAS-kl-5TRiP . HMC03747 ) , motile sperm are not found in the seminal vesicles ( Fig 2H and 2I , kl-3: 100% empty , n = 81 , kl-5: 94% empty , 6% greatly reduced , n = 50 ) and a scattering of late ICs is observed ( Fig 2O , 2P , 2U and 2V ) . Based on these observations , we hypothesized that the sterility and IC defects observed in blanks and heph mutants may arise due to failure in the expression of the Y-loop genes . As blanks was found to localize to Y-loop B , we first determined whether there were any overt defects in Y-loop B formation or kl-3 expression in blanks mutants . To this end , we performed RNA FISH to visualize the Y-loop gene intronic transcripts in blanks mutants . Compared to control testes where intronic satellite DNA transcripts from all Y-loops become detectable fairly early in SC development and quickly reach full intensity ( Fig 3A ) , the signal from the Y-loop B intronic transcripts remains faint in blanks mutants ( Fig 3B ) . The expression of Y-loops A and C is comparable between control and blanks mutant testes ( Fig 3A and 3B ) . In addition to a reduction in the expression of the intronic satellite DNA repeats of Y-loop B/kl-3 , expression of kl-3 exons is also reduced in blanks mutants . By performing RNA FISH using exonic and intronic ( AATAT ) n probes for Y-loop B/kl-3 , we found that blanks mutants display an overall reduction in signal intensity for both intronic satellite repeats and exons compared to controls ( Fig 3C and 3E ) . Moreover , cytoplasmic kl-3 mRNA granules are rarely detected in blanks mutants ( Fig 3F ) . The same results are obtained following RNAi mediated knockdown of blanks ( bam-gal4>UAS-blanksTRiP . HMS00078 , S1 Fig ) . These results suggest that blanks is required for robust and proper expression of Y-loop B/kl-3 and for the production of kl-3 mRNA granules , likely at the transcriptional level . Consistently , we found that the amount of Kl-3 protein is greatly diminished in blanks mutants , confirming that blanks is required for proper expression of Y-loop B/kl-3 ( Fig 3G ) . To obtain a more quantitative measure of kl-3 expression levels in control and blanks mutant testes , we performed RT-qPCR . Primers were designed to amplify early ( close to the 5’ end ) , middle , and late ( close to the 3’ end ) regions of kl-3 . For each region , two sets of primers were designed: one primer set spanned a satellite DNA-containing large intron and another spanned a normal size intron ( Fig 3H , bars denote spanned intron , and S3 File ) . All primer sets show a detectable drop in kl-3 mRNA levels in blanks mutants when normalized to GAPDH and sibling controls ( Fig 3H ) . We noted a detectable drop between the early primer sets ( ~75% reduction in expression levels compared to controls ) and the middle/late primer sets ( ~95% reduction in expression levels compared to controls ) , raising the possibility that blanks mutants may have difficulty transcribing this Y-loop gene soon after encountering the first satellite DNA-containing gigantic intron or stabilizing kl-3 transcripts . A recent study that examined global expression changes in blanks mutant testes reported a similar change in kl-3 gene expression [67] . In summary , the RNA-binding protein Blanks localizes to Y-loop B and allows for the robust transcription of the Y-loop B gene kl-3 . In contrast to Y-loop B/kl-3 expression , Y-loop A/kl-5 expression appeared normal in blanks mutants . We designed RNA FISH probes against kl-5 in the same manner as for kl-3 ( i . e . early exon , intron and late exon ) ( Fig 4A , S1 File ) . We found that transcription of Y-loop A/kl-5 follows a spatiotemporal pattern similar to that of Y-loop B/kl-3 ( Fig 4B and 4C ) : early exon transcripts become detectable in early SCs while kl-5 mRNA granules are not detected in the cytoplasm until near the end of SC development ( Fig 4B and 4C ) . No overt differences are observed in kl-5 expression in blanks mutants and kl-5 mRNA granules are observed in the cytoplasm ( Fig 4D and 4E ) . By RT-qPCR with primers for kl-5 designed similarly as described above for kl-3 ( Fig 3H ) , we found a mild reduction in kl-5 expression in blanks mutants when normalized to GAPDH and sibling controls ( Fig 4F ) . However , considering the fact that the kl-5 mRNA granule is correctly formed in blanks mutant testes ( Fig 4E ) , this reduction may not be biologically significant . The mild reduction in kl-5 transcript in blanks mutants could be an indirect effect caused by defective Y-loop B expression . Alternatively , it is possible that a small amount of ( AATAT ) n satellite , which is predicted to be present in the last intron of kl-5 [43 , 68] , might cause this mild reduction in kl-5 expression in blanks mutant testes . It is well known that SCs utilize a specialized transcription program in order to transcribe the vast majority of genes required for meiosis and spermiogenesis [28 , 69 , 70] . This program is executed by two groups of transcription factors: tMAC and the tTAFs . The tMAC ( testis-specific meiotic arrest complex ) complex has both activating and repressing activities and has been shown to physically interact with the core transcription initiation machinery [71–77] . The tTAFs ( testis-specific TATA binding protein associated factors ) are homologs of core transcription initiation factors [78–81] . tMAC and the tTAFs function cooperatively to regulate meiotic gene expression . To examine whether blanks is part of this established meiotic transcription program , we examined the expression of fzo and Dic61B , known targets of the SC-specific transcriptional program [70 , 79] , which are located on autosomes and do not have gigantic introns ( S1 File ) . In contrast to mutants for the tMAC component aly ( aly2/5P ) , which has drastically reduced levels of fzo and Dic61B transcripts , the expression of these genes is not visibly affected in blanks mutants ( S2 Fig ) , suggesting that blanks is not a part of the SC-specific transcriptional program involving tTAFs and tMAC . Instead , blanks is likely uniquely involved in the expression of the Y-loop genes . As Heph-GFP localized to Y-loops A and C , we first examined whether Y-loops A and C displayed any overt expression defects in heph mutants ( Fig 3B ) . When we performed RNA FISH to visualize the Y-loop gene intronic transcripts in heph mutants , the overall expression levels of both ( AAGAC ) n and ( AATAT ) n satellites appear unchanged between control and heph mutant testes ( Fig 5A and 5B ) . However , we noted that the morphology of Y-loops A and C is altered in heph mutants , adopting a less organized , diffuse appearance ( Fig 5B ) , whereas all Y-loops in control SCs show characteristic thread-like or globular morphologies ( Fig 5A ) . Y-loop B appears unchanged between controls and heph mutants ( Fig 5A and 5B ) . These results indicate that heph may be important for structurally organizing Y-loop A and C transcripts , without affecting overall transcript levels . We next examined the expression pattern of kl-5 exons together with the Y-loop A/C intronic satellite [ ( AAGAC ) n] , as described in Fig 4 . Overall expression levels of kl-5 appear to be unaltered in heph mutant testes ( Fig 5C and 5E ) . However , in contrast to control testes ( Fig 5D ) , heph mutant testes rarely have cytoplasmic kl-5 mRNA granules in late SCs ( Fig 5F ) , suggesting that heph mutants affect kl-5 mRNA production without affecting transcription in the nucleus . heph mutants may be defective in processing the long repetitive regions of transcripts to generate mRNA ( e . g . splicing , mRNA export or protection from degradation ) . We also examined the expression of ks-1 ( ORY ) in heph mutants as Heph-GFP also localized to Y-loop C . While the ORY ORF is too short to allow for designing exon-specific probes to examine temporal expression patterns , RNA FISH with probes targeting all exons of ORY revealed that ORY mRNA granules are not formed in heph mutants ( S3 Fig ) . Similar to blanks mutants , heph mutants show no defects in the expression of fzo or Dic61B ( S2 Fig ) , indicating that heph is not a member of the more general meiotic transcription program . Instead , heph , like blanks , appears to specifically affect the expression of Y-loops to which it localizes . RT-qPCR showed that heph mutants only exhibit a moderate reduction in kl-5 expression when normalized to GAPDH and sibling controls ( Fig 5G ) , which is in accordance with the RNA FISH results described above . A similar moderate reduction in kl-5 mRNA is observed in blanks mutants ( Fig 4F ) , which do not affect kl-5 mRNA granule formation . Thus , it is unlikely that the reduction in kl-5 expression levels alone causes the lack of kl-5 mRNA granules in heph mutant SCs . Instead , we postulate that mRNA granule formation is dependent on proper processing or stability of primary transcripts , which may be defective in heph mutants . Surprisingly , we found that kl-3 mRNA granules are also absent in heph mutants , although Y-loop B/kl-3 expression levels in the nucleus appear to be unaffected ( Fig 6A–6D ) . RT-qPCR showed a similar moderate reduction in kl-3 mRNA in heph mutants when normalized to GAPDH and sibling controls ( Fig 6E ) as was observed in kl-5 mRNA ( Fig 5G ) . Consistent with the absence of cytoplasmic kl-3 mRNA granules , Kl-3 protein levels are dramatically reduced in heph mutant testes ( Fig 6F ) . This is unexpected as Heph protein does not localize to Y-loop B ( Fig 2B ) or affect Y-loop B morphology ( Fig 5A and 5B ) . It is possible that some of the predicted 25 isoforms of Heph are not visualized by Heph-GFP , and these un-visualized isoforms might localize to and regulate Y-loop B/kl-3 expression . Alternatively , this may be an indirect effect of defective Y-loop A and C expression and/or structure . Taken together , our results show that Blanks and Heph , two RNA-binding proteins , are essential for the expression of Y-loop genes , but are not members of the more general meiotic transcription program . As Y-loop genes are essential for sperm motility and fertility , the sterility observed in blanks and heph mutants likely stems from defects in Y-loop gene expression . Blanks and Heph highlight two distinct steps ( transcriptional processivity and RNA processing ( e . g . splicing , export and/or stability of transcripts ) ) in a unique Y-loop gene expression program .
The existence of the Y chromosome lampbrush-like loops of Drosophila has been known for the last five decades [82 , 83] , however little is known as to how Y-loop formation and expression is regulated and whether these SC-specific structures are important for spermatogenesis . Here we identified a Y-loop gene-specific expression program that functions in parallel to the general meiotic transcriptional program to aid in the expression and processing of the gigantic Y-loop genes . Our results suggest that genes with intron gigantism , such as the Y-loop genes and potentially other large genes such as Dystrophin , require specialized mechanisms for proper expression . The mutant phenotypes of blanks and heph , the two genes identified to be involved in this novel expression program , highlight two distinct steps of the Y-loop gene specific expression program ( Fig 6G ) . Blanks was originally identified as an siRNA binding protein , but no defects in small RNA mediated silencing were observed in the testes of blanks mutants [55 , 56] . We found that blanks is required for transcription of Y-loop B/kl-3 , as nuclear transcript levels were visibly reduced in blanks mutants , leading to the lack of both kl-3 mRNA granules in the cytoplasm and Kl-3 protein . As Blanks’ ability to bind RNA was previously found to be required for male fertility [55] , we speculate that Blanks may bind to newly synthesized nascent kl-3 RNA , which contain megabases of satellite DNA transcripts , so that transcripts’ secondary/tertiary structures do not interfere with transcription [84] . It is possible that elongating RNA polymerases , which slow and potentially lose stability on repetitive DNAs [46 , 49] , might require Blanks to increase processivity , allowing them to transcribe through repetitive DNA sequences , as has been observed for repetitive sequences in other systems [85–87] . Heph has been implicated in a number of steps in RNA processing and translational regulation [88–91] , but Heph’s exact role in the testis remained unclear despite its requirement for male fertility [60 , 61] . We found that heph mutants fail to generate kl-5 cytoplasmic mRNA granules even though nuclear transcript levels appeared minimally affected . This suggests that heph may be required for processing the long repetitive transcripts . For example , heph might be required to ensure proper splicing of the Y-loop gene pre-mRNAs , which is predicted to be challenging as the splicing of adjacent exons becomes exponentially more difficult as intron length increases [92] . Y-loop genes may utilize proteins like Heph to combat this challenge or alternatively , Heph could aid in stabilizing this long RNA and preventing premature degradation . These results highlight the presence of a unique program tailored toward expressing genes with intron gigantism . Although the functional relevance of intron gigantism remains obscure , our results may provide hints as to the possible functions of intron gigantism . Even if intron gigantism did not arise to serve a specific function , once it emerges , the unique gene expression program that can handle intron gigantism must evolve to tolerate the burden of gigantic introns , as indicated by our study on blanks and heph mutants . Ultimately , the presence of a unique gene expression program for genes with gigantic introns would provide a unique opportunity to regulate gene expression . Once such systems evolve , other or new genes may start utilizing such a gene expression program to add an additional layer of complexity to the regulation of gene expression . For example , in the case of Y-loop genes , the extended time period required for the transcription of the gigantic Y-loop genes ( ~80–90 hours ) might function as a ‘developmental timer’ for SC differentiation . Similar to this idea , it was shown that the expression of two homologous genes , knirps ( kni ) and knirps-like ( knrl ) , is regulated by intron size during embryogenesis in Drosophila . Although knrl can perform the same function as kni in embryos , mRNA of knrl is not produced due to the presence of a relatively large ( 14 . 9kb ) intron ( as opposed to the small ( <1kb ) introns of kni ) , which prevents completion of knrl transcription during the short cell cycles of early development [93] . A similar idea was proposed for Ultrabithorax ( Ubx ) in the early Drosophila embryo , where large gene size led to abortion of transcription of Ubx during the syncytial divisions of Drosophila embryos , preventing production of Ubx protein . [94] . Thus , intron size can play a critical role in the regulation of gene expression . Alternatively , satellite DNA-containing gigantic introns could act in a manner similar to enhancers , recruiting transcriptional machinery to the Y-loop genes to facilitate expression [1] . In summary , our study provides the first glimpse at how the expression of genes with intron gigantism requires a unique gene expression program , which acts on both transcription and post-transcriptional processing .
All fly stocks were raised on standard Bloomington medium at 25°C , and young flies ( 1- to 3-day-old adults ) were used for all experiments . Flies used for wild-type experiments were the standard lab wild-type strain yw ( y1w1 ) . The following fly stocks were used: heph2 ( BDSC:635 ) , Df ( 3R ) BSC687 ( BDSC: 26539 ) , blanksKG00084 ( BDSC:13914 ) , Df ( 3L ) BSC371 ( BDSC:24395 ) , p ( PTT-GC ) hephCC00664 ( BDSC:51540 ) , UAS-kl-3TRiP . HMC03546 ( BDSC:53317 ) , UAS-blanksTRiP . HMS00078 ( BDSC:33667 ) , UAS-kl-5TRiP . HMC03747 ( BDSC:55609 ) , and C ( 1 ) RM/C ( 1;Y ) 6 , y1w1f1/0 ( BDSC:9460 ) were obtained from the Bloomington Stock Center ( BDSC ) . GFP-blanks ( GFP-tagged Blanks expressed by it’s endogenous promoter ) was a gift of Dean Smith [55] . bam-gal4 was a gift of Dennis McKearin [95] . The aly2 and aly5P stocks were a gift of Minx Fuller [69] . It is important to note that the heph2 allele is known to be male sterile whereas other heph alleles are lethal , thus the heph2 allele is unlikely to be null and affects only a subset of isoforms , including one/those with a testis-specific function . The Y chromosome in the heph deficiency strain Df ( 3R ) BSC687 appeared to have accumulated mutations that resulted in abnormal Y-loop morphology . This Y chromosome was replaced with the yw Y chromosome for all experiments described in this study . The kl-3-FLAG strain was constructed by Fungene ( fgbiotech . com ) using CRISPR mediated knock-in of a 3X-FLAG tag in frame at the endogenous C-terminus immediately preceding the termination codon of kl-3 using homology-directed repair . Two guide RNAs were used ( CCACTGGACTTTAAGGGGTGTTGC and GCATCCTGACCACTGGACTTTAAG ) and point mutations were introduced in the PAM sequences following homology directed repair to prevent continued cutting . All solutions used for RNA FISH were RNase free . Testes from 2–3 day old flies were dissected in 1X PBS and fixed in 4% formaldehyde in 1X PBS for 30 minutes . Then testes were washed briefly in PBS and permeabilized in 70% ethanol overnight at 4°C . Testes were briefly rinsed with wash buffer ( 2X saline-sodium citrate ( SSC ) , 10% formamide ) and then hybridized overnight at 37°C in hybridization buffer ( 2X SSC , 10% dextran sulfate ( sigma , D8906 ) , 1mg/mL E . coli tRNA ( sigma , R8759 ) , 2mM Vanadyl Ribonucleoside complex ( NEB S142 ) , 0 . 5% BSA ( Ambion , AM2618 ) , 10% formamide ) . Following hybridization , samples were washed three times in wash buffer for 20 minutes each at 37°C and mounted in VECTASHIELD with DAPI ( Vector Labs ) . Images were acquired using an upright Leica TCS SP8 confocal microscope with a 63X oil immersion objective lens ( NA = 1 . 4 ) and processed using Adobe Photoshop and ImageJ software . Fluorescently labeled probes were added to the hybridization buffer to a final concentration of 50nM ( for satellite DNA transcript targeted probes ) or 100nM ( for exon targeted probes ) . Probes against the satellite DNA transcripts were from Integrated DNA Technologies . Probes against kl-3 , kl-5 , fzo , and Dic61B exons were designed using the Stellaris® RNA FISH Probe Designer ( Biosearch Technologies , Inc . ) available online at www . biosearchtech . com/stellarisdesigner . Each set of custom Stellaris® RNA FISH probes was labeled with Quasar 670 , Quasar 570 or Fluorescein-C3 ( S1 File ) . For strains expressing GFP ( e . g . GFP-Blanks , Heph-GFP ) , the overnight permeabilization in 70% ethanol was omitted . Total RNA from testes ( 50 pairs/sample ) was extracted using TRIzol ( Invitrogen ) according to the manufacturer’s instructions . 1μg of total RNA was reverse transcribed using SuperScript III® Reverse Transcriptase ( Invitrogen ) followed by qPCR using Power SYBR Green reagent ( Applied Biosystems ) . Primers for qPCR were designed to amplify only mRNA . For average introns , one primer of the pair was designed to span the two adjacent exons . Primers spanning large introns could only produce a PCR product if the intron has been spliced out . Relative expression levels were normalized to GAPDH and control siblings . All reactions were done in technical triplicates with at least two biological replicates . Graphical representation was inclusive of all replicates and p-values were calculated using a t-test performed on untransformed average ddct values . Primers used are listed in S3 File . Testes ( 40 pairs/sample ) were dissected in Schneider’s media at room temperature within 30 minutes , the media was removed and the samples were frozen at -80°C until use . After thawing , testes were then lysed in 200uL of 2X Laemmli Sample Buffer + βME ( BioRad , 161–0737 ) . Samples were separated on a NuPAGE Tris-Acetate gel ( 3–8% , 1 . 5mm , Invitrogen ) and transferred onto polyvinylidene fluoride ( PVDF ) membrane ( Immobilon-P , Millipore ) using NuPAGE transfer buffer ( Invitrogen ) without added methanol . Membranes were blocked in 1X TBST ( 0 . 1% Tween-20 ) containing 5% nonfat milk , followed by incubation with primary antibodies diluted in 1X TBST containing 5% nonfat milk . Membranes were washed with 1X TBST , followed by incubation with secondary antibodies diluted in 1X TBST containing 5% nonfat milk . After washing with 1X TBST , detection was performed using the Pierce® ECL Western Blotting Substrate enhanced chemiluminescence system ( Thermo Scientific ) . Primary antibodies used were anti–α-tubulin ( 1:2 , 000; mouse , monoclonal , clone DM1a; Sigma-Aldrich ) and anti-FLAG ( 1:2 , 500; mouse , monoclonal , M2 , Sigma-Aldrich ) . The secondary antibody was horseradish peroxidase ( HRP ) conjugated anti-mouse IgG ( 1:10 , 000; Jackson ImmunoResearch Laboratories ) . Initially , ~2200 candidate genes were selected based on gene ontology ( GO ) terms ( e . g . . “mRNA binding” , “regulation of translation” , “spermatid development” ) . These genes were cross-referenced against publicly available RNAseq data sets ( i . e . : FlyAtlas , modENCODE ) and only those genes predicted to be expressed in the testis were selected . Additionally , candidate genes were eliminated if they are known to be involved in ubiquitous processes ( e . g . general transcription factors , ribosomal subunits ) or processes that are seemingly unrelated to those associated with the Y-loop genes ( e . g . mitochondrial proteins , GSC/SG differentiation , mitotic spindle assembly ) . Finally , candidates were limited to those with available reagents for localization and/or phenotypic analysis , leaving a final list of 67 candidate genes ( S2 File ) . If available , we first analyzed protein localization for each candidate . If candidate proteins did not localize to SCs or the Y-loops , they were not further examined . If the candidate was found to be expressed in SCs or if no localization reagents were available , then RNAi mediated knockdown or mutants were used to examine Y-loop gene expression for any deviations from the expression pattern described in Fig 1D–1H and to assess fertility . As Y-loop genes are all essential for sperm maturation [14] , any genes essential for Y-loop gene expression should also be needed for fertility . All selection criteria and a summary of phenotypes observed can be found in S2 File . Testes were dissected in 1X PBS , transferred to 4% formaldehyde in 1X PBS and fixed for 30 minutes . Testes were then washed in 1X PBST ( PBS containing 0 . 1% Triton-X ) for at least 60 minutes followed by incubation with Phalloidin-Alexa546 ( ThermoFisher , a22283 , 1:200 ) antibody in 3% bovine serum albumin ( BSA ) in 1X PBST at 4°C overnight . Samples were washed for 60 minutes in 1X PBST and mounted in VECTASHIELD with DAPI ( Vector Labs ) . Images were acquired using an upright Leica TCS SP8 confocal microscope with a 63X oil immersion objective lens ( NA = 1 . 4 ) and processed using Adobe Photoshop and ImageJ software . To determine the presence of motile sperm , testes with seminal vesicles were dissected in 1X PBS , transferred to 4% formaldehyde in 1X PBS and fixed for 30 minutes . Testes were then washed in 1X PBST ( PBS containing 0 . 1% Triton-X ) for at least 60 minutes and mounted in VECTASHIELD with DAPI ( Vector Labs ) . Seminal vesicles were then examined by confocal microscopy . The number of sperm nuclei , as determined by DAPI staining , was observed . If comparable to wildtype , the seminal vesicle was scored as having a normal number of motile sperm , if the seminal vesicle contained no detectable sperm nuclei , it was scored as empty and if the seminal vesicle contained only a few sperm , it was scored as greatly reduced . To obtain representative images , seminal vesicles were dissected in 1X PBS and transferred to slides for live observation by phase contrast on a Leica DM5000B microscope with a 40X objective ( NA = 0 . 75 ) and imaged with a QImaging Retiga 2000R Fast 1394 Mono Cooled camera . Images were adjusted in Adobe Photoshop .
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Introns are non-coding elements of eukaryotic genes , often containing important regulatory sequences . Curiously , some genes contain introns so large that more than 99% of the gene locus is non-coding . One of the best-studied large genes , Dystrophin , a causative gene for Duchenne Muscular Dystrophy , spans 2 . 2Mb , only 11kb of which is coding . This phenomenon , ‘intron gigantism’ , is observed across species , yet little is known about its purpose or regulation . Here we identify a unique gene expression program utilized for the proper expression of genes with intron gigantism using Drosophila spermatogenic genes as a model system . We show that the gigantic introns of these genes are transcribed in line with the exons , likely as a single transcript . We identify two RNA-binding proteins that specifically localize to the site of transcription and are needed for the successful transcription or processing of these genes . We propose that genes with intron gigantism require a unique gene expression program , which may serve as a platform to regulate gene expression during cellular differentiation .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[] |
2019
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Satellite DNA-containing gigantic introns in a unique gene expression program during Drosophila spermatogenesis
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Emerging information technologies present new opportunities to reduce the burden of malaria , dengue and other infectious diseases . For example , use of a data management system software package can help disease control programs to better manage and analyze their data , and thus enhances their ability to carry out continuous surveillance , monitor interventions and evaluate control program performance . We describe a novel multi-disease data management system platform ( hereinafter referred to as the system ) with current capacity for dengue and malaria that supports data entry , storage and query . It also allows for production of maps and both standardized and customized reports . The system is comprised exclusively of software components that can be distributed without the user incurring licensing costs . It was designed to maximize the ability of the user to adapt the system to local conditions without involvement of software developers . Key points of system adaptability include 1 ) customizable functionality content by disease , 2 ) configurable roles and permissions , 3 ) customizable user interfaces and display labels and 4 ) configurable information trees including a geographical entity tree and a term tree . The system includes significant portions of functionality that is entirely or in large part re-used across diseases , which provides an economy of scope as new diseases downstream are added to the system at decreased cost . We have developed a system with great potential for aiding disease control programs in their task to reduce the burden of dengue and malaria , including the implementation of integrated vector management programs . Next steps include evaluations of operational implementations of the current system with capacity for dengue and malaria , and the inclusion in the system platform of other important vector-borne diseases .
Emerging information technologies are improving our capacity to predict , prevent and control vector-borne and other infectious diseases [1]–[7] . In North America , for example , the United States Centers for Disease Control and Prevention has implemented a National Electronic Disease Surveillance System to promote rapid collection of standardized disease data [8] . Furthermore , the recent emergence of mosquito-borne West Nile virus in North America resulted in new electronic surveillance systems for mosquito-borne arboviruses both in the United States [9] and Canada [10] . This type of initiative is , however , most commonly achieved through systems that include software components with high acquisition and/or licensing costs , thus preventing system implementation in resource-constrained environments and limiting the potential for using the systems to address the problem of neglected tropical diseases . One way of overcoming this problem is to harness the explosion of new software products , for example those emerging from the open source community [11] , that can be used and distributed without incurring licensing costs . Practical examples of this occurring in public health include the use of Google Earth to map dengue cases in Mexico and Nicaragua , examine the spatial spread of dengue in Mauritius and track polio cases in the Democratic Republic of Congo [12]–[15] . The Innovative Vector Control Consortium recognized the potential for using emerging information technologies to improve vector and disease control program performance and ultimately reduce the burden of tropical vector-borne diseases such as dengue and malaria [16] . This resulted in an initiative that led to the development of the software package described herein: a multi-disease data management system platform ( hereinafter referred to as the system ) with current capacity for dengue and malaria , and with potential for addition of other important tropical vector-borne diseases such as Chagas disease , human African trypanosomiasis , leishmaniasis , lymphatic filariasis and onchocerciasis . To some extent , the system builds upon previous experience with development and implementation of data management systems for malaria in southern Africa [4] , [17]–[20] . Key project goals included 1 ) ensuring that the system can be distributed without the user incurring licensing costs , 2 ) producing a system that can be adapted to local circumstances by the user with no or minimal involvement of software developers , 3 ) achieving a user-friendly system to support data entry , storage and query , as well as production of maps and both standardized and customized reports and 4 ) delivering a system capable of enhancing the user's ability to carry out continuous surveillance , monitor interventions , evaluate control program performance and engage in evidence-based decision making .
System functionalities were developed in an iterative process over an 18-month period with close contact between software developers and subject matter experts including operational field input from Malawi , Mexico , Mozambique , South Africa and Zambia public health partners to ensure that development priorities were in line with actual needs . System functionalities were assessed by positive and negative testing conducted by an internal testing team . Downstream more extensive testing needs to include pilot implementations of the system in different operational settings with naïve users . The system was developed with a 3-tiered architecture ( data tier – application/business logic tier – presentation tier ) and is comprised exclusively of software components that can be distributed to users without licensing costs . The data tier includes a PostgreSQL relational database [21] enhanced with the PostGIS extension [22] to support geographical data . The application/business logic tier includes Java [23] and Apache Tomcat [24] . The presentation tier uses Firefox [25] and is complemented by applications to support production of reports , BIRT [26] , and maps , GeoServer [27] and OpenLayers [28] . To link the tiers , and to allow the user to make changes to the system that automatically are reflected across the 3-tiered architecture , the system includes TerraFrame's Runway SDK application [29] . Runway SDK also was used as a rapid development tool to facilitate the development process . The system requires a minimum of 2 GB RAM , 100 GB hard drive and Intel Core2 2 . 0 GHz to operate on a stand-alone machine ( projected hardware cost of $500–600 per stand-alone desktop ) . It was developed for a Microsoft Windows XP ( Microsoft Corporation , Redmond , WA ) operating system but has been informally tested on and shown to function also for Windows Vista , Windows 7 , Apple Mac OSX ( Apple Inc . , Cupertino , CA ) and Ubuntu [30] . The system installation package , which will be made available on DVD media , includes the system itself , the system manual and stand-alone versions of OpenOffice [31] , to support export/import files if the computer is not already equipped with Microsoft Excel , the reporting tool BIRT , to allow the user to create customized report templates which then can be imported into the system , FWtools to assist the user in transforming spatial data into well known text ( WKT ) format [32] and the application QCal which is specifically designed to calculate dose/time response curves for insecticide resistance bioassays [33] . Downstream we plan to enhance the installation package by also including a stand-alone Dengue Models application , developed by the University of California at Davis and Infectious Disease Analysis , and consisting of upgraded versions of the CIMSiM and DENSiM simulation model applications [34]–[37] . Distribution and licensing of the system is currently executed through the Innovative Vector Control Consortium [38] . There is no cost associated with the license ( the system is a royalty free , licensed software ) .
To minimize data entry error , data entry fields make extensive use of geo entities selected from the geographical entity tree , dates selected from pop-up calendars and pop-up select lists from the term tree . To facilitate use of the geographical entity tree and term tree , which can be tedious to navigate in order to find a specific geo entity or term , the user can simply start typing the name of the geo entity or term that a field should be populated with and be presented with a drop-down list containing all possible options based on what has been typed in so far . The desired geo entity or term can then be selected from the drop-down list . Data querying is accomplished through a set of unique system tools referred to as query builders where the user can define a specific data query ( Figure 6 ) . Separate query builders were developed to handle different functionalities , for example individual disease cases , aggregated disease cases , insecticide resistance bioassays , intervention monitoring relating to indoor residual spraying , etc . All query builders include the capacity to filter a query on 1 ) start and end dates , 2 ) geo entities from the geographical entity tree , 3 ) terms from the term tree , corresponding to the term options available for the data entry field , for query fields that relate to the term tree , 4 ) specific values included in hard-coded select lists and 5 ) numeric values or ranges for query fields based on numerical data ( Figure 6 ) . This provides exceptional potential for the user to easily and rapidly define and execute specific data queries . Many of the query builders also include the option of executing pre-defined custom calculations . For example , the query builders for disease cases include custom calculations for case incidence and case fatality rate , and the query builder that handles information for container collections of mosquito immatures includes custom calculations for commonly used indices such as Breteau index , container index , house index , and pupae per person ( Figure 7 ) . These calculations also can be aggregated to coarser geographical scales than the one at which the data were entered . For example , data entered against county would allow for calculations to be executed against county , state , or country . The functionality relating to pupae recorded by individual container provides a powerful example of the dynamic linkage between the term tree , the data entry screens and the query builders . The data entry screen includes a table where each row represents an individual container and where the numbers of pupae collected by mosquito taxon are entered in columns that are created dynamically from a list of taxa defined under a term tree root ( default list is Ae . aegypti , Ae . albopictus , and Aedes spp . ) . The corresponding query builder includes a separate section for each of these taxa with options for summary numerical data and a set of pre-defined custom calculations . The inclusion of Runway SDK to drive system operations allows for an automatic , adaptive process where the user only needs to go into the term tree and inactivate a default taxon or add a new taxon in order for the system to automatically 1 ) update the dynamic columns in the table in the data entry screen and 2 ) add or remove the section for the affected taxon from the query builder . All query builders also include options to 1 ) export query results as . csv or . xls files , 2 ) save and re-use specific querying field combinations that are executed on a regular basis and 3 ) upload pre-configured BIRT report templates and use these to produce standardized reports ( see buttons at the bottom of the query builder shown in Figure 6 ) . Mapping is directly linked to the query builders in that the map generation process makes use of information that is saved in the query builders as specific named query results . Importantly , mapping supports multiple layer views from different query results that may have been produced through a single query builder or several different ones , for example combining results for entomological surveillance , disease case surveillance and intervention monitoring in a single map . The system also supports export of spatial data in commonly used formats such as shapefile or Keyhole Markup Language ( KML ) file . The multi-disease platform includes functionalities that are entirely or in large part re-used across diseases . This includes the administration functionalities , stock management , GIS , case surveillance ( re-used in large part across dengue and malaria ) , and most of the functionalities relating to entomological surveillance . Because the platform at this point only includes two diseases , some of the functionalities are relevant only for dengue or malaria . However , as more vector-borne diseases are added to the platform , these functionalities also will become re-used across diseases . For example , container surveillance of mosquito immatures would be directly applicable to other arboviral diseases where the causative agent is transmitted by Ae . aegypti , such as chikungunya or yellow fever . Intervention monitoring provides other examples of functionalities that will be re-used when additional vector-borne diseases where similar control methods are employed are added to the system platform . Some data in the system are shared between diseases because they are relevant across diseases . This includes universal terms and human population data recorded against geo entities representing administrative boundary units or health facilities . Other data are shared across diseases but can be made active or inactive by disease; this includes , for example , the terms in the term tree . To avoid cluttering the menu for a given disease with functionalities that are unlikely to be relevant for that disease , the system comes with a default set of functionalities by disease . Functionalities which in the default system for dengue and malaria are included only for the malaria menu include 1 ) surveys , where the data captured largely conform to the malaria indicator survey developed by the Roll Back Malaria Partnership's Monitoring and Evaluation Reference Group [44]–[45] , 2 ) planning and monitoring of indoor residual spraying programs , 3 ) monitoring of interventions based on use of insecticide-treated nets or intermittent preventive treatment for pregnant women with anti-malarial drugs and 4 ) monitoring of control of anopheline mosquito immatures that inhabit non-container aquatic habitats , which may range in size from cattle hoof prints or small puddles to rice fields and lake shores . Functionalities which are included only for the dengue menu include 1 ) container-based surveillance of immatures of key dengue virus vectors , such as Ae . aegypti and Ae . albopictus , which exploit a wide range of containers ( e . g . , water storage containers , tires , bottles , cans , flower pots , etc . ) as larval development sites [46]–[47] and 2 ) capture of summary data , by individual premises or aggregated to larger spatial units such as blocks or neighborhoods , for intervention methods used in a given geographical area during a specific time period . Our multi-disease system incorporates a broad range of information including entomological , epidemiological , stock and spatial data . This sets the stage for using the system to support integrated vector management ( IVM ) which uses a wide range of interventions , often in combination and synergistically [48]–[49] . For example , the functionality for intervention monitoring in the dengue menu allows for capture of data , in space and time , for different implemented intervention methods ( the methods are defined by the user through the term tree ) used as parts of an IVM program . These data , together with data for entomological and epidemiological surveillance captured in other parts of the system , can then be used to assess the impact of the IVM program on entomological and epidemiological outcome measures such as vector abundance or presence or prevalence of infection in the human population .
Plans are now underway to trial the system operationally with control program partners so that it can be rigorously evaluated . Ideally , the system should be evaluated in parallel with existing management systems for a 1–2 year period in multiple settings for each relevant disease . This will also provide valuable feedback on system performance and lead to improvements in later versions of the system . The system has exceptional potential for adaptation by the user to local circumstances without the involvement of software developers . The cost of this versatility is increased complexity for the system administrator . Implementation and operational use of the system without a highly competent local system administrator is likely to result in poor system performance , especially with regards to synchronization of data . The complexity is most apparent during the initial system configuration . The system includes extensive capacity for data import , including import spreadsheets that are tailored to specific functional components such as entry of individual disease cases , entry of data for insecticide resistance bioassays , entry of survey data , entry of data for distribution of insecticide-treated nets , etc . This allows the user to rapidly populate the system with historical data . However , it should be noted that the import process , for data quality purposes , is unforgiving when it comes to poor quality data . Therefore , it often will be necessary to clean historical data that originate from other sources , which may not enforce input of high quality data , before importing the data into the system . Furthermore , the behavior of the import process for a given import spreadsheet is linked to the behavior of the corresponding data entry screen , for example with regards to data fields where an entry is mandatory . Thus , it becomes important that all personnel executing data imports also have a working knowledge of the corresponding manual data entry functionalities in the system . The data import/export functionality also allows for linkage to existing health information systems by import or export of relevant disease case data . Initial incompatibility issues are expected , especially with regards to names of geo entities , due to inconsistency in spellings or name changes . For geo entities , this can be addressed through an import synonym tool which is included in the system and assists the user in finding names for a given geo entity that closely resembles the one the user is attempting to include in the import . The user then can add the name of the geo entity from the external health information system as a synonym for the same geo entity in the geographical entity tree in our system , which will facilitate subsequent data imports . Finally , the system still lacks capacity for mass-deletion of data , which can become an issue if the data import process is not handled carefully . The information trees ( geographical entity tree , universal tree and term tree ) provide tremendous adaptability in the system but require careful consideration when they are configured for a local implementation . There is no question that poorly configured information trees will lead to downstream problems with the operational use of the system . It is important that the team executing the initial configuration of the information trees has domain expertise relating to vector and disease surveillance and control practices in the local environment , as well as a clear idea of what the local user wants to get out of the system in terms of specific outputs to support decision-making and reporting . The system comes with a default term tree and the universal tree is not onerous to configure . Data for geo entities can be imported into the system by the user to build a locally relevant geographical entity tree . This is , however , restricted to import for a single geographical hierarchical level at a time , which can make the process of building the geographical entity tree time-consuming . Alternatively , the data making up the geographical entity tree can be mass-imported but this currently requires assistance by software developers . The system was developed primarily to support operational disease control programs and therefore has very limited statistical and spatial analysis capacity . Statistical operations that are directly supported in the system are restricted to 1 ) query builder calculations of sums , averages , and minimum and maximum values and 2 ) pre-configured query builder custom calculations that relate to specific system functionalities , such as disease case incidence or mosquito abundance indices . Other statistical operations require the user to export data for subsequent import into a statistical software package . The system supports , through the use of GeoServer/OpenLayers , basic mapping functions but essentially lacks spatial analysis capacity . The system is capable of producing map overlays to illustrate spatial patterns , for example disease case incidence in relation to percentage coverage by a given control intervention , but lacks capacity for applying spatial statistics to further explore these patterns . To achieve this , the user needs to export a shapefile from the system for subsequent import into a GIS software with spatial analysis capacity . The most important short-term future directions are pilot implementations of the system , including assessments of the cost for system set-up and operation , and the inclusion in the multi-disease data management system platform of additional important vector-borne diseases such as Chagas disease , human African trypanosomiasis , leishmaniasis , lymphatic filariasis and onchocerciasis . Additional future plans include 1 ) making the system directly compatible with hand-held mobile data capturing technologies , such as Personal Digital Assistants and smartphones , 2 ) developing an over-arching query builder to make it easier to combine data from different parts of the system , 3 ) developing additional user-configurable functionalities such as configurable indicator surveys or knowledge , attitudes and practices surveys and 4 ) determining the potential for expanding the system to include infectious diseases with other modes of pathogen transmission than arthropod vectors ( e . g . , through ingestion of water or direct human-to-human contact ) .
|
Emerging information technologies , such as data management system software packages , can help disease control programs to better manage and analyze their data , and thus make it easier to carry out continuous surveillance , monitor interventions and evaluate control program performance . This will lead to better informed decisions and actions . We have developed a multi-disease data management system platform with current capacity for dengue and malaria that supports data entry , storage and query . It also allows for production of maps and both standardized and customized reports . The system includes only software components that can be distributed without the user having to pay licensing costs . It was designed so that the user can adapt many aspects of the system to suit local conditions ( for example roles and permissions , user interfaces and display labels and which functionality is included under a given disease ) without having to involve software developers . In conclusion , we have developed a system capable of aiding disease control programs in their task to reduce the burden of dengue and malaria , including the implementation of integrated vector management programs . The next steps include operational implementations and evaluations of the current system with capacity for dengue and malaria , and the inclusion in the system platform of other important vector-borne diseases .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"infectious",
"diseases",
"computer",
"science",
"mosquitoes",
"dengue",
"fever",
"software",
"engineering",
"neglected",
"tropical",
"diseases",
"malaria",
"information",
"technology",
"software",
"tools",
"parasitic",
"diseases",
"vectors",
"and",
"hosts",
"infectious",
"disease",
"control"
] |
2011
|
Multi-Disease Data Management System Platform for Vector-Borne Diseases
|
Necroptosis is a lytic programmed cell death mediated by the RIPK1-RIPK3-MLKL pathway . The loss of Receptor-interacting serine/threonine-protein kinase 3 ( RIPK3 ) expression and necroptotic potential have been previously reported in several cancer cell lines; however , the extent of this loss across cancer types , as well as its mutational drivers , were unknown . Here , we show that RIPK3 expression loss occurs progressively during tumor growth both in patient tumor biopsies and tumor xenograft models . Using a cell-based necroptosis sensitivity screen of 941 cancer cell lines , we find that escape from necroptosis is prevalent across cancer types , with an incidence rate of 83% . Genome-wide bioinformatics analysis of this differential necroptosis sensitivity data in the context of differential gene expression and mutation data across the cell lines identified various factors that correlate with resistance to necroptosis and loss of RIPK3 expression , including oncogenes BRAF and AXL . Inhibition of these oncogenes can rescue the RIPK3 expression loss and regain of necroptosis sensitivity . This genome-wide analysis also identifies that the loss of RIPK3 expression is the primary factor correlating with escape from necroptosis . Thus , we conclude that necroptosis resistance of cancer cells is common and is oncogene driven , suggesting that escape from necroptosis could be a potential hallmark of cancer , similar to escape from apoptosis .
Necroptosis is a necrotic programmed cell death pathway mediated by the RIPK1-RIPK3-MLKL signaling cascade [1–4] . Receptor-interacting serine/threonine-protein kinase 1 ( RIPK1 ) can be activated when cells are stimulated by Tumor necrosis factor alpha ( TNFα ) , Fas , or TRAIL ligands as well as downstream of Toll-like receptors [5 , 6] . Cells can be sensitized to necroptosis by repressing function of the inhibitor of apoptosis proteins ( IAPs: cIAP1 , cIAP2 , and XIAP ) by Smac mimetics , such as SM-164 , while caspase inhibition by a pan-caspase inhibitor such as zVAD . fmk also further sensitizes cells to necroptosis [5 , 7 , 8] . During necroptosis activation , RIPK1 interacts with Receptor-interacting serine/threonine-protein kinase 3 ( RIPK3 ) to form the necrosome , which in turn phosphorylates pseudokinase Mixed lineage kinase domain-like protein ( MLKL ) to mediate necrotic cell death via plasma membrane rupture [9–17] . In addition to necroptosis [9 , 17–21] , RIPK3 has been implicated in regulation of antitumor immunity [22] , apoptosis [6 , 11 , 23–29] , and cytokine production [30 , 31] . While RIPK3 expression has been shown to be lost in several cancer cell lines and cancer types [18 , 21 , 32–34] , no systematic evidence for the extent of this loss across cancer types or the mechanisms driving this loss have been reported . The Tyro3 , Axl , Mer ( TAM ) receptor family of tyrosine kinases plays a role in regulating cell growth , survival , and proliferation [35 , 36] . TAM kinases are oncogenes , frequently amplified in a variety of cancers , in which their overexpression correlates with poor patient survival [36–40] . Importantly , while TAM kinases are anti-apoptotic and are established as important mediators of resolution of inflammation [41] , their roles in the context of necroptosis have not been studied . BRAF is a major regulator of protein synthesis , cell survival , growth , and proliferation [42] . Overactivation of BRAF is observed in a vast majority of cancers [42–45] . Importantly , while BRAF is an established anti-apoptotic kinase , its role in the regulation of necroptosis is unknown . In this study , we performed a necroptosis sensitivity screen in 941 human cancer cell lines to identify the mutational drivers of the RIPK3 expression loss and the consequent escape from necroptosis . We identified the oncogenic kinases BRAF and AXL , which were validated as potential mediators of this process , because their inhibition can rescue the loss of RIPK3 expression and result in regain of sensitivity to necroptosis . Interestingly , our tumor xenograft studies , as well as transcriptomics analyses of published RNAseq/microarray datasets of patient tumor biopsy samples , show that RIPK3 expression is lost progressively during tumorigenesis . Our results reveal a potential role of BRAF and AXL oncogenes in driving the loss of RIPK3 expression and escape from necroptosis in various cancers .
In order to understand the relevance of necroptosis in tumor growth and the in vivo kinetics of the RIPK3 expression loss during tumorigenesis , we evaluated the changes in RIPK3 mRNA levels in published transcriptomics datasets . Six patient tumor biopsy studies [46–51] and one cancer cell line xenograft study [52] were analyzed . We found that RIPK3 mRNA levels were progressively lost during tumor growth in colorectal , gastric , and ovarian cancer patients ( Fig 1A ) . Notably , the loss of RIPK3 expression also associated with the progression to metastasis in human prostate tumors , and higher-grade adrenocortical and breast tumors ( Fig 1A ) . Moreover , RIPK3 expression was also progressively lost during in vivo passaging of tumor xenografts using 47 human cancer cell lines , in which the majority of the cell lines showed a strong loss of RIPK3 expression at passage 10 , compared to passage 1 , with some heterogeneity in the extent of the loss in a fraction of the cell lines ( Fig 1B and 1C ) . Because the most robust RIPK3 expression loss was observed in ovarian cancer biopsies ( Fig 1A ) , we performed a newly derived patient-derived xenograft ( PDX ) study using primary cells obtained from high-grade serous ovarian cancer biopsies , in order to determine whether necroptosis is physiologically activated in tumors and whether RIPK3 protein levels indeed are lost during tumorigenesis progression . We found that the expression of RIPK3 , but not that of RIPK1 , was progressively reduced during xenograft tumor growth in four out of five PDX samples derived from high-grade serous ovarian cancer biopsies ( Fig 1D and 1E , S1A Fig ) . In addition , we found that MLKL was phosphorylated at Ser358 in tumors at early in vivo xenograft passages ( passage 0 ) , revealing that the necroptosis pathway is endogenously activated in tumors . Consistent with the loss of RIPK3 expression , MLKL phospho-Ser358 levels decreased as a function of serial in vivo passage of the PDXs ( Fig 1D and 1E ) . Importantly , while ex vivo–cultured tumor xenograft cells were sensitive to TNFα+SM-164+zVAD . fmk ( TSZ ) -induced necroptosis at passage zero , they were fully resistant after the third in vivo serial xenograft , and because of the resistance to cell death , this treatment of TSZ did not induce cell death , but rather induced cell growth resulting in an approximately 140% survival rate ( Fig 1F and S1B Fig ) . The TSZ-induced necroptosis in these cells was potently blocked by 10 μM of the RIPK1 inhibitor Nec-1s and 10 μM of the RIPK3 inhibitor GSK’872 , and was also blocked by 10 μM of the MLKL inhibitor necrosulfonamide ( NSA ) ( S1B and S1C Fig ) . These findings reveal that the loss of RIPK3 expression occurs progressively during tumorigenesis in vivo and that necroptosis is activated in tumors that express RIPK3 . In order to identify the mechanisms driving RIPK3 expression loss in cancer cells , we performed a necroptosis sensitivity screen using a panel of 941 human cancer cell lines from the Genomics of Drug Sensitivity in Cancer ( GDSC ) collection , which represent various cancer types from 28 tissues [53 , 54] . A potent TNFα + SM-164 + zVAD . fmk ( TSZ ) treatment was used to stimulate necroptotic cell death under nine different SM-164 concentration conditions in the 4–1 , 024 nM range ( Fig 2A ) . Remarkably , we found that 780 ( 83% ) of these cell lines were fully resistant to necroptosis induced by TSZ even at the highest SM-164 concentration ( Fig 2B and 2C , S1 and S2 Tables ) . These screen results were validated by testing 23 randomly selected cancer cell lines , which showed a complete resistance to TSZ- and TNFα+Cycloheximide+zVAD . fmk ( TCZ ) -induced necroptosis , lack of RIPK3 expression , and lack of MLKL Ser358 phosphorylation upon stimulation with TSZ treatment ( Fig 2D and 2E and S2A Table ) . Out of 28 tissue types from which the cancer cell lines were derived , 8 tissue types were found to have no sensitive cell lines , and no tissue type was found to lack resistant lines ( S2B and S2C Fig ) . Together , these results suggest that the escape from necroptosis is found in most cancer cell lines , independent of tissue and cancer type . Having established that RIPK3 expression loss is observed during tumorigenesis ( Fig 1 ) and that this loss is prevalent across cancer types ( Fig 2B ) , we next set out to identify drivers of this loss . We performed genome-wide Pearson correlation analysis using the mRNA expression datasets from both GDSC and Broad-Novartis Cancer Cell Line Encyclopedia [55] ( CCLE ) in order to identify genes whose elevated expression correlates with high TSZ-IC50 values ( i . e . , resistance to necroptosis ) . We used both databases because the GDSC and the CCLE database cell line collections overlap and the expression values obtained from two independent sources would increase the confidence in the obtained correlation results . Our correlation analyses revealed 634 genes whose expression positively correlated with the resistance to necroptosis ( p < 0 . 01 , Bonferroni correction ) . RIPK3 expression was the most negatively correlated with resistance to necroptosis ( Pearson coefficient = −0 . 43 , p = 4 . 11 × 10−24 ) and its low expression was significantly enriched in necroptosis-resistant ( NR ) cell lines , confirming the validity of the screen and the analysis strategy ( Fig 2F and S3A Fig ) . Consistently with its key role in necroptosis , MLKL expression also negatively correlated with resistance to necroptosis ( Pearson coefficient = −0 . 25 , p = 8 . 45 × 10−7 ) , while RIPK1 expression did not ( Fig 2F ) . Importantly , 20 of these genes were known to be classified as oncogenes or genes that promote oncogenic transformation ( see Materials and methods for the bioinformatics analysis description ) ( S3B Fig ) . Out of the 20 oncogene-related genes , we focused our subsequent experiments on AXL , because ( a ) its family member TYRO3 was also among the 634 genes that positively correlate with resistance to necroptosis; ( b ) out of the two TAM kinase family members , AXL expression showed the strongest positive correlation with TSZ-IC50 ( AXL: Pearson coefficient = 0 . 21 , p = 2 . 91 × 10−5; TYRO3: Pearson coefficient = 0 . 10 , p = 0 . 017 ) ; and ( c ) AXL is the predominant TAM kinase family member that is frequently overexpressed in cancer . Importantly , transcriptomics analysis of the screened 941 cancer cell lines revealed that high AXL and TYRO3 mRNA levels predict both resistance to necroptosis and low RIPK3 mRNA levels ( Figs 2F and 3A–3D , S3 Table ) , but not those of RIPK1 , MLKL , or any other pro-necroptotic genes ( S4A Fig ) . AXL expression levels also negatively correlated with RIPK3 expression in stomach adenocarcinoma tumors and acute myeloid leukemia ( S4B Fig ) , based on the analysis of the Cancer Genome Atlas ( TCGA ) database [56] using cBio Cancer Genomics Portal [51] and according to both Pearson and Spearman correlation analyses . A similar positive correlation between AXL expression and TSZ-IC50 , as well as a negative correlation between AXL-RIPK3 expression levels , was observed when expression values from the CCLE database were used for the analysis ( S5A and S5B Fig ) . Quartile analysis of the data also confirmed these Pearson correlation observations ( S5C and S5D Fig ) . Clustering analysis ( Fig 3D ) revealed that majority of the analyzed cancer cell lines that are resistant to necroptosis ( high IC50 , cluster 1 , about 83% ) are either AXLhigh ( cluster 2 , about 28% ) or TYRO3high ( cluster 3 , about 14% ) , while the majority of those that are sensitive to necroptosis ( low IC50 ) are RIPK3high and have low/medium AXL/TYRO3 levels ( cluster 4 , about 19% ) . While the majority of the cells in the cluster 4 were RIPK3high , a fraction were RIPK3low , suggesting that high RIPK3 mRNA levels are not a prerequisite to undergo necroptosis and that sufficient RIPK3 protein is expressed in these cells to undergo necroptotic cell death . However , RIPK3 expression levels were more heterogeneous than the TSZ-IC50 values , and about 18% of the cell lines were fully resistant to necroptosis despite the presence of RIPK3 expression ( clusters 5 and 6 ) , suggesting that the escape from necroptosis may not be only due to loss of RIPK3 expression . Moreover , not all cell lines with high AXL/TYRO3 levels had lost RIPK3 expression ( cluster 5 ) . Additionally , about 14% of the cell lines with low RIPK3 levels and resistance to necroptosis did not have high AXL/TYRO3 levels , suggesting that other RIPK3 loss-driving forces may exist ( cluster 7 ) . Overall , this analysis revealed a great degree of heterogeneity in AXL/TYRO3 and RIPK3 expression levels and resistance to necroptosis in the screened lines , as well as the presence of high AXL/TYRO3 and concomitant low RIPK3 expression levels in about 56% of the NR lines , suggesting that high expression levels of AXL/TYRO3 could be potential predictors/biomarkers for loss of RIPK3 expression and necroptosis resistance in cancer . A 4-day treatment of A375 and SkMel28 cancer cell lines , which have no initial RIPK3 expression ( but also no genetic mutations of RIPK3 ) , with low concentrations of AXL/TYRO3 inhibitor BMS-777607 resulted in a regain of RIPK3 expression at both mRNA and protein levels ( Fig 3E ) . Importantly , this treatment also restored the sensitivity of these cells to TSZ-induced necroptosis ( Fig 3F ) . Overall , these findings suggest that AXL/TYRO3 overexpression , frequently seen in cancers , promotes the loss of RIPK3 expression and escape from necroptosis , which may be reversed upon inhibition of these kinases . Moreover , high AXL/TYRO3 levels are potential predictors/biomarkers for loss of RIPK3 expression and necroptosis resistance in cancer . Using the differential sensitivity to necroptosis data from the cell-based screen , we performed a second round of bioinformatics analysis with a focus on genome-wide mutational enrichment in NR ( fully resistant to necroptosis even at 1 μM of SM-164 ) versus necroptosis-sensitive ( NS ) cell lines . Our analysis revealed that several oncogenic mutations , including those of BRAF , are strongly enriched in the NR cell lines , compared to the NS cell lines ( Fig 4A and 4B ) . Interestingly , 75 of the NR cell lines were found to have high RIPK3 expression ( S6A Fig ) . Mutational enrichment analysis of the NR-RIPK3high versus NR-RIPK3low populations revealed 73 interesting genes , mutations of which may lead to necroptosis resistance via alternative pathways , independent of the RIPK3 expression suppression ( S6B Fig ) . Due to the importance of BRAF overactivation in cancer , we next focused on this oncogene . Transcriptomics analysis of the screened cell lines showed that mutations that lead to overactivation of BRAF can predict the loss of RIPK3 expression levels in cancer , despite many of the BRAFWT cell lines displaying low RIPK3 expression , consistent with its heterogeneous nature ( Fig 4C , S4 and S5 Tables ) , similar to that of high AXL expression levels . These results raised the question of whether inhibition of BRAF , similar to that of AXL , could also result in reversal of the RIPK3 expression loss . Indeed , a transcriptomics study [57] analyzing melanoma patient tumor biopsies before and after treatments with BRAF inhibitors Dabrafenib and Vemurafenib revealed that RIPK3 expression was increased by at least 1 . 2-fold in 58 . 3% of the patients and decreased by least 1 . 2-fold in 25% of the patients , while no change was observed in 16 . 7% of the patients , consistent with the heterogeneous nature of RIPK3 expression loss ( Fig 4D ) . Importantly , treatment of ES2 and SkMel28 cell lines , both of which carry an activating BRAF V600E mutation and have no initial RIPK3 expression , with low concentrations of BRAF inhibitor TAK-632 for 4 days resulted in an up-regulation of RIPK3 expression ( Fig 4E ) . Importantly , this treatment also restored the sensitivity of these cells to TSZ-induced necroptosis ( Fig 4F ) . These findings suggest that oncogenic BRAF overactivation promotes the loss of RIPK3 expression and escape from necroptosis , which may be reversed upon inhibition of BRAF . Moreover , BRAF overactivating mutations are potential predictors/biomarkers for loss of RIPK3 expression and necroptosis resistance in cancer .
Here , we establish that necroptosis resistance can be found in high percentages of cancer cell lines derived from cancers of different tissue and cell type origins . We discover BRAF and AXL as the first two oncogenes that can drive the loss of RIPK3 expression in cancer cells ( Fig 5 ) . BRAF gain-of-function mutations and AXL overexpression , which are both observed in various cancers at high frequencies , are important therapeutic targets for the treatment of cancers . Interestingly , we found that the expression of RIPK3 may be restored upon inhibition of BRAF and AXL . The loss of RIPK3 is a heterogeneous event , and its extent differs across various cancer cases , as can be seen from the screen data ( Fig 2 and Fig 3 ) and the xenograft data ( Fig 1 and S1A Fig ) . However , the prevalent loss of RIPK3 expression ( Fig 3D ) and resistance to necroptosis may be an important factor to consider during design of anticancer therapies . Our results suggest that therapies targeting key oncogenes BRAF and AXL result in a regain of RIPK3 expression in cancers that have lost it . Therefore , combinations of the compounds targeting these oncogenes with strategies that aim to induce necroptosis in tumors might augment the therapeutic benefit , because the regain of RIPK3 expression induced by the BRAF or AXL inhibitors is expected to render the tumors necroptosis sensitive . However , because we show tumors undergo necroptosis in vivo , this inflammatory mode of cell death could positively contribute to tumor growth . Therefore , one needs to consider the potentially negative consequences of reactivating necroptosis by inducing the lost RIPK3 expression , because increase in necroptosis and inflammation can fuel tumor growth . On the other hand , RIPK3 has been shown to be important for CD8+ T-cell cross-priming and antitumor immunity [22]; therefore , inducing RIPK3 expression in tumor cells could increase their clearance by CD8+ T cells . Thus , induction of RIPK3 expression in cancer could prove to be a double-edged sword . Furthermore , while RIPK3-induced cytokine production and necroptosis-induced inflammation ( or necroinflammation ) [58] can fuel the tumor cell growth , such RIPK3-dependent processes may also promote antitumor immunity and programmed cell death of the tumor cells . It is conceivable that uncoupling necroptotic cell death , the pro-growth inflammation it brings , and CD8+ T-cell cross-priming induction could bring forward the benefits of RIPK3 expression induction in cancer ( i . e . , antitumor immunity stimulation via cross-priming ) and diminish its disadvantages ( inflammation , increased tumor growth and necrosis ) . The presence of MLKL phospho-Ser358 marker in the tumor xenografts ( Fig 1D ) may also indicate that other roles of MLKL unrelated to cell death are at play during tumorigenesis , because phosphorylation of MLKL at this residue has been shown to be not sufficient to commit to necrotic cell death , as demonstrated in a recent study that links the ESCRT-III complex downstream of MLKL [59] . For instance , during tumorigenesis , MLKL/ESCRT-III pathway could be promoting CD8+ T-cell cross-priming and enhancing antitumor immunity , as ESCRT-III was found to be involved in cross-priming by necroptotic cells [59] . Investigating the role of the RIPK3 expression regain in cancer resistance and tumor regrowth in patients following BRAF inhibitor therapies ( e . g . , melanoma ) could be of importance to explain this clinically vital phenomenon . We found that 38 out of 39 melanoma cell lines that have an activating BRAF mutation are fully resistant to necroptosis and have lost RIPK3 expression ( S5 Table ) . Thus , according to our findings , RIPK3 expression is expected to be induced in most anti-melanoma therapies that employ mutant BRAF-specific inhibitors . It would be important to investigate if the regain of RIPK3 expression plays a role in the success or failure of BRAF-targeting therapies , in order to enhance the success rate and overcome the failures . We analyzed the mutational status of BRAF and AXL kinases in the cell lines used for the aforementioned xenograft transcriptomics study . Consistent with the notion that oncogenic BRAF and AXL kinases promote the loss of RIPK3 expression in cancer cells , 14 out of 20 cell lines that harbor mutations promoting BRAF activation or high levels of AXL ( or TYRO3 ) experienced loss of RIPK3 expression during in vivo passaging , while 13 out of 16 cell lines that lack such mutations did not experience that effect ( S6 Table ) . The latter set of cells provides a crucial negative control and further supports that BRAF and AXL overactivation in cancer may drive the loss of RIPK3 during tumor progression . It is possible that the selective pressure to lose RIPK3 expression during tumorigenesis comes from the necessity to evade immunity . For example , loss of RIPK3 in tumors would result in decreased cross-priming [22] and increased escape from immunity , thus benefiting tumor survival and growth , but inadvertently it would also result in loss of necroptosis potential because of the essentiality of RIPK3 for necroptosis . Thus , the cost/benefit for a tumor to lose RIPK3 expression could be dependent on the extent of necessity for the tumor cells to evade the immunity of the patient . This could explain why some cell lines obtained from the patients still had not lost RIPK3 expression but lose it when xenografted into mice . Our in vivo results and published tumor xenograft experiments using immunocompromised animals show that the adaptive immune response ( e . g . , T cells ) is not necessary for the loss of RIPK3 expression in tumor cells . Hence , our findings suggest that RIPK3 loss may be dependent on a tumor cell–intrinsic mechanism in vivo or due to interactions with stromal or innate immune cells . RAS isoforms , known to be upstream of BRAF , were found among 20 oncogenes identified to positively correlate with resistance to necroptosis , further suggesting the involvement of BRAF in escape from necroptosis ( S3A Fig ) . Cancer cell lines with BRAF mutations did not show as high correlation between AXL overexpression and RIPK3 as those with wild-type BRAF , suggesting that oncogenic pressure from either BRAF or AXL is sufficient to promote RIPK3 expression loss , and escape from necroptosis in cancer ( S7 Fig ) . Overall , these observations strongly suggest that pathways downstream of BRAF and AXL are responsible for RIPK3 expression suppression and escape from necroptosis in cancer . RIPK3 expression has been previously shown to be controlled via transcriptional repression mechanisms that include promoter hypermethylation and regulation via transcription factor Sp1 [21 , 60] . BRAF and AXL pathways are known to regulate many transcription factors , including JUN , FOS , ETS , and MYC . It is possible that the pathways overactivated upon mutational overactivation of BRAF/AXL converge on a set of transcription factors that control RIPK3 expression during tumorigenesis . Interestingly , BRAF overactivating mutations have been previously linked to promoter hypermethylation of various genes [61–63] . The delineation of the exact mechanistic details downstream of BRAF/AXL and upstream of transcription factors that control RIPK3 transcription is likely to be of importance to our understanding of cancer escape from necroptosis and will be elucidated in future studies . Notably , both ABIN-1 and OPTN expression levels were found to strongly correlate with AXL expression across the analyzed 1 , 000 cell lines ( S4A Fig ) . It is noteworthy that both of these ubiquitin-binding proteins have recently been linked to the regulation of RIPK1 activation in necroptosis [64 , 65] . Whether AXL regulates apoptosis and necroptosis via controlling expression of these ubiquitin chain adapters will be elucidated in future studies . It is interesting that both BRAF and RIPK3 are in the same kinome branch , namely , in the tyrosine-kinase like ( TKL ) family of kinases [66] . Notably , several BRAF inhibitors have been reported to inhibit RIPK3 kinase activity , highlighting this similarity in the kinase domain structure [67] . Such structural similarity suggests a potential convergence and importance of the TKL family in the regulation of processes involving RIPK3 , including necroptosis , cytokine production , and immunity . In fact , many of the members of the TKL family include regulators of these processes , such as RIPK1 , RIPK2 , RIPK3 , MLKL , and TAK1 as well as IRAK and LRRK kinases [66] . In conclusion , we provide the first systematic evidence that most human cancer cell lines escape from necroptosis , independent of their tissue of origin or cancer type , and identify the first two oncogenic alterations upstream of the RIPK3 expression suppression . We show that BRAF and AXL oncogene overactivation in cancers is likely to be among the driving forces for the loss of RIPK3 during tumorigenesis and the consequent escape from necroptosis , as well as other RIPK3-driven processes . Understanding the mechanism of escape from necroptosis in tumorigenesis is likely to pave the way for development of better anticancer therapies .
BMS-777607 and TAK-632 were purchased from SelleckChem ( Houston , TX ) . Luminol ( A8511 ) , p-coumaric acid ( C9008 ) , Tween 20 , and zVAD . fmk were from Sigma ( St . Louis , MO ) . DMSO ( sc-20258 ) was from Santa Cruz Biotechnology ( Santa Cruz , CA ) . The following antibodies were used in this study: RIPK1 ( Cell Signallng Technology [Danvers , MA] , #3493 ) ; p-MLKL ( S358 ) ( Abcam ( Cambridge , UK ) , ab187091 ) ; hMLKL ( Abcam [Cambridge , UK] , ab183770 ) ; and Actin ( Santa Cruz Biotechnology ( Santa Cruz , CA ) , sc-81178 ) . Smac mimetic SM-164 was custom synthesized ( SelleckChem [Houston , TX] ) [7] . TNFα was from Cell Sciences ( Newburyport , MA ) . All cell lines were grown in RPMI or DMEM medium ( Corning , with L-glutamine , with 4 . 5 g/L glucose , without pyruvate ) supplemented with 10% FBS ( Sigma ) , 1× penicillin/streptomycin ( Life Technologies ) , 1 μg/mL amphotericin B ( Santa Cruz Biotechnology , sc-202462A ) , 1× non-essential amino acids mix ( NEAA MEM ) ( Gibco , Life Technologies ) and 1 mM sodium pyruvate ( Gibco , Life Technologies ) . High-throughput drug screening and sensitivity modeling ( curve fitting and IC50 estimation ) was performed essentially as described previously [53] . Cells were cultured in RPMI or DMEM/F12 containing 5% FBS and penicillin/streptomycin . Cells were incubated at 37°C in a humidified atmosphere with 5% CO2 . Cells were grown in RPMI or DMEM/F12 in order to minimize the potential effect of different cell culture media on the drug sensitivity during the screening . A panel of 92 SNPs was profiled for each cell line ( Sequenom , San Diego , CA ) , in order to authenticate the cell lines and thus rule out cross-contamination . A pairwise comparison score was calculated for this purpose . Moreover , short tandem repeat ( STR ) analysis ( AmpFlSTR Identifiler , Applied Biosystems , Carlsbad , CA ) was done on the cell lines and the results were matched to existing STR signatures from the repository that provided the cell lines . Briefly , cells were seeded in 384-well plates at variable density to ensure optimal proliferation during the assay . Drugs were added to the cells the day after seeding for adherent cell lines and the day of seeding for suspension cell lines . For tumor subtypes containing both adherent and suspension cells , all lines were drugged the same day ( small cell lung cancer cell lines , for example , were all drugged the day after seeding ) . A series of nine doses was used using a 2-fold dilution factor for a total concentration range of 256-fold . Maximum concentration was chosen for each drug based on prior knowledge of activity on target and in cells . Viability was determined using resazurin after 5 days of drug exposure . Cell lines were treated with TSZ: TNFα ( fixed dose 20 ng/mL ) + ZVAD ( fixed dose 20 μM ) + Variable dose of SM-164 ( Max of 1 . 024 μM ) . Total cell lysates ( 20–30 μg ) were heated at 90° for 5 minutes in 1× SDS-PAGE sample buffer ( 2% SDS , 1% beta-mercaptoethanol , 0 . 01% bromophenol blue , 50% glycerol , 63 mM Tris-HCl , pH 6 . 8 ) , subjected to 10% SDS-PAGE using Bio-Rad’s Mini-PROTEAN Electrophoresis System , and then electrotransferred onto 0 . 2-μm nitrocellulose membranes ( buffer: 5 . 82 g/L Tris , 2 . 93 g/L glycine , 20% ethanol ) for 2 hours at 0 . 4 A current , with the wet transfer tank submerged into an ice/water bath using Bio-Rad’s Trans-Blot cell . Membranes were blocked for 1 hour in TBST buffer containing 5% ( w/v ) nonfat milk and probed with the indicated antibodies in TBST containing 5% ( w/v ) BSA for 16 hours at 4° . Detection was performed using HRP-conjugated secondary antibodies and in-house-made chemiluminescence reagent ( 2 . 5 mM luminol , 0 . 4 mM p-coumaric acid , 100 mM Tris-HCl , pH 8 . 6 , 0 . 018% H2O2 ) . Cells were seeded into 24-well plates in 1 mL of medium at 15%–20% confluence . Cells were treated 16–24 hours later with BMS-777607 , TAK-632 , or Vemurafenib ( 0 . 3–3 μM ) for 96 hours . Cells were washed twice with 1 mL of medium ( 5-minute incubation at 37° for each wash ) and pretreated with 0 . 5 μM SM-164 and 30 μM zVAD . fmk for 30 min with a subsequent treatment with 25 ng/mL hTNFα for 24 hours to induce necroptosis . Cell survival was determined using CellTiterGlo ( Promega ) kit according to manufacturer’s instructions . Equal volumes of the reagent were added to the culture medium and the 24-well plates were incubated at 25° for 10 minutes in the dark , with agitation . A total of 25 μL of the obtained lysates were transferred into opaque 384-well plates and luminescence was measured at 100 sensitivity setting with 0 . 2 seconds integration time , using BioTek Synergy 2 plate reader . For RIPK3 expression analysis , cells were lysed in RLT buffer of the RNeasy kit ( Qiagen ) . RNA was isolated using RNeasy kit ( Qiagen ) and cDNA synthesis was performed using RNA to cDNA EcoDry Premix ( Double Primed ) ( Takara Bio ) . A total of 1 μg of RNA was used per premix tube . Quantitative real-time PCR ( qRT-PCR ) was done using SYBR Green Real-Time PCR Master Mix ( Thermo Fisher Scientific ) , with QuantStudio 7 Flex Real-Time PCR System ( Thermo Fisher Scientific ) . RIPK3 qRT primer sequences ( hRIPK3_F , CAAGGAGGGACAGAAATGGA; hRIPK3_R , GCCTTCTTGCGAACCTACTG ) were as described elsewhere [21] . Experiments were performed as previously described [68] . Tumor ascites from patients with advanced ovarian cancer ( IRB approved protocols at Dana-Farber Cancer Institute ) were implanted orthotopically ( intraperitoneal injection ) in NOD-SCID mice ( 8 weeks old , Jackson labs ) . Mice were followed weekly for abdominal distension and were humanely killed 3–8 months after injection of the original patient tumor ascites ( passage 0 ) to harvest tumor ascites for serial passaging . Ascites harvested from the xenografts were processed for red blood cell lysis and serially passaged ( up to 3 passages ) in new NOD-SCID mice . Tumors were frozen in liquid nitrogen for storage . Tumors were lysed in NP-40 lysis buffer ( 25 mM HEPES [pH 7 . 5] , 0 . 2% NP-40 , 120 mM NaCl , 0 . 27 M sucrose , 5 mM EDTA , 5 mM EGTA , 50 mM NaF , 10 mM b-glycerophosphate , 5 mM sodium pyrophosphate , 1 mM Na3VO4 ( fresh ) , 1 mM benzamidine [fresh] , 0 . 1% BME [fresh] , 1 mM PMSF [fresh] , 2× Complete protease inhibitor cocktail [Roche] ) using VWR 200 Homogenizer , on ice . Lysates were cleared by centrifugation at 16 , 000g , 15 minutes , 4° . Protein concentrations were determined using Bradford reagent ( BioRad ) . Protein samples were mixed with 5× SDS-PAGE sample buffer and frozen at −80° for storage . For all experiments , unless otherwise indicated , n was at least 3 . Statistical analyses were performed using GraphPad Prism 7 or Microsoft Excel . Violin and bean plots were made using BoxPlotR ( http://shiny . chemgrid . org/boxplotr/ ) [69] . Data were analyzed using one-way analysis of variance ( ANOVA ) test with Bonferroni posttest for non-paired datasets . Student t test was used for paired datasets . Data points are shown as means ± SEM . ClustVis was used for heatmap generation [70] . The heatmap in Fig 2D was generated as follows . The data IC50 values from the screen and gene expression values from GCSD database were analyzed by z-test and the heatmap was generated from these z-scores . ClustVis Data Pre-Processing settings were as follows: no row centering , unit variance scaling . Column settings were as follows: clustering distance—Manhattan; clustering method—single; tree ordering—original . Row settings were as follows: no clustering . The following databases were used for bioinformatics analysis of published datasets: cBio Cancer Genomics Portal ( http://www . cbioportal . org/ ) [51] , Broad-Novartis Cancer Cell Line Encyclopedia [55] ( http://www . broadinstitute . org/ccle/home , CCLE_Expression_Entrez_2012-10-18 . res microarray dataset ) , Genomics of Drug Sensitivity in Cancer [54] ( http://www . cancerrxgene . org ) and Gene Expression Omnibus ( http://www . ncbi . nlm . nih . gov/geo/ ) . The following datasets were included in this manuscript: GDS5336 [46] , GDS4367 [47] , GDS3894 [48] , GDS2546 [49] , microarray datasets from Ma and colleagues [50] , and GSE48433 [52] ( see S7 Table ) . The oncogene-related gene database was obtained by searching Uniprot database for key word “oncogene” ( QUERY: keyword:oncogene AND organism:"Homo sapiens ( Human ) [9606]" ) . Intersections of gene lists were made with CrossCheck [71] and Venny ( http://bioinfogp . cnb . csic . es/tools/venny/ ) . Mutational enrichment was done by dividing the number of mutations identified in NR cells by those identified in cells that were sensitive to the necroptosis treatment ( NS ) and then performing a z-test on this dataset . NR cells were defined as those that had no reduction in cell viability at 1 μM SM-164 concentration ( the highest concentration used in the screen ) . The rest of the cell lines that exhibited reduction in cell viability were defined as NS .
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Necroptosis is a regulated process that triggers cell death , resulting in necrosis and inflammation . Cancer cells have been shown to lose their ability to die via necroptosis , but the genetic factors that drive this resistance remain unknown . Here , we have analyzed 941 different cancer cell types and found that 83% of them are fully resistant to necroptosis . In order to identify the mechanisms underlying necroptosis resistance in these cells , we performed bioinformatics analyses to identify genes whose overexpression or mutation correlate with this effect . We show that two major genes , which are frequently deregulated in cancer ( also known as oncogenes ) , are key drivers of the resistance to necroptosis , and that targeting these oncogenes with specific drugs reversed this resistance . We conclude that resistance to necroptosis is a common event in cancer that can be overcome by targeting the genes that drive this resistance , which subsequently allows stimulation of cancer cell death via necroptosis .
|
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"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
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"cancer",
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2018
|
BRAF and AXL oncogenes drive RIPK3 expression loss in cancer
|
Two T helper ( Th ) cell subsets , namely Th1 and Th2 cells , play an important role in inflammatory diseases . The two subsets are thought to counter-regulate each other , and alterations in their balance result in different diseases . This paradigm has been challenged by recent clinical and experimental data . Because of the large number of genes involved in regulating Th1 and Th2 cells , assessment of this paradigm by modeling or experiments is difficult . Novel algorithms based on formal methods now permit the analysis of large gene regulatory networks . By combining these algorithms with in silico knockouts and gene expression microarray data from human T cells , we examined if the results were compatible with a counter-regulatory role of Th1 and Th2 cells . We constructed a directed network model of genes regulating Th1 and Th2 cells through text mining and manual curation . We identified four attractors in the network , three of which included genes that corresponded to Th0 , Th1 and Th2 cells . The fourth attractor contained a mixture of Th1 and Th2 genes . We found that neither in silico knockouts of the Th1 and Th2 attractor genes nor gene expression microarray data from patients with immunological disorders and healthy subjects supported a counter-regulatory role of Th1 and Th2 cells . By combining network modeling with transcriptomic data analysis and in silico knockouts , we have devised a practical way to help unravel complex regulatory network topology and to increase our understanding of how network actions may differ in health and disease .
The immune system is composed of diverse cell populations , for example antigen-presenting cells , T and B lymphocytes as well as effector cells like eosinophils , mast cells and neutrophils . One type of T lymphocytes , called T helper ( Th ) , has an important role in regulating this cellular network . Th cells can be further divided into Th1 and Th2 cells . Th1 and Th2 cells are thought to be mutually inhibitory and also to be involved in different diseases; Th1 cells are associated with autoimmune diseases , while Th2 cells are involved in allergies [1] . Although considered a simplification , the Th1/Th2 dichotomy is supported by a large body of experimental evidence [2] . However , studies of human diseases are more ambiguous in terms of the counter-regulatory roles of Th1 and Th2 cells . We and others have found that allergy , which is mainly thought to be a Th2 disease , can also be associated with Th1 responses [3] , [4] . One explanation could be that the Th1/Th2 paradigm is , to a large extent , based on studies of gene interactions in mice which may differ from those in humans , [5] . Another important aspect is that Th1 and Th2 cells interact in complex cellular networks that include several other T-cell subsets and cell types [5] . Ultimately , the balance between Th1 and Th2 cells is complicated to study experimentally , because it is the net result of altered interactions between multiple genes . Gene expression microarray studies evidence that hundreds of genes are involved in the Th1/Th2 cell differentiation [6] . We and others have found that complex gene expression changes in diseases can be addressed by arranging the genes in networks [7]–[9] . These networks give an overview of the genes that are involved , as well as their interactions , but not the dynamics of network changes that result in phenotypic alterations like , for example , Th1 and Th2 cell differentiation . Recent studies of the dynamics of Th1 and Th2 cell differentiation using in silico modeling have to some extent supported a counter-regulatory role of Th1 and Th2 cells [10] , [11] . The gene networks used have been based on a relatively small , though relevant , number of genes and interactions . In the present work we applied an algorithm previously developed to analyze large gene regulatory networks to perform in silico studies based on a more comprehensive gene network model , which included a larger number of genes [12] , [13] . The network was constructed by combining text mining from Medline ( www . pubmed . com ) based on seed genes and protein interaction data , with manual annotation . The aim of our study was to examine if the so-constructed network model was compatible with a counter-regulatory role of Th1 and Th2 cells from healthy humans as well as patients with different inflammatory diseases . To achieve this we studied the effects of in silico knockouts on the model dynamics [14] , together with analyses of gene expression microarray studies of T-cells from healthy controls and patients with different inflammatory diseases .
We defined a gene regulatory network ( GRN ) model of the genes involved in Th1 and Th2 cell differentiation based on manual annotation and automated data mining of Medline abstracts . To ease inspection , this gene regulatory network was organized into four layers according to the sub-cellular localization of the genes ( see Figure 1 ) . Another reason for this exercise was to enable the network for usage in agent-based models , as in [15] . The extracellular layer included cytokines ( IL-7 , TNFSF4 , IFN- , IL-12 and IL-18 ) , the antigens , as well as two membrane-receptors expressed on antigen-presenting cells , namely CD80 and CD86 . The membrane layer consisted of the T-cell receptor and cytokine receptors . The intracellular layer included signaling molecules as well as transcription factors . Finally , an extra-cellular layer consisted of autocrine cytokines ( IL-4 and IFN- ) and paracrine cytokines ( IL-5 and IL-13 ) . Gene regulatory networks ( GRNs ) can be represented as graphs where nodes represent genes that are either active or inactive . The state of the network is given by the combination of the activation state of all genes . Starting from a certain state , the upcoming configuration is computed by applying synchronously an updating rule . In general , since the number of possible states is finite ( i . e . , if is the number of nodes , and is the number of possible values of a node ) , and the dynamics is deterministic , then from a given initial state , the network can only evolve towards a limit cycle ( i . e . , attractor ) of length one or more ( up to ) . In what follows , we go after Kauffman [15] by identifying the attractors of the network dynamics as differentiation phases of the cell , and the transformations between attractors as pathways of cell differentiation . Using the algorithm in [12] ( briefly discussed in the Materials and Methods section ) , we found that the GRN dynamics was characterized by four attractors , three of which corresponded to known Th subsets , namely Th0 , Th1 and Th2 . The remaining attractor , which we named ThX , contained both Th1 and Th2 genes ( see Table 1 ) . The Th1 and Th2 attractors contained either Th1 or Th2 genes , an observation that was compatible with a counter-regulatory role of Th1 and Th2 cells . For example , the Th1 attractor contained the transcription factor TBET , which has been experimentally shown to induce the Th1 cytokine IFN- and inhibit the Th2 transcription factor GATA3 , which , in turn , induces the Th2 cytokine IL-4 . Conversely , GATA3 inhibits TBET and IFN- . Thus , the two transcription factors TBET and GATA3 play a key role in the counter-regulatory interaction between Th1 and Th2 [5] . However , the mixture of Th1 and Th2 genes in the ThX attractor did not agree with a counter-regulatory role between Th1 and Th2 cells . In particular , the state contained both IFN- and IL-4 , while the state contained both TBET and GATA3 ( Table 1 ) . This suggested that the dynamics of the network had an important role in regulating the balance between Th1 and Th2 cells . This may correspond , in vivo , to the situation in which antigenic stimulation may be temporary or persisting , and result in different inflammatory responses [16] . We performed single gene in silico knockout experiments for all genes in the network , in order to monitor the behaviour of the attractors . In so doing , we distinguished two different settings , corresponding to a different activation modality of the input nodes ( i . e . , those contained in the yellow box of Figure 1 ) : temporary-stimulation and persisting-stimulation . In temporary stimulation we examined the effects of an impulse-like stimulation of the input genes , which means that those genes were considered active for a short and transient period of time , and were set off thereafter . In persisting stimulation instead , inputs were set on or off throughout the observation period . Persisting stimulation is equivalent to introducing self-loops on the input nodes of the GRN . We computed the number of attractors for each single-gene knockout and for both activation modalities . We found that the median number of attractors per knocked out gene was 4 ( range 3–9 ) for temporary stimulation whereas it was 604 ( range 322–1664 ) for persisting stimulation , ( Table 2 ) . Therefore , as a first observation we noted that , similarly to in vivo stimulation , the network dynamics differed greatly between temporary and persisting stimulation . Next , we proceeded to examine the counter regulatory dynamics of the Th1 and Th2 cells . This was done by testing the effects of in silico knockouts of intra-cellular genes in the Th0 , Th1 , Th2 and ThX attractors . We started by knocking out TBET and GATA3 . If TBET and GATA3 were counter-regulatory , knocking out TBET would be expected to result in attractors mainly containing IL-4 , but not IFN- , while the opposite would be expected after knocking out GATA3 . Firstly , we applied the temporary stimulation activation modality ( Figure 2 ) . Knocking out TBET resulted in attractors that contained both IL-4 and IFN- , either IFN- or IL-4 , as well as attractors without IL-4 and IFN- . Knocking out IL-4 resulted in attractors that contained either IFN- or IL-4 , as well as attractors without IL-4 and IFN- . On the other hand , knocking out the same genes but applying the persisting stimulation activation modality mainly resulted in attractors containing both IL-4 and IFN- ( Figure 3 ) . For both temporary and persisting stimulation , the knockout of other transcription factors that regulated Th1 and Th2 cells , namely IRF4 , MAF , NFAT , STAT1 and STAT6 , also resulted in attractors that contained IL-4 and IFN- , either alone or in combination . Thus , the balance between Th1 and Th2 cells was regulated by several transcription factors , and not only by TBET and GATA3 . To summarize , these findings were not compatible with a strictly counter-regulatory role of neither TBET nor GATA3 or any of the other transcription factors . We proceeded to examine how the in silico findings related to in vitro studies of T-cells from healthy controls and patients with different T-cell related diseases . We downloaded several sets of gene expression microarray data from the public domain to test whether Th1 and Th2 genes were inversely correlated in T-cell related diseases . If Th1 and Th2 cells are antagonists we would expect inverse relations between genes in the Th1 and Th2 attractors . If so , the expression levels of those genes would be negatively correlated . Instead of this , we found a highly significant positive correlation between the ratios of differentially expressed Th1-associated genes and Th2-associated genes ( Pearson correlation coefficient , ) . Thereafter , we analyzed the correlations between all gene pairs in the model that , based on the literature , were considered to inhibit each other . This analysis showed that all gene pairs were positively correlated but one ( see Table 3 ) . This included the signature Th1 and Th2 genes TBET and GATA3 , which showed the most significant positive correlation ( , ) as well as IFN- and IL-4 ( , ) .
Because of the large number of proteins involved in Th cell differentiation , alterations in the balance between those proteins are not easily studied experimentally . Computational modeling provides an attractive alternative to study the dynamics of Th1 and Th2 cell regulation and has previously been employed for this purpose by us and others [10] , [11] , [17] . Such models have supported a counter-regulatory role of Th1 and Th2 cells , but were based on a relatively limited number of genes and did not include comparisons with biological data . In this report , we aimed to examine if Th1 and Th2 cells were counter-regulatory by combining modeling , in silico knockouts and gene expression microarray analyses of human T cells in health and disease . We constructed a network model of the proteins involved in Th cell differentiation by manual curation of proteins associated with Th1 and Th2 cells , and that had been identified as relevant through automated text mining of the medical literature . This resulted in a significantly more comprehensive model compared to previous versions . Analysis of the dynamics of that model showed that it contained four attractors , two of which corresponded to the Th1 and Th2 subsets . These contained the Th1 and Th2 specific transcription factors TBET and GATA3 , respectively . This was compatible with a counter-regulatory role of these attractors . However , the fourth attractor , which we named ThX , contained a mixture of Th1 and Th2 proteins , including TBET and GATA3 . This did not agree with a counter-regulatory role of these transcription factors . Furthermore , we extended our analysis by in silico knockout experiments of TBET and GATA3 . We reasoned that if the two were counter-regulatory , then knocking out TBET would result in attractors mainly containing IL-4 , while knocking out GATA3 would result in attractors mainly containing IFN- . Whereas this was true for GATA3 , it was not the case for TBET . In fact , knockout of either TBET or the other Th1 and Th2 attractor proteins mainly resulted in attractors containing both IFN- and IL-4 . Afterthat , we examined the expression of Th1 and Th2 attractor genes in microarray studies of eleven T cell diseases , namely autoimmune , infectious and oncological diseases . In most of these , the expression of Th1 and Th2 attractor genes increased concurrently , rather than in an opposing pattern . Moreover , we found that genes in the network model that were thought to inhibit each other based on experimental studies , were in fact positively correlated . This was particularly true for TBET and GATA3 which are thought to have a key role for the counter-regulation of Th1 and Th2 cells . It is of note that the interactions in the model were chosen based on experimentally validated functions and interactions in Th cells . In many cases those experiments were performed using polarizing cytokines and T cell receptor stimulants . This is likely to result in more homogenous Th cell responses than those seen in vivo . In the latter case Th cells are activated by antigen-presenting cells which process the antigens to peptides , subtle variants of which may have different effects on Th cells . In addition , different doses and timing of antigen exposure play an important role in the Th cell activation and differentiation process . The effects of timing was reflected by the results in our study; temporary and persistent stimulation had profound effects on the network dynamics of these processes . Moreover , the activation involves a complex and variable mixture of proteins . Taken together , it is possible that this complexity may result in a mixture of Th1 and Th2 cells responses , rather than one of the two . The ThX attractor may correspond to such a mixed or transitional response . This is consistent with the increasing recognition that Th cell phenotypes are plastic rather than discrete [2] . This recognition resulted from experimental and clinical studies that show overlap between genes considered to be Th1 and Th2 genes [18] , [19] . Our analyses of gene expression microarray data from human T cells in health and disease lend further support to Th plasticity . From an in vivo perspective , this plasticity allows fine-tuned responses to a constant exposure of different antigens at different time points and doses . It is also of note that in vivo Th1 and Th2 differentiation may be affected by many other T cell subsets , of which an increasing number have been recognized . Moreover , epithelial cells , mast cells and eosinophils release cytokines that affect the differentiation process . Ideally , simultaneous analysis of networks representing those cells and subsets would yield an understanding not only of Th1 and Th2 cells , but comprehensive models of the cellular networks that underlie immunological diseases . Improved methodologies , such as single cell RNA sequencing may make such models feasible in the near future . A limitation is that our model is that the underlying biological data is mainly qualitative . Thus , the model is based on synchronous updating and does not take into account quantitative or time-dependent changes . Others have shown that asynchronous updating may have different effects on attractors [20]–[22] . An interesting future research direction is to perform time series experiments of Th1 and Th2 cells using gene expression microarrays . Using such data it may be possible to improve our model both with regards to quantitative and time-dependent changes and also make predictions which can be validated with other biological methods , such as measuring Th1 and Th2 cytokines on the protein level . In summary , our findings , both based on in silico modeling and analysis of T cells from human diseases agree with Th1 and Th2 cells having complex and possibly synergistic , rather than counter-regulatory roles .
We employed a step-wise procedure to define the set of relevant genes for the differentiation of Th cells into the Th1 and Th2 phenotypes . Firstly , we identified two different sets of genes as a primary source: i ) 17 genes from a previous network model [10]; ii ) a set of 17 genes determined in a gene expression microarray study of polarized Th1 and Th2 cells by [6] . All these 34 genes were used as seed genes . Then we retrieved the first order neighbors of these seed genes and their connections in the BioGrid database ( www . biogrid . org ) . Successively , the connection among the proteins of the first order neighbors were retrieved . Among all the genes retrieved thus far , we selected only those associated to the Gene Ontology term ( www . geneontology . org ) “T cell differentiation” . More specifically , the genes co-cited in the millions Medline abstracts together with this term were retrieved . This resulted in a set of 403 genes , that was further slimmed down and used to construct a manually annotated directed graph of gene interactions relevant for Th1 and Th2 cell differentiation . This was made by using the T-cell receptor pathway in the KEGG database as a template ( www . genome . jp/kegg/pathway . html ) . Genes that were part of that pathway and had well-characterized and experimentally verified functions relevant for Th1 and Th2 cell differentiation were selected for the final network model . A detailed description of each interaction in the network , together with 126 supporting references is given in Text S1 . It is also of note , that the network model was independent of the gene expression microarray experiments , which are described below ( none of the published abstracts pertaining to those experiments contained co-cited genes that were included in the model ) . Given a GRN , the number of attractors of the network dynamics is , in general , not effectively computable since the number of states of the network grows exponentially with . It is not even possible to effectively calculate the initial states of the network that will eventually fall in the basin of attraction of a specified limit cycle . When the number of genes is large , the explicit computation of the dynamics becomes impractical as the number of states the network can assume increases exponentially with the number of nodes . In the worst case the algorithm needs to store the complete description of the state transition graph and therefore the exhaustive study is feasible only when the number of nodes is small [10] , [23] . Just to give an idea , for a network with nodes , one needs about 6 Terabytes to store the state-transition graph of the network . In our case , with , it would require about 7 Petabytes of storage . In recent studies , formal methods such as bounded model-checking technique or reduced order binary decision diagrams have been employed in the study of attractors of Boolean and multivalued networks , see Dubrova et al . , Garg et al . , and Chaves et al . [12] , [21] , [24]–[26] . These formal methods have a potential to handle large networks . In particular we used Dubrova's algorithm based on a solver for the satisfiability problem ( SAT ) which from the logical structure of the network infers the possible attractors . In simple words , the network can be seen a Boolean circuit and its attractors can be computed by using methods and largely optimised algorithms coming from modeling of Very Large Scale Integration ( VLSI ) circuits . What is special about formal methods approach is that it enables to find attractors of large networks . The idea behind the search algorithm is that , by using symbolic computation , it is possible to unfold the dependencies between nodes that are linked together and to compose the update function as a relation among the states ( activation/inhibition ) of the genes/nodes . Then the algorithm uses the SAT solver to determine the values of the states that evaluate to true the relation . This process is then repeated until all attractors are identified . We specified the network as the set of rules , each one representing the activatory or inhibitory relation between genes . For example , if rule stems for the activation of gene , and is determined by the activators and inhibitors ( activators and inhibitors are generically called regulators ) , then it can be written as , where conventionally the subset of inhibitors are tagged with a minus sign . Analogously to [10] , [12] , the time is discrete and the activation states of the genes are changed simultaneously ( i . e . , synchronous update ) . At each time step , the value of the gene is denoted by the same gene name . The successive value of gene is ( 1 ) where and denote the logical operators and , or and not respectively . The rule in Equation 1 states that for a gene to be activated , at least one activator and no inhibitors must be active [10 12 , 27] . In our specific case we had a set of rules involving genes , that was the result of data mining and manual annotation . These are listed in Table 4 . The network so specified was compiled in other formats , in particular GML ( Graph Modeling Language ) , which is used in several applications specialized in graphical layout , and CNET which is the input form accepted by the algorithm to compute the attractors . Whereas the GML output was based simply on activation/inhibition network links , in the CNET format we had to specify the updating function for each node . The last part of this work was the systematic characterization of the networks obtained by knocking out genes one at a time . As a consequence of these in silico knockout experiments we anticipated two results: a ) to identify the set of genes which are pivotal to the Th1/Th2 differentiation; b ) to spot subsets of co-expressed genes belonging to the attractors , since from analysis of microarray data we expected these genes to be correlated . Changes in the set of the attractors were used to highlight relevant nodes . As a first approximation , differences in the mere number of attractors were considered; if a node did not affect the number of attractors , then from the point of view of the dynamics it was considered irrelevant . To examine whether Th1 and Th2 gene activation patterns denoted two opposed pathways , gene expression data were downloaded from Gene Expression Omnibus ( http://www . ncbi . nlm . nih . gov/geo/ ) . Datasets were selected based on the criteria that they i ) measured mRNA expression from CD4+ cells from healthy controls or patients with T-cell related diseases ( e . g . , SLE ) and ii ) that there were at least 5 samples per disease or per controls , ( Table 5 ) . Differentially expressed genes between patients and controls in each disease were determined using the unpaired Student's t-test . Genes with a significance were considered differentially expressed . In order to examine if the differentially expressed genes in the Th1 and Th2 attractors were negatively or positively correlated we performed the following analyses: for each disease , the ratio between differentially expressed genes in the Th1 attractor and all genes in the Th1 attractor was computed . This analysis was repeated for the Th2 attractor genes . It resulted in a list of ratios for each attractor and for each disease . The Pearson correlation coefficient between those ratios was then computed . To test if gene pairs in the network model that had counter-regulatory relationships were also negatively correlated , microarray data belonging to healthy controls in each dataset was pooled and Pearson correlation coefficients were calculated for all the gene pairs with counter-regulatory relationships .
|
Different T helper ( Th ) cell subsets have an important role in regulating the immune response in inflammatory diseases . Th1 and Th2 cells are thought to counter-regulate each other , and alterations in their balance result in different diseases . This paradigm has been challenged by recent clinical and experimental data . Because of the large number of genes involved in regulating Th1 and Th2 cells , assessment of this paradigm by experiments or modelling is difficult . In this study , we combined novel algorithms for network analysis , in silico knockouts , and gene expression microarrays to examine if Th1 and Th2 cells had counter-regulatory roles . We constructed a directed network model of genes that regulated Th1 and Th2 cells through text mining and manual curation . We identified four cycles in the gene expression dynamics , three of which expressed genes that corresponded to Th0 ( Th1/Th2 precursor ) , Th1 and Th2 cells . The fourth cycle contained the expression of a mixture of Th1 and Th2 genes . We found that neither in silico knockouts of the Th1 and Th2 attractor genes nor gene expression microarray data from patients and healthy subjects supported a counter-regulatory role of Th1 and Th2 cells .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"immunology/genetics",
"of",
"the",
"immune",
"system",
"mathematics",
"computational",
"biology/systems",
"biology"
] |
2010
|
Combining Network Modeling and Gene Expression Microarray Analysis to Explore the Dynamics of Th1 and Th2 Cell Regulation
|
The bacterial potassium channel KcsA , which has been crystallized in several conformations , offers an ideal model to investigate activation gating of ion channels . In this study , essential dynamics simulations are applied to obtain insights into the transition pathways and the energy profile of KcsA pore gating . In agreement with previous hypotheses , our simulations reveal a two phasic activation gating process . In the first phase , local structural rearrangements in TM2 are observed leading to an intermediate channel conformation , followed by large structural rearrangements leading to full opening of KcsA . Conformational changes of a highly conserved phenylalanine , F114 , at the bundle crossing region are crucial for the transition from a closed to an intermediate state . 3 . 9 µs umbrella sampling calculations reveal that there are two well-defined energy barriers dividing closed , intermediate , and open channel states . In agreement with mutational studies , the closed state was found to be energetically more favorable compared to the open state . Further , the simulations provide new insights into the dynamical coupling effects of F103 between the activation gate and the selectivity filter . Investigations on individual subunits support cooperativity of subunits during activation gating .
K+ channels play a crucial role in a wide variety of physiological and pathophysiological processes including action potential modeling [1] , cancer cell proliferation [2] , and metabolic pathways mediation [3] . In the last few decades , the understanding of ion channels has increased tremendously . The Hodgkin-Huxley equations [4] provided first insights into the ion flow in nerve cells and Hille showed a comprehensive picture of the electrophysiological properties of ion channels [5] . In 1998 , the first crystal structure of an ion channel , the bacterial potassium channel of Streptomyces lividans ( KcsA ) , shed light on the molecular details of a K+ channel [6] . The pore-forming domain of KcsA is composed of four identical subunits ( SUs ) which are arranged symmetrically around a channel pore . Each SU consists of two transmembrane helices , TM1 and TM2 , which are connected by the P-helix and the selectivity filter ( SF ) ( Figure 1B ) . While the extracellular facing SF tunes the selection of different ions and modulates inactivation , the main conformational changes regulating ion flow , are found at the TM2 helices . These motions , referred to as activation gating , are thought to involve an iris-like motion of the TM2 helices that constrict the permeation pathway at the helix bundle crossing region [7]–[10] . This region is believed to form the main activation gate . Starting in 1998 , several different pore domain structures of KcsA in its closed state [6] , [11] and more recently in intermediate and open states have been solved [12] . These crystal structures provide excellent insights into different conformations of proteins; however , they feature only snapshots of dynamical proteins [13] . Therefore , the transition steps and the mechanisms of activation gating are still unknown . A number of computational studies have been published over the last years , aiming at exploring the gating pathways of ion channels by making use of available X-ray structures as templates [14]–[22] . However , the lack of particular K+ channels in different conformations was a limitation of previous publications . Thus , these studies had to compare crystal structures of different channels or had to rely on homology models of open structures of KcsA . With the successful crystallization of intermediate and open structures of KcsA by Cuello et al in 2010 [12] , in silico activation gating of K+ channels cannot only be readdressed , but also allowed us to calculate a complete energy profile of activation gating . The essential dynamics ( ED ) simulation method has been shown as a useful tool to investigate sampling of proteins in conformational space and to derive transition pathways between conformational states [23]–[27] . In this study , we applied ED simulations combined with umbrella sampling calculations to investigate activation gating of KcsA .
A prerequisite of the ED method is that the starting and target structures are of equal length and identical amino acid sequence . Thus , the KcsA crystal structures ( pdb identifier: 1k4c , closed; 3fb6 , intermediate; 3f7v , open ) were adjusted at the N- and C-termini so that all states started from residue 29 and ended at residue 118 , leading to channels with four times 89 amino acids . Additionally , Q117 in the open and intermediate crystal structure was mutated to arginine to obtain the wild type structure . Before probing the transition pathway between closed and open conformations of KcsA , the stability of the different channel states was assessed in molecular dynamics ( MD ) simulations . Repeated simulations ( 3 times 50 ns ) of the structures , embedded in a lipid-bilayer membrane , were performed . The root-mean-square deviation ( RMSD ) of the backbone atoms without loops of all three channel states is less than 2 Å ( Figure S1 ) . The stability of the closed state is similar to previous values reported in literature [28] , [29] . Moreover , the RMSD of the intermediate state is comparable to the two other states with a RMSD of 1 . 75 Å . To investigate the activation pathway , the backbone atoms of closed and open structures without loops were compared by principal component analysis ( PCA ) . The resulting eigenvector ( EV ) was used to enforce the transition between the two states . Thus , the ED simulation is a free MD simulation , with all coordinates equilibrating except for one coordinate that is biased to drive the gating transition . Ten opening and ten closing ED simulations , all of them lasting for 20 ns , were carried out . In the following paragraphs , results of opening simulations are explained in detail . Since similar observations were also found in the reversed direction , results for the closing runs are summarized at the end of this section and corresponding figures are shown in the supplemental material . The conformational changes during the ED opening simulations were analyzed by monitoring the RMSD as a function of time ( Figure 1A ) . The deviation from the target structure ( open conformation , pdb identifier: 3f7v ) was measured over time . The difference between the starting and target structure is 4 Å . In all ten opening ED simulations , the RMSD values steadily decreased and reached final values between 1 . 35 and 2 . 20 Å , indicating that all simulations reached the open state . Successful opening is defined by a decrease of the RMSD to approximately 2 Å compared to the target structure . For simplicity , the average RMSD and standard deviation of the ten simulations were calculated . On average , a final RMSD of 2 Å as shown in Figure 1A was reached . The standard deviation indicates that in the first 11 ns , the RMSD values of the simulations did not vary . However , in the subsequent simulation time at which the simulations reached the target structure , the RMSD of the ten simulations showed wider distribution . To investigate the conformational states of the end structures , the deviation of the Cα atoms from the target structure was analyzed . An average structure of the ten ED simulations was generated which exhibits minimal RMSD ( Figure 1B ) . This average structure revealed that ED simulations were able to reach the target structure . Figure 1B shows the color coded deviation of each Cα atom from the open structure . As expected , the TM1 and P-helices displayed a very modest RMSD deviation of 0 . 05 Å to the target structure since there are no conformational changes in these regions during activation gating . In contrast , deviations up to 3 Å were found in the C-termini of the TM2 helices , which undergo large conformational changes during channel opening . Additionally , large deviations were found in the loop regions due to the high mobility of loops . Investigations on the loop region ( amino acid G56 ) showed that mutations did not influence gating [30] , [31] . Thus , the loops were not investigated further . The program HOLE [32] was used to calculate the activation gate radius profiles ( Figure 2 ) of the backbone atoms of different channel states . In the closed conformation , the constriction of the activation gate features a diameter of 5 . 9 Å . In the intermediate state , the diameter of the constriction site is 8 . 3 Å . In the open conformation , the activation gate diameter expands to 11 . 8 Å . The diameter of the activation gate in the ED simulations reached 10 . 7 Å on average . The shape of the pore radius profile of the end structures obtained from ED simulations matched the essential features of the profile of the open crystal conformation , further indicating that the simulation derived structures adopted the open state . The major motions of opening were also observed in the reversed direction during closing ( see Figure S2 ) . However , only seven out of ten ED simulations successfully closed ( RMSD<2 . 3 Å ) . Careful inspection revealed that the underlying reason for unsuccessful closure of three runs was partial unwinding of single TM2 helices . This observation may suggest that optimal packing of helices at the bundle crossing region is important for channel closure . As described in the method section , no forces were applied to the side chains in the simulations . Hence , the simulations allowed investigations of the rotameric side chain changes coupled to gating . A phenylalanine , F103 , present in the TM2 helices of KcsA , was shown to change its rotameric state upon activation gating [12] , [21] and affecting the SF conformation [33] , [34] . Therefore , the χ1 angle dynamics in the ten ED simulations were analyzed ( Figure 3A ) . F103 can adopt two different rotameric states which are called “up” ( χ1 angle of −55 to −72° ) and “down” state ( χ1 angle of −166 to −185° ) . In the first 5 ns of the opening ED simulations , F103 was stable in the up state . Subsequently , the conformational changes of the channel allowed F103 to adopt the down state . The F103 amino acids switched from the up to the down state over the next 15 ns . In most of the cases , this change was irreversible . Once F103 was in the down state , it was not able to switch to the up state again . After 20 ns , 78% of all F103 were in the down state . To validate if the F103 rotameric changes occurred because of activation gating , dihedral angles of unbiased open and closed state MD simulations were analyzed ( data not shown ) . In the open state , all F103 of the three 50 ns MD simulations were in the down state . In the closed conformation , F103 showed more flexibility . Initially in the up state , the F103 was able to change to the down state; however , the up state is observed more frequently . This finding is in agreement with adiabatic energy maps of Pan et al [34] and a study by Cuello et al [33] . The dynamic behavior of F103 in the closing ED simulations is shown in Figure S2 . In the first 2 ns , F103 was stable in the down state . Subsequently , F103 can adopt both up and down states as expected from the energy maps of Pan et al [34] . Despite different SF conformations in the closed and open crystal structures ( actived vs . inactivated ) , the SF in all ten opening simulations did not adopt the inactivated conformation as seen in the crystal structure ( pdb identifier: 3f7v; Figure 3B–E ) . The stability of the SF of the ED derived open conformation is further supported by a 100 ns free MD simulation , where no changes in the filter were observed . Previous studies reported that side chain hydrogen bonds between D80 and a protonated E71 promote inactivation of the SF [35]–[37] . Hence , we performed ED simulations with protonated E71 amino acids and analyzed the SF conformation . These simulations revealed similar conformations , irrespective of the protonation state . This conformation might be influenced by the ion occupancy in the filter . The ions were located at the most favored positions S0 , S2 , and S4 since the simulations started from a conductive state [38] . Umbrella sampling was employed to investigate the free energy landscape of activation gating ( Figure 4A ) . The ED simulation with the lowest RMSD was used for a subsequent PCA calculation and thereof the first EV was employed as reaction coordinate . MD simulations of closed , intermediate , and open states were projected onto this reaction coordinate to determine sampling regions of the crystal structures . Three main energy wells , separated by two energy barriers , were identified . The first energy well , which is sampled by the closed state , is located at −0 . 7 to 3 . 1 nm . The intermediate state is sampled at the adjacent energy well , separated by a small energy barrier at 4 nm ( barrier 1 ) from the closed state . Broad sampling of the intermediate conformation was observed , ranging from 3 . 4 to 7 . 4 nm . The subsequent large energy barrier at 9 nm ( barrier 2 ) separates the open conformation from the intermediate state . The open conformation samples a relatively small energy well ranging from 8 . 8 to 11 . 4 nm . Next , we investigated the underlying structural rearrangements shaping the energy wells and barriers . By analyzing the dihedral angles of all side chains , a single residue in the helix bundle crossing region was identified ( F114 ) whose conformational changes correspond to the first energy barrier ( Figure 4B ) . This unique rotameric pattern of F114 was observed in all ten opening ED simulation runs suggesting that this pattern was essential for activation gating ( Figure 5A ) . In the early stage of activation gating ( after 5 ns ) , 80% of all F114 changed from an up state ( χ1 angle of −55 to −72° ) to a down state ( χ1 angle of −166 to −185° ) . After the change to the down state , a rigid phase from 5 to 10 ns was observed . Subsequently , F114 regained its flexibility . This suggests that the first flip of F114 and the changes in interacting amino acids may cause energy barrier 1 . Consequently , interacting amino acids were analyzed in more detail . Figure 5B–E depicts residues that interact with F114 over time . Residues L110 , W113 , and R117 of TM2 and L105 of the adjacent TM2 helix interacting in all states are shown in green . Additional interacting amino acids in the closed state were A108 , A109 , and T112 of the adjacent TM2 ( Figure 5B ) . In the rigid transition state ( Figure 5C ) , additional interactions to V115 were observed . In the open state , interactions with T101 and S102 of the neighboring TM2 were found . When F114 occupied the down state , it was in close contact with A32 of the adjacent TM1 helix . F114 interacted with L35 ( adjacent TM1 helix ) independently of the rotameric state , indicating a specific interaction pattern . The importance of the F114 and adjacent amino acids is supported by experimental mutation studies ( see section “relation to experimental data” ) . The dynamical behavior of the F114 side chain is further supported by free MD simulations of the open and closed state . In the open state , 75% of the 12 F114 side chains in the MD simulations adopted the down state . Flipping between the two states occurred as a rare event , indicating that the F114 side chains showed high stability over 50 ns . An increased flexibility of F114 was observed in the closed state . Although 80% of the F114 side chains adopted the initial up state , flipping between the two states was observed frequently . Nevertheless , the specific rotameric pattern of F114 as seen during the ED simulations did not occur , indicating that this rotameric pattern is unique for activation gating . Additionally , these analyses showed that not only F103 but also F114 is allowed to adopt two rotameric states in the closed conformation . Cα-Cα distances between two opposite T112 residues ( TM2 ) as a measure of activation gate opening ( as proposed by Cuello et al [12] ) were found to correlate with the energy barriers ( Figure 4C ) . This measurement allows direct comparison of ED derived conformational states ( closed , intermediate , and open ) to the crystal structures . At the first energy barrier , an initial conformational change of the activation gate from 12 Å to 14 Å was observed correlating to structural rearrangements of F114 . In the subsequent plateau phase of opening , a good correlation with the energy wells of the intermediate structures was observed . The second energy barrier is linked to a distance increase of 8 Å between the two opposing T112 residues . This suggests that the second energy barrier is mainly caused by global conformational changes of TM2 . To further test the significance of this two-phasic activation gate opening , the T112 distances of all ten opening ED simulations were analyzed . Again , a two-phasic gating with global conformational changes at 4 to 5 ns and at 7 . 5 to 16 ns was found ( Figure S3 ) . These findings are in line with previous computational studies , which showed that the main opening of the gate occurs after an initial unlock from the closed state by structural rearrangements of amino acids [18] , [21] . Additionally , simulations in the reverse direction showed similar local and global structural rearrangements in inverse order supporting the validity of the simulations . The transition pathways obtained by the ED simulations are in good agreement with experimental data . First , the simulations are able to sample the intermediate crystal structure ( pdb identifier: 3f7v; green shaded energy well in Figure 4A ) [12] , which was not included in our ED simulation protocol . Secondly , as expected [13] , KcsA crystal structures 1k4c , 3f7v , and 3fb6 occupy energy wells in our calculated energy profile ( Figure 4A ) . Thirdly , the energy profile indicates that the pore is intrinsically more stable in the closed conformation . This observation is supported by experimental studies on potassium channels [39]–[41] , although it should be noted that the latter two studies were carried out on shaker-like channels , rendering the comparison indirect . Further , residues involved in pH sensing of KcsA were not included in the simulated system , which may also affect stability . Simulations support the hypothesis that the F114 conformational changes are crucial to trigger initial activation gating . Mutational studies have shown the important role of the tightly packed helix bundle crossing region including F114 . Several mutations in this region revealed a destabilization of the closed conformation [39] , [42] . The fact that F114 is conserved in many K+ channels additionally underlines the importance of this aromatic amino acid for channel function [40] , [43]–[46] . Mutational analysis of interacting amino acids in the open state like L35 , T101 , and T102 ( analyzed in Shaker [40] , [47] ) or A32 would be of great interest and may lead to new insights into the packing of F114 in the open state . Since the C-terminus of the TM2 helices moves from a water environment towards the lipid/water interface during activation gating , interactions between the TM2 helices and lipids were investigated . Analyses revealed that the number of hydrogen bonds between the hydrogen bond forming residues W113 and R117 and the lipid head groups increased during gate opening ( Figure 6 ) . This indicates that the C-terminus of TM2 moved towards the inner leaflet of the bilayer membrane while hydrogen bonds are mainly formed between R117 and the phosphate groups of the lipids . A decrease of hydrogen bonds was found for the closing simulations ( Figure S4 ) while TM2 moves back from the lipid environment to the water environment . ED simulations were applied on one , two , and three SUs , respectively , while the other SUs were allowed to move freely . Simulations revealed that at least three SUs are necessary to open the activation gate . RMSD analyses of simulations with the ED method applied on one and two SUs showed that there was only a slight decrease in RMSD over time suggesting that the channel remained in the closed state . However , simulations with the ED method applied on three SUs revealed that the end structures deviated 2 . 5 Å from the target structure ( Figure 7 ) . Cooperativity analyses of ED simulations presented in this study support previous studies on cooperativity of potassium channels in general [48]–[51] and of the pore domain in particular [19] , [21] , [52] , [53] . Our simulations indicate that movement of one SU or two SUs is insufficient to open the gate . However , opening of three SUs is sufficient to obtain an open gate structure . Comprehensive investigations on cooperativity are subject of further studies . The results presented here show that the ED simulation approach successfully sampled transition pathways between closed and open states of an ion channel on the nanosecond time scale and allowed investigations on activation gating . There is good agreement between our investigations and previous experimental and computational studies , supporting the validity of this approach . The simulations provided new insights into conformational changes during gating and revealed that activation gating occurs as a two phase process . Additionally , investigation of the energy landscape allowed the correlation of conformational changes to energy barriers at the atomistic level . The first phase , in which local structural rearrangements in the helix bundle crossing region take place , correlates to a small energy barrier . The second phase was found to correlate with a large second energy barrier . During this phase , the main conformational changes of the TM2 helices , which occur upon gating , were observed . In addition , we showed the feasibility of the ED approach to study the cooperativity of activation gating . The simulations suggest that individual SUs cannot open the activation gate . Rather , several SUs have to move in a cooperative manner in order to open the gate . We expect that ED simulations will be useful for further investigations including the analysis of gating sensitive mutations . This is of special interest with regard to inherited channelopathies . Furthermore , we expect that these simulations will be valuable for studies on drug binding with different channel states .
The closed ( pdb identifier: 1k4c ) [54] and open ( pdb identifier: 3f7v ) [12] crystal structures were used as starting conformations for the ED simulations . Additionally , they were subject to free MD simulations to assess the stability and the side chain dynamics . Free MD simulations of the intermediate conformation ( pdb identifier: 3fb6 ) [12] were performed to investigate the sampling region of the structure along the transition pathway . Since the helices of the open conformation were not crystallized to the same extent as in the closed state ( seven amino acids are missing at the beginning of TM1 and six amino acids at the end of TM2 ) , the helix-lengths of the closed crystal structure were adapted by deleting these amino acids . The Q117 in the crystal structure of the open conformation was mutated to arginine in order to obtain the wild type structure using Swiss-PdbViewer [55] . For the intermediate state , one helical turn on the C-terminus was added in PdbViewer to obtain the same length of the helices as for the closed and open conformation . The protein was embedded in an equilibrated membrane consisting of 280 dioleolylphosphatidylcholine ( DOPC ) lipids using the g_membed tool [56] , which is part of the gromacs package . K+ ions were placed in the SF , as described previously [57] , at K+ sites S0 , S2 , and S4 , with waters placed at S1 and S3 of the SF [38] . Cl− ions were added randomly within the solvent to neutralize the system . All simulations were carried out using the gromacs simulation software v . 4 . 5 . 4 [58] . The amber99sb force field [59] and the TIP3P model [60] were employed for the protein and water , respectively . Lipid parameter for the DOPC membrane were taken from Siu et al [61] . During all simulations , the area per lipid was at 0 . 72 nm2 which is in good agreement with experimental values [62] . Electrostatic interactions were calculated at every step with the particle-mesh Ewald method [63] with a short-range electrostatic interaction cut off of 1 . 4 nm . Lennard-Jones interactions were calculated with a cut off of 1 . 4 nm . The LINCS algorithm [64] was used to constrain bonds , allowing for an integration step of 2 fs . The Nose-Hoover thermostat was used to keep simulation temperature constant by coupling ( tau = 0 . 5 ps for equilibration simulations and tau = 0 . 2 ps during unrestrained simulations ) the protein , lipids and solvent ( water and ions ) separately to a temperature bath of 310 K . Likewise , the pressure was kept constant at 1 bar by using the Parrinello-Rahman barostat algorithm with a coupling constant of 1 ps . Prior to simulation , 1000 conjugate gradient energy-minimization steps were performed , followed by 5 ns of equilibrium simulation in which the protein atoms were restrained by a force constant of 1000 kJ mol−1 nm−2 to their initial position . Lipids , ions , and water were allowed to move freely during equilibration . In order to assess the stability of the open , intermediate , and closed conformation of the KcsA channel , three 50 ns unrestrained MD simulations were carried out for each structure . The basic method of the PCA is described in detail elsewhere [65] . A trajectory consisting of the closed and the open conformation was built and used for PCA . Subsequently , the covariance matrix of the positional fluctuations of the TM1 , P-helix , and TM2 backbone atoms was built up and diagonalized ( loops were excluded from analysis ) . For the PCA , all four SUs ( one , two , and three SUs for cooperativity investigations ) of the homotetrameric channel were taken into account . Only one EV with a non-zero eigenvalue results from this PCA , which represents the difference vector between the open and the closed crystal conformation . This vector was used as reaction coordinate for ED simulations . The ED technique [23] , [24] can be used to simulate the conformational pathway between two crystal structures [26] . During simulation , the distance along the first EV was increased in fixed increments to drive the system from the closed to the open state and vice versa . It is important to emphasize that the EVs were obtained by PCA of the backbone atoms only and therefore did not contain any information on the side chains . For simulations , the equilibrated closed and open systems , respectively , consisting of the channel , lipid-membrane , ions , and water , were used as start positions . Helical restraints were applied to the last four C-terminal amino acids of the TM2 helix of each SU in order to prevent unwinding . All parameters were set as described above . Simulations were performed on the 20 ns timescale . Fixed increment linear expansion for each simulation step ( 2 fs ) was set to 1 . 28e−6 nm in order that the target structure was reached after two thirds of the simulation time . For cooperativity investigations , fixed increment linear expansion was set to 1 . 89e−7 nm , 6 . 27e−7 nm , 9 . 24e−7 nm per step ( 2 fs ) and was applied to one SU , two SUs , and three SUs , respectively . The windows for the umbrella sampling simulation were taken from the ED simulation with the lowest RMSD . The first EV , which was derived from a PCA of the ED simulation , was used as a reaction coordinate . As this EV is dominant ( its eigenvalue is more than an order of magnitude larger than the second largest ) , we assume that the transition pathway is sufficiently accurately covered by this mode . Along this reaction coordinate , 39 windows with the corresponding structures from the first ED simulation were chosen for umbrella sampling and simulated for 100 ns ( Figure S5 ) . 33 windows were simulated with a force constant of 1 kJ mol−1 nm−2 . For six windows , the force constant was set to 100 kJ mol−1 nm−2 in order to obtain sufficient sampling of the energy barriers . In total , umbrella sampling was performed for 3 . 9 µs . The first 50 ns of each window were discarded for equilibration . The potential of mean force and the statistical errors of the activation gating energy profile were estimated by making use of the g_wham tool of gromacs and the integrated bootstrap analysis method [66] . The number of bootstraps was set to 50 .
|
Voltage gated ion channels are membrane embedded proteins that initiate electrical signaling upon changes in membrane potential . These channels are involved in biological key processes such as generation and propagation of nerve impulses . Mutations may lead to serious diseases such as cardiac arrhythmia , diabetes or migraines , rendering them important drug targets . The activity of ion channels is controlled by dynamic conformational changes that regulate ion flow through a central pore . This process , which involves opening and closing of the channels , is known as gating . To fully understand or to control ion channel gating , we need to unravel the underlying principles . Crystal structures , especially of K+ channels , have provided excellent insights into the conformation of different channel states . However , the transition states and structural rearrangements are still unknown . Here we use molecular dynamics simulations to simulate the full transition pathway and energy landscape of gating . Our results suggest that channel gating involves local structural changes followed by global conformational changes . The importance of many of the residues identified in our simulations is supported by experimental studies . The ability to accurately simulate the gating transitions of ion channels may be beneficial for a better understanding of ion channel related diseases and drug development .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Methods"
] |
[
"physics",
"computational",
"chemistry",
"molecular",
"dynamics",
"biophysic",
"al",
"simulations",
"chemistry",
"biology",
"computational",
"biology",
"biophysics",
"simulations",
"biophysics"
] |
2013
|
Probing the Energy Landscape of Activation Gating of the Bacterial Potassium Channel KcsA
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We construct a model of brain circulation and energy metabolism . The model is designed to explain experimental data and predict the response of the circulation and metabolism to a variety of stimuli , in particular , changes in arterial blood pressure , CO2 levels , O2 levels , and functional activation . Significant model outputs are predictions about blood flow , metabolic rate , and quantities measurable noninvasively using near-infrared spectroscopy ( NIRS ) , including cerebral blood volume and oxygenation and the redox state of the CuA centre in cytochrome c oxidase . These quantities are now frequently measured in clinical settings; however the relationship between the measurements and the underlying physiological events is in general complex . We anticipate that the model will play an important role in helping to understand the NIRS signals , in particular , the cytochrome signal , which has been hard to interpret . A range of model simulations are presented , and model outputs are compared to published data obtained from both in vivo and in vitro settings . The comparisons are encouraging , showing that the model is able to reproduce observed behaviour in response to various stimuli .
In recent years there has been widespread use of near infrared spectroscopy ( NIRS ) to monitor brain oxygenation , haemodynamics and metabolism [1] , [2] . Initially the primary chromophores of interest were oxy- and deoxy-haemoglobin , with changes ( termed ΔHbO2 and ΔHHb , respectively ) being measured using differential spectroscopy systems [3]–[5] . Technical developments made possible the measurement of absolute tissue oxygen saturation ( TOS ) . This quantity has been variously labelled rSO2 ( regional saturation of oxygen , Somanetics INVOS systems ) , TOI ( tissue oxygenation index , Hamamatsu NIRO systems ) and StO2 ( tissue oxygen saturation , Hutchinson InSpectra systems ) . TOS provides a percentage measure of mean oxygen saturation across all vascular compartments in the tissue of interest . TOS has been used extensively as a marker of tissue oxygenation in a range of applications [6]–[9] but its relationship to underlying physiology is still under investigation [10] , [11] . In addition to the haemoglobin chromophores , the CuA centre in cytochrome c oxidase ( CCO ) is a significant NIR absorber . Measurement of the changes in oxidation level of this centre give rise to a signal , here referred to as the ΔoxCCO signal , which has been extensively investigated as a marker of cellular oxygen metabolism [12]–[15] . A number of clinical studies have been performed to elucidate its role as a measure of cerebral well being [16]–[18] . Although in the case of TOS and ΔoxCCO there are no obvious “gold standard” measurements against which a direct experimental validation can be performed , these NIRS signals undoubtedly encode information of biological and , potentially , clinical importance on tissue oxygen levels , blood flow , metabolic rate ( CMRO2 ) , and other underlying state variables in the brain . However the mapping between NIRS signals and the underlying variables is not straightforward , as a number of different causes may give rise to the same signal changes . The data on CCO redox state is particularly difficult to interpret because of the potential complexity of the correlations between physiological changes and mitochondrial redox states [12] , [19] . Thus in order to correctly interpret and maximise the clinical usefulness of the information that can be extracted from NIRS data , a model of the underlying physiology is required . This is our aim in this paper . The model we construct is based on thermodynamic principles , and is to date the only model which attempts to predict the state of the CuA centre in cytochrome c oxidase in an in vivo setting . It is designed to be able to simulate responses to physiologically and clinically important stimuli ( listed below ) , and is able to reproduce several experimental data sets including both in vivo data , for example on NIRS signal changes during functional activation [5] , and in vitro data on mitochondrial flux and redox state during hypoxia and uncoupling [20] . Moreover our simulations suggest important practical conclusions: For example , that the ΔoxCCO signal contains information independent of that contained in the other NIRS signals , and that physiological variability between individuals has the potential to affect its relationship with the haemoglobin signals . The model is designed to respond to four input stimuli , which have been chosen both because they are physiologically important , and because there is considerable data on the response of NIRS signals to these inputs . The stimuli can be expected to cause changes in the different NIRS signals via a variety of different physiological pathways . They are One key consideration has been to keep the model small enough to allow eventual optimising of key parameters to an individual's data . This would be required if the signals were to be used to interpret physiological changes in an individual , for example in the clinical setting . For this reason rather than attempting to append a model of mitochondrial metabolism to the large and complex BRAINCIRC model [25] , we have used this model as the basis for a much simpler model . In order to increase readability , model differential equations , and tables of model variables and parameters are presented in Text S1 . The model was written and simulated in the open source BRAINCIRC interface [26] and is available for download [27] , complete with instructions on how to reproduce each of the simulation plots presented in this paper .
The model consists essentially of two components . The first is a submodel of the cerebral circulation , which is known to respond in complicated ways to a variety of stimuli – physical , chemical and neuronal [28] . Though much of the physiology is still under investigation , there are a variety of more or less simplified models which attempt to capture some features of this control . Among these are the models of Ursino and co-workers ( [29] , [30] for example ) , the model of Aubert and Costalat [31] , and the BRAINCIRC model [25] described in [32] and still under active development . All of these models have contributed to the construction of the model described in this paper . The second component of the model presented here is a submodel of mitochondrial metabolism . Several such models exist , notably the models of Korzeniewski and co-workers ( e . g . , [33] ) and Beard and coworkers [34] , [35] . These models have also played a large part in the construction of our model , and processes here are often either caricatures or refinements of processes in these models . The two components of the model are linked via oxygen transport and consumption . The basic structure of the model is illustrated in Figure 1 . In order to aid model validation , a smaller mitochondrial model appropriate to in vitro situations will also be introduced later . In particular this model omits all processes relating to blood flow , with oxygen being supplied directly to the mitochondria . Following the normal simplification in most chemical models , all chemical reactions are assumed to take place in solution in compartments . A reference brain volume is assumed ( although never needed explicitly ) and other volumes are calculated as fractions of this reference volume . Thus “blood volume” and “mitochondrial volume” will refer to blood/mitochondrial volume per unit brain volume . Processes take place at two sites: in a blood compartment , divided into an arterial compartment with variable volume , a capillary compartment with negligible volume , and a venous compartment with fixed volume; and a mitochondrial compartment with fractional volume Volmit which can be interpreted as ml mitochondrial volume per ml tissue . The arterial volume Volart and venous volume Volven are expressed as fractions of normal total blood volume , so that in normal conditions , Volart+Volven = 1 . In other words they measure ml arterial/venous blood per ml normal blood volume . Following [33] , the presence of buffers in the mitochondria serves effectively to enlarge mitochondrial volume as seen by protons . We define an effective mitochondrial volume for protons VolHi = RHiVolmit whereCbuffi and dpH are constants . As discussed above , all volumes are taken as fractions of a reference volume and are thus , strictly speaking , dimensionless . When the reference volume is not clear the complete units will be presented . In general , chemical concentrations are millimolar ( mM ) , with the reference volume being implicit ( so for example concentration of a substance Y in mitochondria has units millimoles Y per litre of mitochondrial internal volume ) . The exceptions are when a unit conversion is carried out to follow convention or to facilitate comparison with data , as in the case of NIRS quantities which are generally in μM and where the reference volume is brain volume even when the quantity is confined to some specific compartment . All blood pressures and partial pressures of gases are in mmHg . For readability , units will be generally omitted from the text but are presented in the Sections B and C of Text S1 . The first component of the model is a basic representation of the mechanics of cerebral blood flow . This part of the model is a simplification of the detailed biophysics in [29] , where regulation occurs at two sites—a proximal and a distal arterial compartment , each responding to stimuli differently . We constructed a version of this model with a single compartment which was able to reproduce steady state responses to stimuli adequately , and so in our model here , a single compartment is used . Certain processes are omitted , including the viscous response of blood vessels , and the complexities of the venous circulation . The conductance of the circulation , G , determines cerebral blood flow CBF according to the ohmic equationPa and Pv are arterial and venous blood pressure respectively , which are parameters external to the model . Cerebral blood flow ( CBF ) in the model means the volume of blood which flows through a unit volume of tissue in unit time . G is taken to be a function of a typical “radius” r of the resistance vessels according to the Poiseuille law:KG is simply a constant of proportionality . r is determined by the balance of forces ( 1 ) where Te and Tm are , respectively , the elastic and muscular forces developed in the vessel wall , both functions of the radius , and Pic is extravascular pressure ( assumed to be constant ) . ( Pa+Pv ) /2 is an average intravascular pressure . Following [29] the elastic tension is given an exponential dependence on radius: ( 2 ) Here σe0 , Kσ , r0 and σcoll are parameters , while h is the vessel wall thickness , set by conservation of wall volume according to the equation: ( 3 ) h0 represents wall thickness when vessel radius is r0 . The muscular tension is given by ( 4 ) Tm has a bell-shaped dependence on radius , taking value Tmax at some optimum radius rm . rt and nm are parameters determining the shape of the curve . Maximum muscular tension Tmax is a crucial quantity , and is affected by all stimuli which cause changes in vascular smooth muscle tension . To this end it is useful to define a dimensionless quantity μ which represents the level of regulatory input , givingTmax0 is a constant and kaut is a control parameter , normally set to 1 , but which can be lowered to simulate loss of a vessels ability to respond actively to stimuli . μ varies between a minimum value of μmin and a maximum value of μmax . The level of regulatory input depends on the level of stimuli capable of producing a response in vascular smooth muscle . These stimuli are combined into a dimensionless quantity η which determines μ via a sigmoidal function:A single compartment with these functional responses was found in preliminary simulations to be able to reproduce experimentally observed steady state responses well . Further details are presented in the results and in Text S1 . In the model , four quantities are capable of producing direct or indirect responses in vascular smooth muscle and hence affecting η: arterial blood pressure , oxygen levels ( taken for simplicity to be mitochondrial oxygen levels ) , arterial CO2 pressure PaCO2 , and demand , which we represent as a dimensionless parameter u . In its action within mitochondria , u may be identified with the ADP/ATP ratio , while in its effect on blood flow it can be seen as the level of the substrates connected with neurovascular coupling . u is introduced in order primarily to simulate , via a single parameter , the events occurring during functional activation . In order to construct η , we define four quantities and vu . These are essentially Pa , [O2] , PaCO2 and u , respectively , passed through first order filters , in order to represent possibly different time constants associated with each of these stimuli: ( 5 ) The time constants τx control how long it takes for each stimulus to have a vasoactive effect . Given that blood flow regulation in response to a single stimulus often involves multiple processes occurring on different time scales ( for example direct and metabolic effects of hypoxia ) , use of a single time constant for each stimulus is necessarily an approximation . η is chosen to be linear in all stimuli:The parameters RP , RO , RC and Ru represent the sensitivities to changes in the different stimuli while vx , n represents the normal value of vx , so that at normal values of all stimuli η = 0 , and hence μ = ( μmin+μmax ) /2 . Collapsing the complexity of the biology into a single quantity η will necessarily have some pitfalls . However for our purposes here , the simple form of η is sufficient . Knowledge of oxygen levels in blood is necessary both in order to interpret haemoglobin related NIRS signals , and also in order to calculate oxygen transport to tissue . It is conceptually simplifying to consider oxygen binding sites on haemoglobin as the chemical of interest , with concentration four times the concentration of haemoglobin . Thus oxyhaemoglobin concentration will refer to the concentration of filled oxygen binding sites on haemoglobin . Arterial oxyhaemoglobin concentration [HbO2 , a] is calculated from arterial saturation SaO2 and total haemoglobin concentration in arterial and venous blood [Hbtot] ( assumed constant ) via [HbO2 , a] = SaO2[Hbtot] . A quantity JO2 can be defined as the rate of oxygen flux from blood to tissue ( in micromoles O2 per ml tissue per second ) . A key requirement is that total O2 supplied to the tissue is matched by oxygen delivery . This requirement is encoded in an equation ( 6 ) [HbO2 , a] and [HbO2 , v] are the arterial and venous concentrations of oxygenated Hb respectively . From the venous oxyhaemoglobin level we can calculate a venous saturation SvO2 = [HbO2 , v]/[Hbtot] . The concentration of oxyhaemoglobin will clearly vary along the capillary bed . Defining a typical capillary oxygen saturation ScO2 = ( SaO2+SvO2 ) /2 we can use this to calculate a typical capillary oxygen concentration ( 7 ) φ is the concentration of dissolved oxygen giving half maximal saturation , while nh is the Hill exponent of the dissociation curve . Clearly choosing this form for dissolved oxygen ignores possible complications arising from the Bohr effect ( see [36] for example ) . By choosing a simplified form for the level of capillary oxygen , we run the risk of miscalculating oxygen delivery . An example of a more complete treatment using a distributed model can be found in [37] . In order to investigate the possible errors introduced by this simplification a distributed model was solved numerically and the true average capillary oxygen concentration compared to that calculated from Equation 7 . The results are presented in Section D of Text S1 . The approximation causes consistent overestimation of capillary oxygen concentration introducing an error of approximately 2 . 5 percent in normal circumstances . During severe ischaemia this error can grow to 6 percent . In order to minimise model complexity , we accept this level of error in the current model . The process by which oxygen is supplied to the mitochondria is assumed to be diffusive occurring at a rate ( 8 ) where [O2] is the mitochondrial oxygen concentration , and DO2 is the diffusion coefficient . In order to ensure that arterial oxygen supply can never exceed tissue oxygen delivery ( and thus avoid venous oxygenation becoming negative ) we do not allow the value of supply to exceed CBF[HbO2 , a] , i . e . we set JO2 = min{DO2 ( [O2 , c]−[O2] ) , CBF[HbO2 , a]} . More details on this crude methodology for modelling a process which properly requires PDE modelling are given in Appendix C of [32] . For in vivo simulations where oxygen saturation may decrease significantly , in order to avoid non-smooth behaviour we use the smooth approximation to the function , choosing ε in this case to be CBFn[HbO2 , a , n]/10 . Equations 6–8 collectively serve to determine the values of [HbO2 , v] , [HbO2 , c] , [O2 , c] and JO2 and need to be solved simultaneously . A key variable measurable using NIRS is tissue oxygen saturation ( TOS ) , the average saturation level of blood in the brain for which an absolute value can be obtained . This can be expressed as a value between 0 and 1 or as a percentage , and in the equations below we choose the former . In addition , changes in tissue oxy- , deoxy- , and total haemoglobin concentration ( as distinguished from blood concentrations ) , termed ΔHbt , ΔHbO2 and ΔHHb respectively and measured in μmol ( l tissue ) −1 can be calculated . In order to calculate TOS , we need only the relative volumes Volart and Volven ( and no value for the fractional volume of blood per unit brain volume ) . Ignoring the capillaries , which are assumed to have small volume , we getNext we assume that Volart is proportional to r2 so that where Volart , n and rn are the normal values of Volart and r . Dividing the expression for TOS through by the normal arterial volume , Volart , n , and defining normal arterio-venous volume ratio AVRn = Volart , n/Volven , then gives:In order to define the other NIRS quantities we require some estimate of absolute blood volume in the tissue . So we define a parameter Volblood , n in ( ml blood ) ( ml tissue ) −1 , and get the tissue concentrations of total , oxy- and deoxy-haemoglobin in μmol ( l tissue ) −1 as , respectively:The factor of 1000 arises from conversion from mM to μM , while division by 4 occurs because of our definition of Hb as binding sites on haemoglobin . Multiplication by Volblood , n is to convert to tissue concentrations . NIRS signals ΔHbt , ΔHbO2 and ΔHHb are thenwhere Hbtn , HbO2n and HHbn are normal values of Hbt , HbO2 and HHb . The second key component of the model is a basic submodel of mitochondrial dynamics centred in particular on the oxidation state of the CuA centre in cytochrome c oxidase . The inspiration for this model comes from the detailed models of [33] and [34] , and the abstract model in [38] . However , in order to minimise model size , many of the processes in [33] and/or [34] have been omitted: in particular phosphate and ADP/ATP transport , and the adenylate kinase and creatine kinase reactions . Further , the behaviour of complexes I-III has been lumped into a single process . On the other hand somewhat more detail has been included in the treatment of complex IV ( cytochrome c oxidase ) with a view to more accurate information on the redox state of the CuA centre . It is worth mentioning that the simplifying assumption of a single site of oxidative metabolism ignores the diverse roles of neurons and astrocytes in brain energy metabolism . Two redox centres in cytochrome c oxidase are identified explicitly , CuA , and the terminal electron acceptor cytochrome a3 ( henceforth termed cyta3 ) . Each of these centres can exist in either an oxidised or a reduced form . A reducing substrate transfers electrons ( directly or indirectly ) to CuA , which in turn transfers its electrons to cyta3 . Finally cyta3 transfers its electrons to oxygen . These three electron transfers , which we will refer to as reaction 1 , reaction 2 and reaction 3 , occur at rates f1 , f2 and f3 . These rates are taken to be the rates of transfer of four electrons between substrates . They are accompanied by the pumping of protons across the mitochondrial membrane , and hence both create and are affected by the proton motive force Δp ( also termed PMF , discussed below ) . The structure of this submodel is shown in Figure 2 . From here on , we represent the concentration of oxidised and reduced CuA by CuAo and CuAr respectively . Similarly oxidised and reduced cyta3 are represented by a3o and a3r respectively . The total concentrations of CuA and cyta3 in mitochondria are assumed constant at some value cytoxtot . The proton motive force Δp has both a chemical and an electrical component and has the formHere ΔΨ is the mitochondrial inner membrane potential , pHo is pH in the intermembrane space assumed to be a constant or controllable parameter . Z = RT/F where F is the Faraday constant , R is the ideal gas constant , and T is the absolute temperature . The dynamics of ΔΨ are discussed below . Protons move across the mitochondrial membrane in both directions . A quantity p1 of protons are pumped out during the reduction of four CuA centres , and p2 are pumped out during their oxidation , and p3 are pumped during the final oxidation of cyta3 . ptot = p1+p2+p3 is thus the total number of protons pumped out of the mitochondria during the reduction of one molecule of O2 . The value of p1 , and hence ptot , will depend on the reducing substrate . The protons pumped out of mitochondria during electron transfer return into the mitochondria via leak channels at rate Llk , and via processes associated with ATP production ( i . e . through Complex V , and during ADP/ATP and phosphate translocation ) at a rate LCV . Thus the total return of protons into the mitochondria occurs at rate L = LCV+Llk . Following [33] the leak rate is exponentially dependent on Δp:Llk0 and klk2 are parameters controlling the sensitivity of the leak current to changes in Δp . kunc is a control parameter , normally set to 1 , used to simulate the effect of adding uncouplers to the system . It is only altered during simulations of the simplified model of isolated mitochondria described below . LCV depends on both Δp and the demand u . The formis chosen . If we identify the demand parameter u with an ( appropriately rescaled ) ADP/ATP ratio , we see that this form is similar to that for the rate of complex V in [33] . It is also qualitatively similar to the form in [39] despite the apparent complexity of the form in that reference . The parameter Δp CV 0 is the value of Δp at which , given normal demand , LCV goes to zero . kCV controls the sensitivity of the rate to changes in Δp . rCV controls the relative sizes of maximal and minimal rates of LCV . If nA protons enter the matrix for every molecule of ADP phosphorylated , the actual rate of ADP phosphorylation is LCV/nA . The current consensus value of nA is given as 4 . 33 in [40] . Note that because of differences in the constructions of the two models , the parameter nA has a somewhat different meaning to its counterpart in [33] . Following the methodology in [35] , [41] , the rate of change of ΔΨ depends only on the flows of protons across the membrane and is given byCim is the capacitance of the mitochondrial inner membrane . We now return to reactions 1 , 2 and 3 with rates f1 , f2 and f3 . For simplicity each of these rates refers to the transfer of four electrons . The processes associated with rates f1 and f2 are assumed to be reversible . Assuming first order kinetics for f1 giveswhere k1 and k−1 are the forward and backward rate constants for the reaction . Although the details of how the rate constants change with changes in Δp are not known in advance , the equilibrium for the reaction can be set from energetic principles: Associated with f1 we have a free energyThe important quantity E1 is discussed further in Section C of Text S1 . Setting ΔG1 = 0 determines the equilibrium constant of the reaction Keq1 , givingTo allow for inhibition by changes in the proton motive force , k1 is set aswhere k1 , 0 is the value of k1 at normal Δp . Since demand or experimental set-up may influence the redox state of the initial reducing substrate k1 , 0 is not a constant ( details in Section C of Text S1 ) . The exponential term reflects inhibition of the forward rate by Δp , and the strength of this inhibition is controlled by the parameter ck1 . The backward rate constant is then determined from the equilibrium constant: A very similar process can be used to set f2 . Again , forward and backward rate constants k2 and k−2 are assumed , givingThis time the free energy isgiving the equilibrium constant Keq2k2 is then set asck2 controls the effect of changes in Δp on k2 . The backward rate constant is simplyReaction 3 is assumed to be irreversible , and its rate f3 is set as ( 9 ) The quantities c3 and Δp30 are parameters controlling the sensitivity of f3 to Δp . From the above form it is possible to calculate an apparent second-order rate constant for the reaction taking place at zero PMF as ( 10 ) Values of this parameter can be experimentally measured [42] and the measured values are used to determine the value of k3 in the model . As f3 is the rate of oxygen consumption it is used to calculate the crucial model output: ( 11 ) In order to simplify the model we have assumed that control of cytochrome c oxidase is via Δp alone , ignoring the fact that changing ΔpH and ΔΨ can have different effects on cytochrome c oxidase turnover [43] . The NIRS ΔoxCCO signal can be identified as the change , in μM , in the tissue concentration of oxidised CuA . In order to model this quantity , we defineThe factor of 1000 is to convert from mM to μM , while multiplication by Volmit—mitochondrial volume as a fraction of tissue volume—converts from mitochondrial to tissue concentration . Apart from the model described above , in order to set parameters and compare model behaviour to experimental data a simpler submodel is also constructed . This model will be referred to as the simplified model while the model described above will be referred to as the full model . The simplified model is designed to simulate in vitro experiments on mitochondrial solutions , and so omits a number of processes in the full model . A schematic of this model is shown in Figure 3 . The key differences between the simplified mitochondrial model and the full model are that all processes and feedback involving blood flow are removed . Mitochondrial O2 becomes a control parameter rather than a model output , and the reducing substrate is not automatically assumed to be NADH , but may be chosen to be other substrates such as succinate or TMPD . The simplified model can also model experimental data involving uncouplers: These are molecules , generally protonophores , that uncouple oxygen consumption from oxidative phosphorylation , allowing rapid electron transfer with no ATP synthesis . Data from experiments such as that in [20] can then be used for model parameter setting or model validation .
The steady state response of cerebral blood flow to changes in blood pressure gives rise to “autoregulation” curves with blood flow being insensitive to changes in blood pressure around the physiological value [44]–[47] . This is obviously key behaviour that our model must be able to reproduce . Steady state responses of cerebral blood flow to other stimuli , in particular PaCO2 , are also well characterised experimentally [48] . The model steady state blood flow responses to changes in blood pressure and CO2 levels are plotted in Figure 4 . The pressure autoregulation curve is consistent with experimental curves ( e . g . the autoregulation curve in [44] constructed from data in [46] , [47] ) and modelled curves ( e . g . using the model in [29] ) . Data from these studies was used to set model parameters as described in Section E of Text S1 . The value of RC has been set so that model steady state response to changes in PaCO2 is consistent with published data [48] . Data from a hypercapnia study described below suggests that the magnitude of this response may vary between individuals . Functional activation provides a repeatable challenge giving rise to discrete changes in metabolic demand , which can be assumed to be primarily cerebral . Since its inception in 1993 [49]–[52] , the study of functional activation by NIRS ( fNIRS ) has rapidly become one of the main drivers in the development of NIR technology for monitoring the human brain . Yet there have been few studies focusing on the ΔoxCCO signal , despite its potential to inform on the critical question of neurovascular coupling . In 1999 , a paper reported on oxidation of ΔoxCCO during fNIRS [15] . Despite a number of attempts to dismiss this result as an optical artefact , the basic finding has resisted such explanations [53] . However , whether the oxidation can be explained physiologically ( effect of increased oxygen delivery ) or biochemically ( effect of increased ATP turnover ) is not clear . In order to shed light on such questions , functional activation was simulated in the model , via a step up in the demand parameter u . A ten second activation was simulated by running the model at normal parameter values for 10 seconds , followed by a 10 second increase in u , followed by a further ten seconds at baseline . The responses of various quantities are plotted in Figure 5 . As expected , the increase in blood flow more than compensates for the increase in CMRO2 so that TOS goes up . The ratio of changes in blood flow to changes in CMRO2 is consistent with the data in [54] where a ratio of 2∶1 is typical , although higher values are reported in [55] . Also clear from the data is that at normal parameter values an increase in demand causes oxidation of CuA , and hence an increase in the ΔoxCCO signal consistent in direction , but smaller in magnitude ( by about 50 percent ) than the typical traces in [5] . Below we show that , perhaps surprisingly , this effect is not primarily dependent on an increase in blood flow and blood oxygenation . The behaviour of the other NIRS signals—ΔHbO2 , ΔHHb and ΔHbt—during functional activation is plotted in Figure 6 . Changing the time constant associated with demand ( τu ) affects the shape of the response , and the magnitude of a slight initial increase in deoxygenated haemoglobin before it starts to drop . Both the levels and direction of change of the haemoglobin signals are comparable with previous experimental data [24] , although the magnitudes predicted are somewhat higher than reported in [5] . Consistent with the analysis in [38] , both the size and the direction of ΔoxCCO change in response to functional activation are sensitive to a number of model parameters including the baseline PMF and values of the standard redox potentials . One interesting question is whether the effect is driven solely by the increase in cerebral blood flow associated with functional activation . A simple way to test this is by abolishing the response of blood flow to demand by setting Ru = 0 . This reduces the ΔoxCCO increase ( by about 40 percent ) but does not abolish it ( results not shown ) . In this light it is interesting to run an analogous simulation involving a step up in demand on the simplified mitochondrial model . Such a change can be identified with a transient increase in the ADP/ATP ratio in an in vitro situation . As in the in vivo case , there was a small but significant oxidation of CuA . To see whether this oxidation is a robust response to activation , the level of activation was varied so that CMRO2 varied between 80 percent and 170 percent of baseline . The results of both simulations are plotted in Figure 7 . As is clear from Figure 7 , increased demand oxidises CuA even in the simplified model where there is no change in oxygen level . Qualitatively similar results are obtained when an increase in demand is replaced with uncoupling . These results suggest the important conclusion that the change in the ΔoxCCO signal during functional activation is primarily associated with changes in proton motive force rather than being slaved to changes in oxygen levels . The ΔoxCCO signal thus appears to encode information about cerebral metabolic state independent of that contained in the other NIRS signals . It is also interesting to note this work supports the conclusion of [55]: That in the physiological range , an increase in CBF is not required for the observed increase in CMRO2 to take place . In order to verify this , the full model was run with Ru = 0 so that demand had no effect on blood flow . Again , significant increases in CMRO2 – up to about 45 percent – could occur . The relationship between oxygen levels and CMRO2 was also consistent with data in [55] as shown in Figure 8 . Understanding the response of the ΔoxCCO signal to changes in oxygen concentration is central to understanding much experimental data . Yet the details of this response are controversial , even when measured during in vitro experiments in cells and mitochondria . Partly this arises from the technical difficulty of making measurements at low oxygen concentrations ( see [56] for a lively discussion of this from one author ) . In particular , debate has centred around the Km for oxygen consumption , which is known to be a complex function of cell metabolism [57] . Even simple models suggest that there is no need for standard Michaelis-Menten type behaviour of consumption rate with oxygen levels [58] . Apart from the uncertainties in the behaviour of consumption when oxygen concentration is dropped , there are also uncertainties about how mitochondrial redox states change in this situation . Again the quantitative response cannot be heuristically predicted , and there is contradictory data in the literature [59] , [60] . We used our simplified model to explore some of these questions . There are very few reliable papers reporting on changes in the CuA redox state with oxygen; therefore we focussed on a key paper that reported on cytochrome c redox state changes [20] , which we have shown is likely to be in close redox equilibrium with CuA during enzyme turnover [61] . Here we show that our model is capable of reproducing quantitatively key results from [20] . In Figure 9 the behaviour of redox state of cytochrome c and the equivalent data for CuA in the model are presented . There is good agreement between the experimental and modelled data . The figure caption gives details of the simulation . The apparent Km for oxygen of mitochondrial oxygen consumption is quoted as 0 . 8 μM in [33] , consistent with values in [20] . The behaviour of CMRO2 as [O2] is lowered in the simplified model is illustrated in Figure 10 . Details of the simulations are presented in the figure legend . For the coupled mitochondria , half-maximal CMRO2 occurs at a little less than 1 μM O2 . For the uncoupled mitochondria half-maximal CMRO2 occurs below 0 . 1 μM O2 . ( In order to calculate the Vmax—and hence Km—values in the case of the coupled mitochondria , larger values of oxygen than shown were needed . As with the model in [58] , the graph does not fit a simple Michaelis-Menten curve well . In the uncoupled case the graph was blown-up for very low oxygen values in order to determine the Km value . ) The model values are consistent with the results in [20] . It should be noted that the low value of u ( high phosphorylation potential ) used in these simulations was essential to get the marked lowering of apparent Km during uncoupling . Without this choice , the Km for coupled mitochondria is also very low , suggesting that experimental results of this kind might be sensitive to experimental details such as the levels of ADP supplied . In [58] we showed that the lowering of the Km for oxygen during uncoupling can be achieved assuming that the effect of uncoupling is to inhibit the reverse reaction during which electrons are transferred from cyta3 to CuA . However in the model presented in that paper the lowering in Km was not accompanied by any increase in flux . As shown in the graphs above our new model can simultaneously achieve an increase in flux and a drop in the Km for oxygen . Obtaining the qualitative behaviour shown in Figure 9 , the quantitative match in Figure 10 , and the qualitative behaviour during functional activation in [5] and [24] was achieved by varying the six model parameters which control the response of reaction rates to Δp: i . e . Δp30 , c3 , ck1 , ck2 , LCV , 0 , rCV and Δp CV 0 . This is discussed further in Section C of Text S1 . As NIRS-derived parameters report on oxygen delivery and consumption in the brain , there is obviously wide interest in the effect of hypoxia on the NIRS signals . Indeed hypoxia is by far the most common in vivo NIRS challenge , especially in animal models . It is also amongst the most controversial , with different mathematical algorithms leading to different conclusions about the relationship between the haemoglobin-based NIR signals and that of ΔoxCCO [22] , [62]–[64] . Even with a single algorithm [65] different physiological explanations have been proposed for the changes during hypoxia ( large decrease in oxCCO from baseline ) and immediately post-hypoxia ( small increase in oxCCO from baseline ) . Currently the debates in this area have revolved around the physics of making the measurements ( choice of wavelengths , effect of multiple tissue layers on light transport , etc . ) Moreover , the systems studied have not always been identical ( animal models versus humans and newborn versus mature ) , raising the possibility of differences in the underlying biochemistry and physiology . Therefore an analysis of how our model behaves during hypoxia , and how variations in the model parameters affect the relationship between the NIR signals , is clearly important , being independent of measurement concerns and allowing an exploration of possible effects of physiological variation . The dynamic and steady state responses of modelled NIRS signals to hypoxia were explored . In the first simulation a one minute drop in arterial oxygen saturation from 96 percent to 80 percent was carried out . The results are plotted in Figure 11 . Following hypoxia there is an increase in blood flow leading to a partial restoration of TOS ( and to a lesser extent ΔoxCCO ) during the hypoxia . This behaviour is connected with the rapidity of the drop in arterial oxygen saturation and so in simulations of real hypoxias ( see next section ) this adaptation is unlikely to be observed . Both TOS and ΔoxCCO show an overshoot associated with the hyperaemia following reoxygenation , consistent with some experimental observations [65] . In [66] data on the relationship between ΔHbO2 and ΔoxCCO during hypoxia is presented . In order to test the model behaviour in this situation , a steady state simulation ( as in the production of steady state curves above ) was carried out . The results of this simulation are plotted in Figure 12 . In [66] a very clear biphasic relationship was reported between ΔHbO2 and ΔoxCCO . At normal parameter values , although the model does predict increased sensitivity of ΔoxCCO to oxygen levels at lower oxygen levels , the biphasic relationship is slight ( Figure 12A ) . Interestingly , lowering both demand ( and hence baseline CMRO2 ) and normal blood flow leads to a considerably more marked nonlinearity in the relationship ( Figure 12C ) . This simultaneous change in demand and normal flow leads to a normal TOS of about 60 percent consistent with that calculated from the absolute oxy- and deoxy-haemoglobin values in [66] . This leads to some interesting questions . In both of the simulations above , ΔoxCCO has an approximately linear relationship with CMRO2 ( Figure 12B and 12D ) , and so any significant drop in ΔoxCCO implies that arterial oxygen supply can no longer match demand – an event we can term metabolic failure . The simulations indicate that the threshold for metabolic failure can be more or less sharp depending on the normal matching of oxygen supply and demand for an individual . They raise the possibility that the relationship between ΔHbO2 and ΔoxCCO during hypoxia may depend on differences between species , age , and possibly individual , with some individuals being more vulnerable to hypoxia . This may have important implications for clinical management of patients in neurocritical care . In the future we intend to challenge our model to reproduce a wide variety of in vivo data sets . Here we present preliminary results in this direction . First we compared our model output to experimental data from subjects undergoing the most common challenge used to provoke responses in the oxCCO signal – cerebral hypoxia . The data is from a study described in [67] . Modelled and measured TOS and ΔoxCCO signals for a subject undergoing a hypoxic challenge are presented in Figure 13 . The stimuli were a series of drops in inspired oxygen and consequent drops in arterial oxygen saturation . Experimentally measured inputs to the model were SaO2 , PaCO2 and mean arterial blood pressure . All inputs were down-sampled to 1 Hz . The baseline value of the ΔoxCCO signal has been brought to zero , and in order to remove high frequency noise the data has been filtered using a 5th order low pass Butterworth filter with a cut-off frequency of 0 . 1 Hz ( Matlab Mathworks Inc . ) In spite of the known inter-subject and regional variability in TOS , both baseline TOS and changes in TOS are predicted well for this subject by the model . The model seems to slightly underestimate ΔoxCCO signal changes , although given the level of noise in the experimental data the extent of this is not clear . As a test of the model's behaviour in the context of changes in arterial CO2 , NIRS data from healthy subjects monitored while undergoing moderate hypercapnia , described in [68] , was compared with model predictions . In this study , the only NIRS signal monitored was TOS . There was wide variation in baseline TOS between subjects , corresponding to natural variability in blood flow and CMRO2 , but more importantly to the fact that the arterio-venous ratio in the region of tissue queried can have high variability . In all cases the modelled and measured data were qualitatively comparable before any attempt to optimise model parameters . However a good fit to the data could be obtained by varying two parameters: Normal arterio-venous ratio AVRn , and RC , the sensitivity of blood flow to PaCO2 . Despite the fact that information is often not clearly visible in the data ( see Figure 14A , for example ) , in all cases but one , optimisation gave positive values for RC , in other words , the model was able to detect a positive cerebrovascular reactivity to CO2 in the data—a fact which is potentially of clinical importance ( [69] for example ) . Two examples of data-sets before and after fitting are presented in Figure 14 . Overall , preliminary comparisons between modelled and measured in vivo data are encouraging . A future task will be to compare further data from these studies and other in vivo studies with model outputs . A basic model of the control of cerebral blood flow and the behaviour of various NIRS signals has been presented . The model is relatively simple , containing very few dynamic variables , but nevertheless preliminary simulations show that it is capable of reproducing basic expected behaviours , and matching experimentally measured data . One important conclusion from these simulations is that the ΔoxCCO signal contains information above and beyond what is available from the other NIRS signals . This in turn gives more hope of achieving the ultimate aim: Real time reconstruction from NIRS data of underlying physiological events of clinical importance . So far , several model parameters have only been set heuristically , and comparison with measured data has not been systematic . The immediate next stage is to explore systematically the effects of model parameters on important model behaviours , for example on the Km for oxygen during hypoxia and the direction of the ΔoxCCO signal during activation . Once key outputs are identified it will be possible to carry out a sensitivity analysis of the kind carried out in [34] . Parallel to identifying how model behaviour is sensitive to parameter values , is the need to identify which parameters are liable to show variability between individuals , or between health and pathology . Some of our observations in these directions are presented in Text S1 . Once these parameters have been identified , optimisation of the kind described in Figure 14 can focus on setting these parameters from an individual's data . A number of limitations of the model have been pointed out in the text . The limitations we consider most serious are: By running sensitivity analyses and comparisons with experimental data it will become clear which of these limitations affect model behaviour appreciably , enabling us to refine the model as necessary . The process of gathering data needed to help validate the model is ongoing . Once the model is well validated it should be possible to integrate its use into the normal NIRS measurement process , greatly enriching the value of the measured data .
|
Monitoring the brain noninvasively is key to solving various biological and clinical problems . Near-infrared spectroscopy ( NIRS ) is a technique that can measure changes in the colour of the brain . The brain has an absolute requirement for oxygen; the spectroscopically observed colour changes are due to the proteins that deliver ( haemoglobin ) and consume ( mitochondrial cytochrome c oxidase ) oxygen . Haemoglobin changes colour when it binds oxygen . The changes in cytochrome c oxidase are due to the electron occupancy ( reduction ) of a particular copper metal centre in the enzyme . The way that the state of this enzyme changes in various situations is poorly understood . Currently there is no theoretical model that can be used to decode simultaneously all of the spectroscopic changes in these proteins , and thus limited information about the underlying biochemistry and physiology can be extracted from the NIRS signals . We therefore constructed such a model , ensuring that it is consistent with the scientific literature , in vivo data , and the underlying thermodynamic principles . The model was able to predict the physiological and spectroscopic responses to a wide range of stimuli , including changes in brain activity and oxygen delivery . It is likely to be of significant value to a wide range of clinical and life science users .
|
[
"Abstract",
"Introduction",
"Methods",
"Results/Discussion"
] |
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"mathematics",
"biochemistry/chemical",
"biology",
"of",
"the",
"cell",
"computational",
"biology/metabolic",
"networks",
"biochemistry/theory",
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"simulation",
"physiology/integrative",
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2008
|
A Model of Brain Circulation and Metabolism: NIRS Signal Changes
during Physiological Challenges
|
Protein phosphorylation and dephosphorylation ( catalysed by kinases and phosphatases , respectively ) are post-translational modifications that play key roles in many eukaryotic signalling pathways , and are often deregulated in a number of pathological conditions in humans . In the malaria parasite Plasmodium , functional insights into its kinome have only recently been achieved , with over half being essential for blood stage development and another 14 kinases being essential for sexual development and mosquito transmission . However , functions for any of the plasmodial protein phosphatases are unknown . Here , we use reverse genetics in the rodent malaria model , Plasmodium berghei , to examine the role of a unique protein phosphatase containing kelch-like domains ( termed PPKL ) from a family related to Arabidopsis BSU1 . Phylogenetic analysis confirmed that the family of BSU1-like proteins including PPKL is encoded in the genomes of land plants , green algae and alveolates , but not in other eukaryotic lineages . Furthermore , PPKL was observed in a distinct family , separate to the most closely-related phosphatase family , PP1 . In our genetic approach , C-terminal GFP fusion with PPKL showed an active protein phosphatase preferentially expressed in female gametocytes and ookinetes . Deletion of the endogenous ppkl gene caused abnormal ookinete development and differentiation , and dissociated apical microtubules from the inner-membrane complex , generating an immotile phenotype and failure to invade the mosquito mid-gut epithelium . These observations were substantiated by changes in localisation of cytoskeletal tubulin and actin , and the micronemal protein CTRP in the knockout mutant as assessed by indirect immunofluorescence . Finally , increased mRNA expression of dozi , a RNA helicase vital to zygote development was observed in ppkl− mutants , with global phosphorylation studies of ookinete differentiation from 1 . 5–24 h post-fertilisation indicating major changes in the first hours of zygote development . Our work demonstrates a stage-specific essentiality of the unique PPKL enzyme , which modulates parasite differentiation , motility and transmission .
Reversible protein phosphorylation is a ubiquitous regulatory process for a variety of eukaryotic and prokaryotic pathways , including cell-cycle regulation , cell to cell signalling , cell proliferation and differentiation [1] . In humans , aberrant regulation of protein phosphorylation has been implicated in cancers [2] , and plays a central role in many other pathological diseases [3] . Protein phosphorylation and dephosphorylation is catalysed by protein kinases and phosphatases , respectively [4]–[7] , and nearly two-thirds of the proteins encoded in the human genome are believed to be modified by reversible phosphorylation [8] at over 650 , 000 phosphosites ( PhosphoNET KnowledgeBase ( www . phosphonet . ca ) ) , emphasizing the importance of this post-translational modification . This regulatory mechanism is highly conserved [3] , and plays a vital role in development of apicomplexan protozoan parasites of the genus Plasmodium [9] , [10] , which are globally responsible for over a million deaths annually through malaria [11] . The Plasmodium life-cycle proceeds via a number of distinct developmental stages: asexual exo-erythrocytic proliferation in liver hepatocytes and intra-erythrocytic multiplication in erythrocytes of the vertebrate host , and sexual development in the female Anopheles mosquito [12] . During both asexual and sexual development the parasite utilises a number of signalling pathways , many of which involve reversible protein phosphorylation . Systematic functional analyses of plasmodial protein kinases ( PKs ) in both the human parasite P . falciparum and the rodent model P . berghei species have revealed that over half of their kinome is essential to asexual blood stage schizogony [9] , [10] . Furthermore , reverse genetic studies in P . berghei have shown that a further 14 PKs have specific functions during sexual development of the parasite within the mosquito mid-gut lumen and subsequent migration to the salivary glands [10] , [13]–[21] . In particular , development of the motile and invasive ookinete within the mosquito mid-gut is known to be highly dependent upon two NIMA-related kinases , NEK2 and NEK4 [14] , [15] . Of the three invasive stages of malaria parasite development ( namely the sporozoite , merozoite and ookinete ) , the ookinete stage is unique in that it can develop extracellularly and lacks rhoptries , an organelle specifically associated with cell invasion . This is in contrast to the other “zoite” forms ( merozoites and sporozoites ) that develop intracellularly and contain both rhoptries and micronemes as apical organelles . However , all of these invasive stages comprise a unique cortical structure termed a pellicle , which consists of a parasitic plasma membrane and an underlying double membrane structure termed the inner membrane complex ( IMC ) [22] . Parasite motility is powered by an actin myosin motor termed the glideosome [23] , which resides within the pellicle of invasive stages . At the molecular level , the motility and mid-gut invasion of the ookinete involves the secretion of a number of membrane proteins including the motor complex-associated proteins glideosome-associated protein 45 ( GAP45 ) and myosin-A tail domain interacting protein ( MTIP ) [24] , micronemal proteins such as circumsporozoite-and trap-related protein ( CTRP ) [25] and secreted ookinete adhesive protein ( SOAP ) [26] , as well as calcium-dependent protein kinase 3 ( CDPK3 ) [21] . Although the role of plasmodial kinases has been intensively studied , the role of all the complementary protein phosphatases ( PPs ) during any stage of Plasmodium development is unknown . Biochemical studies have shown that P . falciparum possesses predominantly phosphatase-1-like activities , and chemical inhibition of phosphatase activity by use of calyculin A and okadaic acid significantly reduces asexual blood stage proliferation [27] . In contrast to the human phosphatome ( comprising approximately 156 phosphatases [28] ( and PhosphoNET KnowledgeBase ( www . phosphonet . ca ) ) , the Plasmodium genome codes for one of the smallest phosphatomes of all the eukaryotic phyla known to date , with 27 putative protein phosphatases falling into four major classes: phosphoprotein phosphatases ( PPPs ) , metallo-dependent protein phosphatases ( PPMs ) , protein tyrosine phosphatases ( PTPs ) and NLI interacting factor-like phosphatases ( NIFs ) [28] . Like the kinases ( although fewer in number ) , the Plasmodium phosphatome contains members that have no orthologues in mammalian systems [28] . The function of the Shewanella-like PPs ( Shelphs ) and the likely inactive pseudo-phosphatase EF-hand containing phosphatase ( EFPP ) is completely unknown [29]; however , in P . falciparum , Shelphs are postulated to have a role in erythrocyte invasion [30] . Furthermore , a distinctive PP1-related enzyme belonging to the PPP family of phosphatases comprising an N-terminal kelch repeat domain and a C-terminal PP1-like phosphatase domain ( named PPKL: Protein Phosphatase with Kelch-Like domains ) has been detected in the apicomplexans Cryptosporidium hominis , Toxoplasma gondii and Theileria parva ( one gene per genome ) , as well as in the land plants Arabidopsis thaliana and Oryza sativa ( 4 and 5 genes respectively ) [31] , [32] . The kelch motif is widespread and involved in many cellular functions , particularly in actin-based cytoskeleton formation and transcriptional regulation [33] . PPKL itself has only been studied in detail in Arabidopsis thaliana ( where it is known as BSU1 or bri1 suppressor1 ) and along with the kinase BIN2 , is involved in brassinosteroid hormone signalling and phosphorylation of the transcription factors BZR1 and BES1 [34] . Even though PPKL was first discovered in P . falciparum ( initially named PfPPα ) , where RT-PCR analysis showed mRNA expression exclusively in gametocytes and hence suggesting a role during sexual development [35] , its function has remained unknown . Transcriptomic and proteomic studies of P . falciparum [36] and P . berghei [37] showed high levels of ppkl transcripts in gametocytes and PPKL protein in ookinetes , respectively ( PlasmoDB ) , although discordant results were found in a study of the proteome of sex-specific gametocytes [18] . In this study , we have used P . berghei to elucidate the function of the PPKL enzyme during the Plasmodium life-cycle . We show by using reverse genetics that Plasmodium PPKL has an essential function during ookinete differentiation , affecting apical end integrity , collar and pellicle morphology , and microtubule linkage to the IMC that are crucial for parasite shape , motility and invasion .
To identify PPKL-like phosphatases in Plasmodium , we used BSU1 from Arabidopsis thaliana to seed an iterative profile-based similarity search [38] . Phylogenetic analysis based on a common phosphoesterase domain ( Pfam: PF00149 ) revealed a robust family of BSU1-like proteins encoded in the genomes of land plants , green algae and alveolates ( Figure 1A , B ) , but not other eukaryotic lineages , in agreement with previous studies [31] , [32] . All members of the BSU1-like family including the single example contained in each Plasmodium spp share a distinctive conserved protein architecture , with N-terminal kelch repeats followed by the phosphatase domain . P . berghei PPKL contains five complete and one truncated kelch repeat ( Figure 1A ) , a configuration which is conserved in most identified orthologues . Phylogenetic trees and identity matrices of individual kelch domains showed that the fourth domain has the highest identity between species , while the sixth was the least similar ( Figure S1 ) . The phosphatase domain of P . berghei PPKL shows the greatest similarity to type 1 and 2A protein phosphatases . All the important signature motifs of the serine/threonine phosphatase ( STP ) family are present ( Figure S2 ) ; they form the active site , and are known to have a role in metal ion binding ( GDxHG , GDxVDRG , and GNHE ) [39] . The binding site for the inhibitor microcystin is conserved , but some of the residues important for docking the PP1 inhibitor Inhibitor-2 and the PPP inhibitor okadaic acid are not conserved , making it difficult to predict in silico the inhibitor specificity of this enzyme . Additionally , we identified 4 conserved insertions ( Figure S2I – IV ) in the catalytic domain , which are only present in Apicomplexa , suggesting a specific role for these sequences in this group . Transcriptomic and proteomic studies in Plasmodium [36] , [37] have suggested that ppkl transcripts and PPKL protein are present in gametocytes and ookinetes only , respectively , although PPKL is not present in the proteome of sex-specific gametocytes [18] . To confirm this , we analysed ppkl mRNA expression by qRT-PCR and PPKL protein expression and localisation by generation of a C-terminal green fluorescent protein ( GFP ) fusion protein of endogenous ppkl ( PBANKA_132950 ) using a single crossover recombination strategy ( Figure S3A–E ) . ppkl mRNA is expressed in asexual blood stages , gametocytes and ookinetes , with the highest expression in schizonts ( Figure 2A ) relative to hsp70 and arginyl-tRNA synthetase genes used as controls in this assay . The intensity of PPKL-GFP fluorescence in sexual stages was highest in the nuclear and cytoplasmic compartments of female gametocytes and zygotes , and in the apical cytoplasm of ookinetes . The protein was also detected in oocysts and sporozoites , but was not observed in activated microgametocytes or microgametes ( Figure 2B ) . These data suggest that in sexual stages PPKL is female-specific and has a role during sexual development . We also investigated whether PPKL is an active phosphatase in P . berghei . Using 3-O-methylfluorescein phosphate ( MFP ) as a substrate , we found that PPKL-GFP immunoprecipitated with GFP-TRAP beads from parasite lysates had phosphatase activity proportional to the amount of lysate in the assay; whereas the WT parasite lysate controls produced much lower fluorescence levels ( Figure 2C ) . The residual fluorescence observed in the WT lysate compared to the corresponding control without MFP substrate might be due to endogenous phosphatase activity non-specifically pulled-down by the GFP-TRAP beads . Our results therefore indicate that PPKL has protein phosphatase activity , correlating with its orthologue in Arabidopsis , BSU1 [34] . To examine the function of PPKL during the Plasmodium life-cycle , we used a double homologous recombination strategy to replace the endogenous ppkl gene with a dhfr/ts selectable marker from Toxoplasma gondii ( Figure S4A–E ) . Analysis of two ppkl deletion mutant clones from two independent transfections , named ppkl− cl3 and ppkl− cl9 identified no phenotypic changes during asexual proliferation in terms of parasite growth , erythrocyte invasion or morphology and no effect on sexual stage cell development ( gametocytogenesis ) , as assessed on blood smears ( data not shown ) . Gametogenesis in the activated microgametocyte was also comparable to wild-type controls ( Figure 3A ) . However , analysis of in vitro cultures for 18–24 h to monitor differentiation into ookinete stages [40] showed that cultures of ppkl− mutants were dominated by grossly abnormal retort forms [41] ( Figure 3B , C , S5C ) . As a result of this observation , we performed a time-course analysis of ookinete differentiation over 6 , 9 , 12 , 15 , 18 and 24 h . Development of the zygote through stages I–III of ookinete maturity ( 0–9 h post-fertilisation ) [41] was indistinguishable in the ppkl− mutants compared to wild-type . Wild-type controls showed normal progression and maturation to stage IV ( at 12 h ) , with 69% of all macrogamete-derived parasites progressing to stage VI 24 h post-fertilisation ( Figure 3D ) . In contrast , the majority of ppkl− mutants did not progress from stage III to stage IV , but produced a high proportion of abnormal retort forms 24 h post fertilisation ( 35% of the total population ) with only 4% progressing to stage IV–VI . This suggests that PPKL is essential for maturation , differentiation and morphological development of ookinetes from stage III to stage IV . We next examined whether the defect was sex-specific by performing genetic crosses between ppkl− parasites and lines deficient in either male ( map2− ) or female ( nek4− ) gametes [14] , [40] . Cross-fertilization with nek4− parasites did not rescue the phenotype , whereas crossing with map2− gametocytes resulted in 26% of all macrogamete-derived parasites progressing to stage VI , revealing that the requirement for the phosphatase is inherited through the female line ( Figure 4A ) . To substantiate the in vitro findings , parasites in mice infected with either wild-type or ppkl− gametocytes were fed to mosquitoes to analyse oocyst development . Wild-type oocysts developed normally; whereas no oocysts were found in the guts of mosquitoes fed on ppkl− parasites when analysed 14 and 21 days after feeding ( Figure 4B ) . This result confirms that PPKL is vital to ookinete development and that oocyst formation is completely blocked in the ppkl− parasites . Due to the ablation of oocyst development in our in vivo study ( Figure 4B ) , as well as defects in the morphology and maturation of ppkl− mutants , we analysed gliding motility of wild-type and ppkl− parasites by embedding cultured ookinetes in dilute Matrigel and using time-lapse video microscopy to quantify gliding movement [20] . Using this method , we found that wild-type parasites followed a characteristic helical gliding motion with an average speed of 4 . 74 µm/min ( Figure 4C and Video S1 ) , in close agreement with previous studies [20] . Strikingly , gliding motility in ppkl− retorts was significantly reduced compared to wild-type parasites ( 0 . 13 µm/min; p<0 . 001 ) , although occasional limited forward motion and “flexing” at the apical end was observed ( Figure 4C and Video S2 ) . To assess whether the ablation of oocyst development in ppkl− mutant parasites was also due to a defect in invasion of the mid-gut epithelium , we bypassed the gut barrier by injecting ppkl− parasites directly into the haemocoel of A . stephensi mosquitoes and analyzed salivary gland invasion 20 days post-injection [42] . Using this method , we found that ppkl− parasites were able to form viable sporozoites , which could migrate to the salivary gland and actively invade ( Table 1 ) . Onward transmission experiments via a mosquito biting resulted in infection in mice with both wild-type and ppkl− lines . Subsequent analysis of parasites recovered from these lines and cultured in ookinete medium for 24 h confirmed the knockout phenotype in the ppkl− lines . This strongly suggests that sporozoites of ppkl− mutants are able to migrate to and invade the salivary glands , as well as undergo exo- and intra-erythrocytic proliferation , but the abnormal retorts of ppkl− are not able to penetrate the epithelial lining of the mosquito mid-gut . To examine whether the morphology of the ppkl− mutant retorts could be linked to their inability to glide and invade , we performed ultrastructure analyses using transmission electron microscopy ( TEM ) . Significant structural differences were detected between wild-type and ppkl− mutant parasites at the apical end of the ookinete . Wild-type ookinetes had a very uniform appearance with a conical shaped apical end ( Figures 5Ai ) , with unique and complex structures [43] consisting of an enclosing plasmalemma , beneath which is a conical electron dense collar with a central aperture and in firm contact with the IMC ( Figure 5Aiii , S5A ) . Beneath the collar , but in close contact with it , is a second somewhat smaller and less electron dense layer termed the apical ring ( Figure 5Aiii , S5A ) , which is subtended by the sub-pellicular microtubules . The apical end also has a large number of micronemes with fine ducts running through the aperture in the collar to the apical plasmalemma ( Figure 5Aiii , S5A ) . In contrast , ppkl− retort forms showed marked variation in shape with clear constriction and elongation of the apical end ( Figure 5Aii , 5Avii and S5B ) . Detailed examination of the apical end showed structural differences from the wild-type , namely: the electron dense collar's reduced length ( Figure 5Aiv , S5B ) ; the apical ring appeared reduced in size compared to the wild-type , resembling a series of small vesicles and some loss of connection to the collar ( Figure 5Aiv ) ; the microtubules run longitudinally from the ring , but while they were closely associated with the IMC in the wild-type parasite ( Figure 5Av ) , they were more disorganised in the mutant and the electron dense links between the microtubules and the IMC were not found ( Figure 5Avi ) ; in severe cases , collapse of the apex resulted in groups of tubules within a neck-like region not associated with the IMC ( Figure 5Avii , viii ) . Due to the collapse of the apex , fewer micronemes were located in the apical region , but those present appeared normal with ducts running to the apex ( Figure 5Aiv , S5B ) . The other cytoplasmic and nuclear features were similar in both wild-type and mutant parasites ( Figure 5Ai , ii ) . In summary , there is a loss of apical integrity due to defects in the collar and the attachment between the microtubules and IMC possibly resulting in an immotile parasite . Indirect immunofluorescence analysis using antibodies for the cytoskeletal proteins tubulin and actin showed mislocalisation with intense atypical tubulin staining at the apical end of the ppkl− abnormal retort as well as loss of the normal apical staining for actin . While the motor complex proteins GAP45 and MTIP showed no obvious abnormal pattern in ppkl− lines , micronemal CTRP showed less distinct apical staining and is more distributed throughout the body of the retort . This may be due to the structural and cytoskeletal changes at the apical end . However , no change in the normally diffuse intracellular distribution of the microneme-associated protein SOAP was observed ( Figure 5B ) . The protein kinase NEK4 and the DDX6-class RNA helicase DOZI are essential to female/zygote development [14] , [18] , [44] . To ascertain whether PPKL is regulated by either of these , we analysed mRNA expression of the phosphatase in mutants of NEK4 and DOZI by qRT-PCR . Expression of ppkl compared to wild-type was only significantly altered in both non-activated and activated gametocytes of the nek4− mutant ( p<0 . 001 for both ) . In ppkl− mutants , transcript levels of nek4 compared to wild-type controls were indistinguishable in both total asexual blood stages and non-activated and activated gametocytes ( Figure 6A ) . However , dozi was significantly up-regulated compared to wild-type ( p<0 . 01 for all stages analysed ) , suggesting that PPKL could be involved in the function of this RNA helicase . As studies in Arabidopsis thaliana have shown that the activity of BSU1 is regulated by phosphorylation [45] , we investigated whether PPKL was phosphorylated in P . berghei using PPKL-GFP parasites metabolically labelled for 30 min with 32P-orthophosphate , lysed and immunoprecipitated with GFP-TRAP to assess PPKL phosphorylation in vivo . Activated gametocytes showed a higher level of phosphorylation compared to schizonts ( 7 . 6 times ) as assessed by measuring the ratio of the intensity of the PPKL phosphorylation signal on the autoradiograph to the intensity of the PPKL protein band on the Western blot ( Figure 6C , arrow ) . As PPKL is essential for ookinete maturation , we assessed the impact of ppkl deletion on protein phosphorylation during ookinete development . This was achieved by analysing global phosphorylation profiles of wild-type and ppkl− parasites 1 . 5 , 6 and 24 h post gametocyte activation ( pga ) using a metabolic labelling technique [46] . Three representative fractions ( 9–11 ) , in which significant differences in phosphorylation were observed are shown in Figure 6C . Equal protein loading was assessed by Coomassie blue staining ( data not shown ) . While the majority of the phosphorylated proteins remained unchanged in wild-type versus ppkl− lysates , as early as 1 . 5 h pga ( Figure 6C , arrows ) , and also later in zygote development ( 6 h ) ( Figure 6C , arrowhead ) and 24 h pga ( Figure 6C , asterisk ) , we observed several specific changes , including both increases and decreases in phosphorylation levels . These data suggest that although PPKL is involved in the regulation of the phosphorylation status of specific proteins throughout ookinete development , the largest impact of PPKL on protein phosphorylation occurs early ( 1 . 5 h ) after gametocyte activation .
Reversible phosphorylation is a major regulator for many cellular processes . The kinases are well recognised as important signalling molecules and drug targets in various diseases , but phosphatases have been neglected and their roles remained elusive until recently [32] , [47] . Although recent systematic functional analyses of the Plasmodium kinome have identified a number of potential drug targets [9] , [10] , studies on phosphatases in Plasmodium have been mostly limited to biochemical studies . Recently however , the total protein phosphatome of P . falciparum was published and showed that the genome of this malaria parasite codes for one of the smallest known eukaryotic phosphatomes , comprising 27 phosphatase sequences [28] . We have shown that the unique phosphatase PPKL is encoded by a single-copy gene belonging to a robust family that is only detected in Viridiplantae and Alveolata , confirming previous studies [31] , [32] . Analysis of wild-type mRNA transcription and protein expression confirmed the findings of previous global studies [36] , [37] , suggesting that PPKL is expressed during sexual development . Furthermore , we have also shown that PPKL is an active phosphatase , which in gametocyte/gamete stages is female-specific and in the ookinete is found preferentially at the apical end . However , the localisation is not uniquely nuclear but is also cytosolic , contrasting the exclusively nuclear localisation of the Arabidopsis BSU1 phosphatase [34] . Our functional studies have shown that the only point of essentiality for PPKL in the malaria parasite is ookinete differentiation , particularly in the latter stages when the machinery for motility and invasion is formed . Zygote development is normal in ppkl− mutants , but ookinete differentiation is grossly impaired , producing a parasite population dominated by retorts with abnormal morphology and varying degrees of structural deformities . By using a motility assay we show here that the gliding motility of ppkl− abnormal retorts is virtually abolished . The data suggest that apical structure and organelle distribution are determined at the early female/zygote stage . We also show that this defect is inherited through the female line . Previous studies have shown that many defects at the zygote/ookinete stage are carried through maternal inheritance of a number of ookinete specific molecules [42] . In addition , the haemocoel infection experiment clearly suggests that this impairment is restricted to the ookinete stage since by-passing the mid-gut barrier by direct injection of mutant retorts into the haemocoel allowed oocyst formation and the production of invasive sporozoites that could cause blood stage infection after a mosquito bite-back . However , the abnormality persisted at the subsequent ookinete stage in these ppkl− parasites . The ookinete is morphologically and biochemically distinct from sexual stage gametocytes and zygotes , as well as the later oocyst and sporozoite stages . It is also the only motile and invasive stage that is formed extracellularly , does not require invasion of a new host cell as it moves between the mosquito gut cells in traversing to the basal surface and also develops extracellularly into the next stage . Ultrastructure analyses using TEM revealed severe structural abnormalities in the apical end of the mutant retort forms , particularly that of microtubule organisation and association with the IMC , as well as a reduction in the length of the apical ring and distinct shortening of the apical collar . The apical complex of the ookinete is unique amongst the invasive Plasmodium stages . In particular , the distinctive ookinete morphology is maintained through an array of microtubules , the subpellicular network [22] , and a lattice of intermediate filaments . These are enclosed by the pellicle consisting of the plasmalemma and two underlying closely associated unit membranes formed from flatten vacuoles and referred to as the IMC . The structural abnormalities seen in the microtubular association with the IMC , as well as the mislocalisation of cytoskeletal proteins in ppkl− lines could explain the distinctive morphology of the mutant . It would also explain why no ppkl−-associated defects are observed in the rest of the life-cycle since the unique apical complex of the ookinete is absent in both merozoites and sporozoites . Immunofluorescence studies with antibodies to the microneme marker CTRP suggested that in ppkl− lines CTRP is distributed throughout the parasite body and the apical end localisation as seen in the wild-type parasite is not observed . It is important to note however , that even though CTRP and SOAP are both microneme-associated proteins , the localisation we see in our wild-type parasites is consistent with previous studies [26] , [48] , with CTRP showing intense apical staining and SOAP localisation more diffuse throughout the cell body . The altered distribution of CTRP in ppkl− lines could more likely be due to the structural abnormality at the apical end , since we still observe a high concentration of apical micronemes in the mutants . Although this may explain their inability to move and invade the gut wall , it has been shown previously in misfit disruption mutants [49] that even in the absence of micronemes , parasites could still form oocysts . Nevertheless , this particular defect in MISFIT is quite different to ppkl− because it shows paternal inheritance and a range of distinct molecular defects . The location of markers of the motor complex such as GAP45 and MTIP did not show any marked difference between wild-type and ppkl− parasites . However , the mislocalisation of the cytoskeletal protein tubulin and of actin in ppkl− parasites is consistent with the structural defects identified in the ultrastructure studies . In mammalian systems protein phosphatases are important in microtubule formation and are regulated during the cell cycle [50]; it is therefore possible that PPKL has a direct role in microtubule distribution , consistent with the aberrant morphology of the ppkl− retorts . Cellular processes are regulated through complex signalling networks , although very little is known in Plasmodium . Studies of kinases have demonstrated the importance and specificity of protein phosphorylation at every stage of the Plasmodium life-cycle . For example , a number of kinases are implicated in male gamete ( CDPK4 , SRPK , MAP2 ) , female gamete ( NEK2 , NEK4 ) , zygote ( NEK2 , NEK4 ) , and ookinete ( PK7 , GAK ) development [10] , [51] . We have shown that ppkl transcripts are down-regulated in nek4− mutants . Furthermore , we have also shown that PPKL is phosphorylated in both schizonts and activated gametocytes , confirming phosphoproteomic studies indicating that PPKL is itself phosphorylated [52] and suggesting that its activity or interactions with other proteins may also be regulated by phosphorylation . As a precedent , phosphorylation of the Arabidopsis PPKL orthologue , BSU1 , by the constitutive differential growth 1 ( CDG1 ) kinase results in its activation and the subsequent dephosphorylation of BIN2 [45] , suggesting a fine balance between phosphorylation/dephosphorylation pathways in signalling in plants [53] . Whether such a mechanism operates in Plasmodium is to be elucidated in future studies . The global protein phosphorylation profile during ookinete development of the ppkl− mutant is evidently different from that of wild-type parasites . Most changes were seen within 1 . 5 h of gametocyte activation and zygote formation , suggesting that the function of PPKL is crucial at the early zygote stage even though the phenotype is observed at the ookinete stage . For example , the molecular blueprint for apical end formation may be established following fertilization in the early zygote stage . One intriguing observation is that the qRT-PCR analyses revealed that transcripts of dozi ( which is involved in mRNA processing ) [44] are significantly up-regulated in ppkl− lines . This might suggest that in the absence of the phosphatase the modulation of DOZI is perturbed and this could directly affect the process of translational repression for many proteins in early zygote development . Previous studies have identified IMC-associated proteins that are vital for maintenance of ookinete morphology and virulence , namely IMC1b and IMC1h ( or Alv3 ) [54]–[56] . Disruption and deletion mutants of the nucleotide cyclase guanylyl cyclase β ( GCβ ) and the cyclic nucleotide degrading phosphodiesterase δ ( PDEδ ) , respectively , showed unregulated signalling via cGMP resulting in defective ookinete development and gliding motility [20] , [57] . Moreover , mutants of CDPK3 [21] , micronemal proteins including CTRP and SOAP [25] , [26] , PPL3 and 5 [58] , and other molecules such as PSOP2 and PSOP7 show severe defects in ookinete formation and gliding motility , respectively [42] . Whether PPKL interacts with any of these vital regulators of ookinete biology will be dissected in future studies . In summary , this is the first functional analysis , to our knowledge , of a protein phosphatase in Plasmodium and demonstrates that like the kinases , a phosphatase is also involved in a regulatory pathway in a stage-specific and essential manner .
All animal work has passed an ethical review process and was approved by the United Kingdom Home Office . Work was carried out in accordance with the United Kingdom ‘Animals ( Scientific Procedures ) Act 1986’ and in compliance with ‘European Directive 86/609/EEC’ for the protection of animals used for experimental purposes . The permit number for the project licence is 40/3344 . Tuck-Ordinary ( TO ) ( Harlan ) outbred mice were used for all experiments except for mosquito “bite-back” infections of mice , where C57/Bl6 mice were used . The targeting vector for ppkl was constructed using the pBS-DHFR plasmid , which contains polylinker sites flanking a Toxoplasma gondii dhfr/ts expression cassette conveying resistance to pyrimethamine , as described previously [10] . PCR primers P0011P ( 5′-CCCCGGGCCCCATGTTTTATATTGTGTTTTGGC-3′ ) and P0012P ( 5′-GGGGAAGCTTCAAACATTCGTTTCTTTAAATGATCC-3′ ) were used to generate a 788 bp fragment of 5′ upstream sequence of ppkl from genomic DNA , which was inserted into ApaI and HindIII restriction sites upstream of the dhfr/ts cassette of pBS-DHFR . A 694 bp fragment generated with primers P0013P ( 5′-CCCCGAATTCCCACCAACCCCACCAAGAAGTCAACCG-3′ ) and P0014P ( 5′-GGGGTCTAGACCGGCAAATTGATGAAATCGC-3′ ) from the 3′ flanking region of ppkl was then inserted downstream of the dhfr/ts cassette using EcoRI and XbaI restriction sites . The linear targeting sequence was released using ApaI/XbaI . For GFP-tagging by single homologous recombination , a 1047 bp region of ppkl starting 1693 bp downstream of the ATG start codon and omitting the stop codon was amplified using primers P1tag F ( 5′-CCCCGGTACCGAGCTCCGATAAAAATATATGGTGATATAC-3′ ) and P1tag R ( 5′-CCCCGGGCCCTGGAGCCCCATAATTTAATTCTCTC-3′ ) , producing an amplicon 1047 bp in length . This was inserted upstream of the gfp sequence in the p277 vector using KpnI and ApaI restriction sites . The p277 vector contains the human dhfr cassette , also conveying resistance to pyrimethamine . Before transfection , the sequence was linearized using BglII and P . berghei ANKA line 2 . 34 was then transfected by electroporation [59] . Briefly , electroporated parasites were mixed immediately with 200 µl of reticulocyte-rich blood from a phenylhydrazine ( Sigma ) treated , naïve mouse , incubated at 37°C for 20 min and then injected intraperitoneally . From day 1 post infection pyrimethamine ( 70 µg/ml ) ( Sigma ) was supplied in the drinking water for four days . Mice were monitored for 15 days and drug selection was repeated after passage to a second mouse . Resistant parasites were then used for cloning by limiting dilution and subsequent genotyping . Chromosomes of wild type and gene knockout parasites were separated by pulsed field gel electrophoresis ( PFGE ) on a CHEF DR III ( BioRad ) using a linear ramp of 60–500 s for 72 h at 4 V/cm . Gels were blotted and hybridized with a probe recognizing both the resistance cassette in the targeting vector and , more weakly , the 3′UTR of the P . berghei dhfr/ts locus on chromosome 7 . For the gene knockout parasites , two diagnostic PCR reactions were used as illustrated in Figure S4 . Primer 1 ( INT P1P , 5′- CGCATAAAGTGTTGCATTATATAAATTACAC-3′ ) and primer 2 ( ol248 , 5′-GATGTGTTATGTGATTAATTCATACAC-3′ ) were used to determine successful integration of the selectable marker at the targeted locus . Primers 3 ( P1 KO1P , 5′- CACCCCCAGAAGCTAGATATCAACATACTTGCG-3′ ) and 4 ( P1 KO2P , 5′- GAACTAGGTGAATCGAGCATATTTCTGTAG-3′ ) were used to verify deletion of the gene . Having confirmed integration , genomic DNA from wild type and mutant parasites was digested with EcoRI and the fragments were separated on a 0 . 8% agarose gel , blotted onto a nylon membrane ( GE Healthcare ) , and probed with a PCR fragment homologous to the P . berghei genomic DNA just outside of the targeted region . For the C-fusion GFP tagging parasites , one diagnostic PCR reaction was also used as illustrated in Figure S3 . Primer 1 ( INT P1 , 5′-GGTCAAATGTATCTATATTATGTTC-3′ ) and primer 2 ( ol492 , 5′- ACGCTGAACTTGTGGCCG-3′ ) were used to determine correct integration of the gfp sequence at the targeted locus . Having confirmed correct integration , genomic DNA from wild type and transgenic parasites was digested with EcoRI and the fragments were separated on a 0 . 8% agarose gel , blotted onto a nylon membrane , and probed with a PCR fragment homologous to the P . berghei genomic ppkl sequence using the Amersham ECL Direct Nucleic Acid Labelling and Detection kit ( GE Healthcare ) . Parasites were also visualized on a Zeiss AxioImager M2 ( Carl Zeiss , Inc ) microscope fitted with an AxioCam ICc1 digital camera ( Carl Zeiss , Inc ) and analysed by Western blot to confirm GFP expression . Infections for phenotype screens were initiated by intraperitoneal injection of infected blood containing 5×106 parasites into mice pre-treated with 0 . 2 ml of 6 mg/ml phenylhydrazine in PBS injected intraperitoneally to induce reticulocytosis 3 days prior to infection . Asexual stages and gametocyte production were monitored on Giemsa-stained blood films . Exflagellation was examined on day 4–5 post infection . 10 µl of gametocyte-infected blood were obtained from the tail with a heparinized pipette tip and mixed immediately with 40 µl of ookinete culture medium ( RPMI1640 containing 25 mM HEPES , 20% fetal bovine serum , 10 mM sodium bicarbonate , 50 µM xanthurenic acid at pH 7 . 6 ) . The mixture was placed under a Vaseline-coated cover slip and 15 min later exflagellation centres were counted by phase contrast microscopy in 12–15 fields of view using a 63× objective and 10× ocular lens . Ookinete formation was assessed the next day . 10 µl of infected tail blood were obtained as above , mixed immediately with 40 µl ookinete culture medium , and incubated for 2 h at 20°C to allow completion of gametogenesis and fertilization . Each culture was then diluted with 0 . 45 ml of ookinete medium and incubated at 20°C for a further 21–24 h to allow ookinete differentiation . Cultures were pelleted for 2 min at 5000 rpm and then incubated with 50 µl of ookinete medium containing Hoechst 33342 DNA dye to a final concentration of 5 µg/ml and a Cy3-conjugated mouse monoclonal antibody 13 . 1 [16] recognizing the P28 protein on the surface of ookinetes and any undifferentiated macrogametes or zygotes . P28-positive cells were counted with a Zeiss AxioImager M2 microscope ( Carl Zeiss , Inc ) fitted with an AxioCam ICc1 digital camera . Ookinete conversion was expressed as the percentage of P28 positive parasites that had differentiated into ookinetes [40] . For mosquito transmission experiments 20–50 Anopheles stephensi SD500 mosquitoes were allowed to feed for 20 min on anaesthetized infected mice whose asexual parasitaemia had reached ∼5–7% and were carrying comparable numbers of gametocytes as determined on Giemsa stained blood films . Day 14 post feeding approximately 20 mosquitoes were dissected and oocysts on their mid-guts counted . Oocyst formation was examined by Hoechst 33342 staining for 10–15 min and guts were washed and mounted under Vaseline-rimmed cover slips . Images were recorded using a 63× oil immersion objective on a Zeiss AxioImager M2 microscope fitted with an AxioCam ICc1 digital camera . Day 21 post feeding another 20 mosquitoes were dissected and their guts and salivary glands crushed separately in a loosely fitting homogenizer to release sporozoites , which were then quantified using a haemocytometer . Due to day-to-day variations in transmission levels , all data were normalized to a matching number of wild type controls analyzed on the same day . Using Arabidopsis thaliana BSU1 sequence ( RefSeq: NP_171844 ) as a seed , the iterative strategy enacted by the ‘jackhammer’ method of HMMER3 [38] was used to search the predicted proteomes of 46 diverse eukaryotes [60] with the addition of the Emiliania huxleyi dataset from JGI ( www . jgi . doe . gov ) . Four iterations were made with an inclusion theshold ( e-value ) of <10−25 . 221 sequences matching the final profile at e<10−120 were found to form 3 well-defined clusters using the BLAST-clustering approach [61] . Selected sequences were trimmed to 50 aa either side of the phosphatase domain as defined by Pfam domain PF00149 and aligned using MAFFT6 . 24 [62] . Well-aligned blocks were used to infer a Bayesian phylogeny using the metropolis-coupled Markov chain Monte Carlo ( MCMCMC ) [63] . Four independent runs of 400 , 000 generations were performed from random start trees , using the WAG substitution matrix with a gamma-distributed variation in substitution rate approximated to 4 discrete categories ( shape parameter estimated from the data ) . Protein domain architectures were predicted from the models in Pfam25 ( A and B ) with e-value<10−3 . Additional orthologues from Plasmodium species were identified from PlasmoDB ( http://plasmodb . org/ ) . Phylogenetic trees of the 6 different kelch domains were constructed using kelch phosphatase sequences identified in BLAST searches of eukaryotic genomes . The sequences were aligned using ClustalW2 and optimised using CLC Genomics Workbench ( CLC bio , Cambridge , MA ) . After identifying the 6 kelch-domains the kelch-domain coding sequences were realigned using the same program and neighbour-joining bootstrap trees were generated . The phylogenetic trees were drawn using Fig Tree v1 . 3 . 1 . Purification of gametocytes was achieved using a protocol modified from [64] . Mice were treated by intra-peritoneal injection of 0 . 2 ml of phenylhydrazine ( 6 mg/ml ) ( Sigma ) in PBS to encourage reticulocyte formation four days prior to infection with parasites . Day four post infection ( p . i . ) mice were treated with sulfadiazine ( Sigma ) at 20 mg/L in their drinking water for two days to eliminate asexual blood stage parasites . On day six p . i . mice were bled by cardiac puncture into heparin and gametocytes separated from uninfected erythrocytes on a 48% NycoDenz gradient ( 27 . 6% w/v NycoDenz in 5 mM Tris-HCl , pH 7 . 20 , 3 mM KCl , 0 . 3 mM EDTA ) in coelenterazine loading buffer ( CLB ) , containing PBS , 20 mM HEPES , 20 mM Glucose , 4 mM sodium bicarbonate , 1 mM EGTA , 0 . 1% w/v bovine serum albumin , pH 7 . 25 . Gametocytes were harvested from the interface and washed twice in RPMI 1640 ready for activation of gamete formation . Blood cells from day 5 p . i . mice were placed in culture for 24 h at 37°C for schizont ( with rotation at 100 rpm ) and 20°C for ookinete production as described above . Schizonts and ookinetes were purified on a 60% and 63% NycoDenz gradient , respectively and harvested from the interface and washed . Schizonts and gametocytes were purified as described above and frozen in Trizol ( Sigma ) prior to RNA extraction . Asexual blood parasites were extracted as for gametocytes above but on day four p . i . with very low gametocytaemia and no sulfadiazine treatment . RNA was isolated according to manufacturer's instructions . Isolated RNA was treated with DNase I ( Promega ) and used in reverse transcription reactions ( SuperScript III Reverse Transcription kit , Invitrogen ) from 500 ng of total RNA . Gene expression was quantified by SYBR green PCR using Fast mastermix on an ABI 7500 QPCR System ( Applied Biosystems ) . Primers were designed using the PerlPrimer software program [65] to be 18–22 bp in length , with 30–60% GC content , to amplify a region 50–150 bp long and when possible , to bind within 600 bp of the 3′ end of the genes of interest . Primer efficiencies were all between 90–110% , with qRT-PCR resulting in no detectable primer dimers , as determined by dissociation curves . cDNA was diluted 1∶20 with DEPC-treated water before use . Reactions consisted of 3 . 6 µl of diluted cDNA , 5 µl SYBR green fast mastermix ( Applied Biosystems ) , 0 . 2 µl each of forward and reverse primer and 1 µl of DEPC water . Cycling conditions were: 95°C for 20 s followed by 40 cycles of 95°C , 3 s , and 60°C , 30 s , followed by dissociation curve . Three biological replicates , with three technical replicates from each biological replicate were performed for each assayed gene . Wild-type gene expression was determined using the comparative cycle threshold method [66] , whereas relative quantification in mutant lines was determined using the Pfaffl method [67] . Both methods used hsp70 ( PBANKA_081890 ) ( forward , 5′-GTATTATTAATGAACCCACCGCT-3′; reverse , 5′-GAAACATCAAATGTACCACCTCC-3′ ) and arginyl-tRNA synthetase ( PBANKA_143420 ) ( forward , 5′-TTGATTCATGTTGGATTTGGCT-3′; reverse , 5′-ATCCTTCTTTGCCCTTTCAG-3′ ) as reference genes . ppkl primers were: forward , 5′- TTCTAAAGTACCTTCACCAAGAG-3′; reverse , 5′- TAGCAGGTCCTTCTTTACAC-3′ . map2 ( PBANKA_093370 ) primers were: forward , 5′-AATGAAGAACCAGGGCCA-3′; reverse , 5′-ACCATCTAGTAACTACATGGCT-3′ . nek4 ( PBANKA_061670 ) primers were: forward , 5′-CTTCAGATGTATGGGCTATTGG-3′; reverse , 5′- TTCCCTTTGTTGAATGAAATGG-3′ . dozi ( PBANKA_121770 ) primers were: forward , 5′- GCAAGAATGTCGCAAACAC-3′; reverse , 5′-TCTGAGGAAACTAAACATCGAC-3′ . Western blot analysis was performed on cell lysates prepared by re-suspending parasite pellets in a 1∶1 ratio of PBS containing Protease inhibitor ( Roche ) and Laemmli sample buffer , boiling and separating on a 4%–15% SDS-polyacrylamide gel ( BioRad ) . Samples were subsequently transferred to nitrocellulose membranes ( Amersham Biosciences ) and immunoblotting performed using the Western Breeze Chemiluminescent Anti-Rabbit kit ( Invitrogen ) and anti-GFP polyclonal antibody ( Invitrogen ) at a concentration of 1∶1250 , according to the manufacturer's instructions . Blood aliquots from mice infected with PPKL-GFP and WT parasites were incubated in ookinete medium for 30 min at 20°C and processed as described previously [46] . The resulting parasite pellets were incubated for 30 min at 4°C in lysis buffer ( 10 mM Tris-HCl , pH 7 . 5 , 150 mM NaCl , 0 . 5 mM EDTA , 0 . 5% NP-40 ) supplemented with protease inhibitors ( Roche ) , then the lysates were centrifuged at 20 , 000 g for 5 min and the supernatants were subjected to immunoprecipitation using GFP-TRAP beads ( ChromoTek ) according to manufacturer's instructions . Protein phosphatase activity of the immunoprecipitate was assessed using the SensoLyte MFP Protein Phosphatase Assay Kit ( AnaSpec ) according to manufacturer's instructions . Briefly , the GFP-TRAP beads were resuspended and diluted in phosphatase assay buffer ( 100 mM Tris-HCl pH 7 . 5 , 4 mM DTT , 0 . 2 mM EDTA , 0 . 5 mM MnCl2 , 0 . 4 mg/ml BSA ) , incubated for 30 min at 37°C in the presence or absence of MFP fluorogenic phosphatase substrate , and centrifuged for 2 min at 2700 g . Supernatants were transferred to a 96-well microplate and the fluorescence generated by the dephosphorylation of MFP was measured using a microplate fluorimeter . The presence of PPKL-GFP in the bead pellets was assessed by Western blot . Samples cultured in ookinete medium as described above were fixed in 4% glutaraldehyde in 0 . 1 M phosphate buffer and processed for routine electron microscopy as previously described [68] . Briefly , samples were post fixed in osmium tetroxide , treated en bloc with uranyl acetate , dehydrated and embedded in Spurr's epoxy resin . Thin sections were stained with uranyl acetate and lead citrate prior to examination in a JEOL1200EX electron microscope ( Jeol UK Ltd ) . IFAs were performed on air dried ookinete slides from ookinete cultures produced as previously described [46] . Briefly , infected blood from mice with 7–10% gametocytaemia was incubated for 24 h in ookinete medium at 20°C , then ookinete smear slides were prepared and air dried . For various antibodies different procedures were followed . For α-tubulin , slides were fixed in 4% paraformaldehyde in MTSB buffer [46] and the mouse monoclonal α-tubulin ( Sigma ) was used as the primary antibody ( 1∶1000 dilution ) . For actin staining the procedure of [21] was followed . Briefly , ookinetes were freshly fixed in 4% paraformaldehyde in MTSB with 0 . 2%Triton , followed by methanol fixation for 5 min and stained with monoclonal Dictyostelium anti-actin antibody ( 1∶1000 dilution ) . For GAP45 and MTIP , cells were fixed in 4% paraformaldehyde in MTSB buffer and stained with antibodies against GAP45 ( 1∶250 ) and MTIP ( 1∶250 ) [69] . For CTRP , air dried slides were fixed for 5 min in 1% formaldehyde and mouse monoclonal anti-CTRP antibody ( 1∶1000 ) was used [25]; for anti-SOAP antibodies , slides were fixed in 4% paraformaldehyde and a 1∶100 dilution of antibody was used [26] . For mouse monoclonal antibodies 568 AlexaFluor-labelled anti-mouse ( Invitrogen ) ( 1∶1000 ) was used as a secondary antibody . For GAP45 and MTIP antibodies , AlexaFluor 466 labelled anti-rabbit ( Invitrogen ) ( 1∶1000 ) was used as a secondary antibody . Blood aliquots from infected mice were incubated overnight , from which schizonts were purified as described previously [46] . Gametocytes were purified and activated for 25 min at 20°C in ookinete medium as described above . Schizonts and activated gametocytes were then washed in phosphate-free Krebs buffer ( 118 mM NaCl , 4 . 7 mM KCl , 4 . 2 mM NaHCO3 , 1 . 2 mM MgSO4 , 11 . 7 mM glucose , 10 mM HEPES , 1 . 3 mM CaCl2 , pH 7 . 4 ) and metabolically labelled with 3–5 MBq 32P-orthophosphate in phosphate-free Krebs buffer for 30 min at 20°C or 37°C for activated gametocytes and schizonts , respectively . After two washes in phosphate-free Krebs buffer , the labelled parasites were lysed for 30 min at 4°C in lysis buffer ( 10 mM Tris-HCl pH 7 . 5 , 150 mM NaCl , 0 . 5 mM EDTA , 0 . 5% NP-40 ) supplemented with protease and phosphatase inhibitors ( Roche ) , the resulting lysate was centrifuged at 20 , 000 g for 5 min and the supernatant collected . PPKL-GFP protein was isolated using GFP-TRAP beads ( ChromoTek ) , the immunoprecipitated proteins were then resuspended in Laemmli sample buffer and separated by SDS-PAGE . 32P-labelled proteins were visualized using a phosphorimager ( Molecular Dynamics ) and GFP-tagged proteins analysed by Western blot as described above . The relative PPKL-GFP phosphorylation levels in activated gametocytes with respect to schizonts were obtained by taking the normalized ratio between the intensity of the phosphorylation signal from the phosphoimager and the intensity of the GFP immunoreactive signal from the corresponding Western Blot by using the ImageJ software ( National Institute of Health ) . Gametocytes from wild type , ppkl− and nek4− mutant parasites were purified as described above from the blood of infected mice . Purified gametocytes were placed for 60 min , 5 . 5 h and 23 . 5 h in ookinete medium at 20°C to activate both male and female gametocytes to form gametes . For metabolic labelling , the parasites were washed once with 1 ml of phosphate-free Kreb's buffer and resuspended in 500 µl of the same buffer . 20–25 µl 32P-orthophosphate ( 7–9 . 25 MBq ) was added to the suspension and parasites incubated at 20°C for 30 min . The labelled parasites were then lysed in lysis buffer ( 50 mM Tris , 0 . 5 mM EDTA , 5% β-glycerolphosphate , pH 7 . 6 , supplemented with protease/phosphatase inhibitors ( Roche ) and 1% NP-40 ) . Following incubation on ice for 10 min , the samples were centrifuged for 3 min at 20000 g and the supernatants were collected for further fractionation . Fractionation was carried out on an AKTA chromatographer ( Amersham Pharmacia Biotec ) using Resource Q ( Amersham Pharmacia Biotec ) anion-exchange column ( matrix volume 1 ml ) . The proteins were eluted using a linear gradient of 0–1 . 0 M NaCl in running buffer ( 10 mM Tris , 5 mM EDTA and 20 mM β-glycerolphosphate , pH 7 . 4 ) . Fractions ( 1 ml ) were collected and analysed further by resolution on SDS-PAGE gels . 32P-labelled proteins were visualised by autoradiography . Ookinetes were cultured using standard methods and harvested by pelleting . A . stephensi mosquitoes were anaesthetised with CO2 and approximately 500 ookinetes in 69 µl of complete ookinete medium were injected into the thorax using a microinjector ( Drummond , Nanoject II ) . Bitebacks and dissections for quantification of salivary gland sporozoites were performed 20 days after injection . All statistical analyses were performed using GraphPad Prism ( GraphPad Software ) . For ookinete motility analysis , non-parametric t-tests were used . For relative quantification of qRT-PCR reactions , a pair-wise fixed reallocation randomisation test [67] was used . Sequences are derived from Uniprot ( http://www . uniprot . org/ ) , P . berghei ( Q4Z2M2 ) , P . yoelii ( Q7RA97 ) , P . chabaudi ( Q4XN17 ) , P . falciparum ( Q8IKH5 ) , P . knowlesi ( B3L999 ) , P . vivax ( A5K396 ) , T . gondii ( B6KKQ9 ) , T . parva ( Q4N2C8 ) , C . muris ( B6A994 ) , T . thermophila ( Q22BC4 ) , C . reinhardtii ( A8HNE3 ) , A . thaliana ( Q8L7U5 ) , P . marinus ( C5K554 ) , B . bovis T2Bo ( A7AT59 ) , C . variabilis ( E1ZIE2 ) , V . carteri f . nagariensis ( D8UHM6 ) , S . moellendorffii ( D8RUC3 ) , P . tetraurelia strain d42 ( A0CLB3 ) , M . pusilla CCMP1545 ( C1MQB3 ) , O . lucimarinus CCE9901 ( A4S3K6 ) , N . caninum ( F0VC41 ) , I . multifiliis ( G0QV54 ) .
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Malaria parasites are single-celled organisms , which alternate their life-cycle between vertebrate and mosquito hosts . In the mosquito , the malaria parasite undergoes sexual development , whereby a male and female gamete fuse to form a zygote . This zygote then elongates into an invasive stage , termed an ookinete , which can glide to and penetrate the mosquito's gut wall in order to form a cyst ( called an oocyst ) . Protein phosphorylation is known to play a vital role during this process; however , the role of Plasmodium kinases ( which phosphorylate proteins ) during zygote/ookinete maturation is better understood than the completely uncharacterised plasmodial phosphatases ( which dephosphorylate proteins ) . Using a malaria parasite which infects mice , Plasmodium berghei , we show that a unique protein phosphatase containing kelch-like domains ( called PPKL ) plays a vital role in ookinete maturation and motility . Deleting this gene produces ookinetes whose shape is grossly abnormal , resulting in non-motile parasites that cannot penetrate the lining of the mosquito gut wall . Overall , PPKL is an essential phosphatase that is critical to ookinete development , motility and invasion .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"developmental",
"biology",
"genetics",
"biology",
"microbiology",
"molecular",
"cell",
"biology",
"genetics",
"and",
"genomics"
] |
2012
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A Unique Protein Phosphatase with Kelch-Like Domains (PPKL) in Plasmodium Modulates Ookinete Differentiation, Motility and Invasion
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In a previous study PCR analysis of clinical samples from suspected cases of Buruli ulcer disease ( BUD ) from Togo and external quality assurance ( EQA ) for local microscopy were conducted at an external reference laboratory in Germany . The relatively poor performance of local microscopy as well as effort and time associated with shipment of PCR samples necessitated the implementation of stringent EQA measures and availability of local laboratory capacity . This study describes the approach to implementation of a national BUD reference laboratory in Togo . Large scale outreach activities accompanied by regular training programs for health care professionals were conducted in the regions “Maritime” and “Central , ” standard operating procedures defined all processes in participating laboratories ( regional , national and external reference laboratories ) as well as the interaction between laboratories and partners in the field . Microscopy was conducted at regional level and slides were subjected to EQA at national and external reference laboratories . For PCR analysis , sample pairs were collected and subjected to a dry-reagent-based IS2404-PCR ( DRB-PCR ) at national level and standard IS2404 PCR followed by IS2404 qPCR analysis of negative samples at the external reference laboratory . The inter-laboratory concordance rates for microscopy ranged from 89% to 94%; overall , microscopy confirmed 50% of all suspected BUD cases . The inter-laboratory concordance rate for PCR was 96% with an overall PCR case confirmation rate of 78% . Compared to a previous study , the rate of BUD patients with non-ulcerative lesions increased from 37% to 50% , the mean duration of disease before clinical diagnosis decreased significantly from 182 . 6 to 82 . 1 days among patients with ulcerative lesions , and the percentage of category III lesions decreased from 30 . 3% to 19 . 2% . High inter-laboratory concordance rates as well as case confirmation rates of 50% ( microscopy ) , 71% ( PCR at national level ) , and 78% ( including qPCR confirmation at external reference laboratory ) suggest high standards of BUD diagnostics . The increase of non-ulcerative lesions , as well as the decrease in diagnostic delay and category III lesions , prove the effect of comprehensive EQA and training measures involving also procedures outside the laboratory .
Buruli ulcer disease ( BUD ) , caused by Mycobacterium ulcerans , is an infectious disease affecting skin , soft tissue and bones . If left untreated , extensive destruction of tissue followed by fibrous scarring and contractures may lead to severe functional limitations [1]–[6] . BUD is treated with rifampicin and streptomycin ( or clarithromycin ) for eight weeks if necessary followed by surgical interventions; the laboratory confirmation of clinically suspected BUD cases prior to treatment has become an integral part of clinical management . Whereas microscopy is an appropriate and cost-effective first-line test for peripheral laboratories , PCR is considered the method of choice and WHO recommends PCR confirmation of at least 50% of suspected BUD cases [3] , [7]–[13] . Microscopy and various PCR assays have been successfully implemented in other endemic countries and case confirmation rates of 29–78% ( microscopy ) and 54–83% ( PCR ) were reported [10] , [12]–[32] . Since the early 1990s , close to 2 , 000 BUD cases were reported from Togo . However , due to the lack of local diagnostic laboratory capacity , the majority of these cases remained unconfirmed [7] , [13] , [33]–[35] . From 2007 through 2010 , a joint research project between the German Leprosy and Tuberculosis Relief Organization , Togo office , Lomé , Togo ( DAHWT ) and the Department of Infectious Diseases and Tropical Medicine ( DITM ) , University Hospital , Ludwig-Maximilians-University , Munich , Germany , allowed the first systematic study on laboratory confirmation of BUD cases from Togo and proved the prevalence of BUD in South Togo ( region “Maritime” ) . The study revealed a relatively poor performance of local Ziehl-Neelsen microscopy , suggesting the need for a stringent system for external quality assurance ( EQA ) including regular supervision of microscopy laboratories . Intensified training measures in the area of sample collection resulted in a PCR case confirmation rate of 70% . Effort and turnaround time associated with shipment of samples to an external reference laboratory , however , necessitated the availability of local laboratory capacities [13] . In the context of the EC-funded research project “BuruliVac” ( FP7/2010–2013; grant agreement N° 241500 ) , the implementation of a national reference laboratory for BUD in Togo was envisaged . Therefore , from January 2011 through April 2012 , microscopy and PCR facilities were established at the “Institut National d'Hygiène” ( INH ) , Lomé , Togo . This study describes the approach to implementation of a national reference laboratory and analyzes the impact of intensified EQA and training measures on laboratory diagnosis and control of BUD in Togo .
Ethical clearance was obtained through the national Togolese ethics committee ( “Comité de Bioéthique pour la Recherche en Santé” ) at the University of Lomé ( 14/2010/CBRS ) and the study was approved by the “Ministère de la Santé de la République Togolaise” Lomé , Togo ( Ref . No . 0009/2011/MS/DGS/DPLET ) . All samples analyzed in this study were collected for diagnostic purposes within the EC funded research project “BuruliVac” . Written informed consent was obtained from all study participants . This study constitutes a collaborative project between several Togolese and German institutions . Since 2007 , the German Leprosy and Tuberculosis Relief Organization ( DAHW ) has supported the Togolese National Buruli Ulcer Control Program ( “Programme National de Lutte contre L'Ulcère de Buruli – Lèpre et Pian” [PNLUB-LP] ) in the area of training , laboratory confirmation and treatment of BUD . In this study , the main tasks of DAHWT , as partner of the “BuruliVac” consortium were field work , recruitment of study participants , and collection of diagnostic samples . The tasks of DITM – an accredited laboratory according to DIN EN ISO 15189 - as lead partner for all patient related activities of the “BuruliVac” project consisted of implementation of molecular diagnostic laboratory methods at the designated national Togolese BUD reference laboratory and standardization of all processes through on-site training , standard operating procedures ( SOPs ) , and EQA of microscopy and PCR ( by standard gel-based IS2404 PCR and IS2404 quantitative real-time PCR [qPCR] ) including supervisory visits . Patients with suspected BUD were referred to peripheral health posts ( “Unité de Soins Périphérique” , USP; operating on district level as point of care facilities with a catchment area of 5 , 000–9 , 000 inhabitants depending on the number of facilities per district ) , or a regional hospital ( “Centre Hospitalier Régional [CHR] de Tsévié” , region “Maritime” , Togo , since 2007 national reference centre for BUD in Togo; catchment area: 2 , 599 , 955 inhabitants ) for diagnosis and treatment; CHR conducted microscopic analysis . The “Institut National d'Hygiène” ( INH ) , Lomé , Togo – a laboratory accredited by COFRAC ( “Comité Français d'Accréditation” ) according to NF EN ISO/CEI 17025 ( version 2005 ) – constitutes the national Togolese reference laboratory for surveillance of transmissible , especially outbreak prone diseases , and has been nominated national reference laboratory for Buruli ulcer disease in 2010 [13] . In this study , INH resumed EQA for microscopy conducted at regional level and – after installation of a BUD PCR laboratory – PCR assessment of diagnostic samples by means of a dry-reagent-based PCR [21] , [25] , [29] . In March 2011 , INH joined the WHO network for laboratory confirmation of BUD and – like DITM – participates in the annual program for external quality assessment of molecular detection of M . ulcerans in clinical specimens provided by the Mycobacteriology Unit , Microbiology Department , Institute for Tropical Medicine , Antwerp , Belgium , WHO Collaborating Centre for the diagnosis and surveillance of M . ulcerans infection [36] . In each of the six districts ( Golfe , Ave , Zio , Yoto , Vo , Lac ) of the region “Maritime” , five districts ( “Direction de District Sanitaire” [DDS] 1–5 ) of the region “Lomé Commune” where BUD was proven to be endemic [13] and the four districts of the region “Central” ( Blitta , Sotouboua , Tchaoudjo , Thamba ) , where BUD has been assumed to be endemic , outreach teams ( “CLT teams” ) consisting of district controllers ( “Contrôleur Lèpre-TB-Buruli” , CLT ) , USP staff ( “Infirmière du Centre Peripherique” , ICP ) and community health workers ( “Agent de Santé Communautaire” , ASC ) , and village nurses were formed and trained by experienced PNLUB-LP , CHR , DITM and DAHW staff . The main tasks of the CLT teams are supervision of USPs , as well as sensitization and screening activities in the field which are mostly conducted under participation of DAHW and CHR staff and in collaboration with PNLUB-LP and the non-governmental organization Handicap International . In particular the ASCs who are trained and continuously supervised by the respective CLTs constitute an integral part of the outreach activities . They organize quarterly sensitization activities and present educational films and information material in villages within proven or assumed areas of endemicity . Villagers are instructed to report to their local ASCs in case of wounds or other lesions suspicious for BUD , thus ASCs represent the primary contact person for the population on community level . Furthermore , ASCs organize regular screening programs in village schools to identify suspected BUD cases in the field . The final decision on referral of suspected BUD cases to USPs or CHR for further diagnosis and treatment lies with a superordinate “BUD team” consisting of medical staff ( physician , nurse ) from CHR , ASCs , and the regional CLT . Visits to field sites are conducted on demand of district CLT teams according to a schedule elaborated by the ASCs . A routine reporting system between ASCs , ICPs , CLTs and CHR staff has been established and to facilitate communication within and between CLT teams and BUD teams a mobile phone network has been implemented by DAHW in 2010 . Data collection was conducted by means of the WHO “BU01” form [3] and standardized project specific laboratory data entry forms ( Form S1 ) . All clinical , epidemiological and laboratory data including EQA results were entered in a web-based database specifically designed for the “BuruliVac” project . Diagnostic samples were collected according to standardized procedures . Briefly , swabs were collected by circling the entire undermined edges of ulcerative lesions . Three millimeter punch biopsies and fine needle aspirates ( FNA ) were collected from the center of non-ulcerative lesions or from undermined edges of ulcerative lesions including necrotic tissue . To facilitate sampling , standardized specimen collection bags including swabs , biopsy punches , syringes and needles , slides , containers with transport media ( 700 µl [swab and punch biopsy samples] , 300 µl [FNA samples] CLS [cell lysis solution , Qiagen , Hilden , Germany] for PCR samples ) and data entry forms were provided to the study sites [13] , [23] , [25] , [26] , [29] , [37]–[41] . Samples for PCR analysis were transported in CLS at ambient temperature in an upright position in custom-made specimen collection bags from the field to INH by DAHWT cars within a maximum of 48 hours following sample collection . Upon arrival of PCR samples at INH these were stored at 4–8°C until further processing . Slides for microscopy were transported in slide boxes at ambient temperature to CHR and subsequently to INH . Direct smears for microscopy were prepared from swab and FNA samples at USPs or CHR and subjected to Ziehl-Neelsen staining at CHR . Slides were analyzed according to the WHO recommended grading system [42] . For PCR analysis DNA was prepared using the Gentra Puregene DNA extraction kit ( Qiagen , Hilden , Germany ) with minor modifications of the manufacturer's protocol [21] . Three IS2404 PCR formats ( dry-reagent-based [DRB] IS2404 PCR [INH] , standard gel-based IS2404 PCR and IS2404 qPCR [DITM] ) were applied in this study . Briefly , for DRB-PCR the oligonucleotides MU5 and MU6 were lyophilized in reaction tubes . Illustra PuReTaq Ready-To-Go PCR beads ( GE Healthcare , Munich , Germany ) were added and dissolved in water before adding template DNA [21] , [25] , [26] . Standard IS2404 PCR was performed according to the protocol described by Stinear et al . [15] , [17] . IS2404 qPCR was performed as recently described using a BioRad CFX96 real-time PCR detection system [27] , [43] . All PCR assays included negative extraction controls , positive , negative ( no template ) and inhibition controls . Implementation of diagnostic laboratory facilities at INH was accomplished in several phases . Before launching the national BUD reference laboratory at INH in January 2011 , laboratory assessment of diagnostic samples from “BuruliVac” study participants was conducted at CHR ( microscopy ) and DITM ( PCR ) respectively ( “initial phase” [phase I] from September 2010 through December 2010 ) . To implement standardized BUD microscopy and PCR services at INH , all required equipment , reagents and consumables were shipped to Togo by DAHWT and installed under supervision of DITM staff from November through December 2010 . Subsequently , the transitional phase ( phase II ) was initiated in January 2011 . All relevant laboratory procedures were defined in SOPs ( SOP S1–S4 ) . An initial laboratory training workshop was held by DITM staff , and INH staff was familiarized with the principles of standardized documentation of samples and corresponding results ( laboratory data entry forms , web-based database ) , the flow of information between the participating laboratories , and the principles of EQA as outlined below . Whereas during the transitional phase from January 2011 through April 2012 parallel diagnostic samples of all study participants were simultaneously subjected to PCR analysis at INH and DITM , the final phase ( phase III ) of PCR implementation ( ongoing since May 2012 ) provides for diagnostic PCR conducted independently at INH accompanied by EQA on DNA extracts at DITM . ( Figure 1 ) During the initial phase EQA was conducted for microscopy only . Slides were read at CHR by two readers , forwarded to DITM for blinded re-reading [13] , and both , CHR and DITM results were entered in the web-based database . In case of discordant results between CHR and DITM , slides were subjected to a second re-reading at DITM which determined the consensus result . During the transitional phase CHR conducted the first reading of slides by two readers , entered a consensus result in a specific result form ( Form S1 ) , and forwarded slides and forms to INH ( first controller ) for blinded re-reading . INH consensus results were also determined by two readers and entered in a specific result form ( Forms S2 ) . Finally , CHR and INH results were entered in the web-based database by INH data managers . In case of discordant results the respective slides were re-read by both , CHR and INH staff , and a consensus result was determined . Subsequently , slides were forwarded to DITM ( second controller ) for blinded re-reading , and DITM results were entered in the web-based database . Slides with discordant results between DITM and INH were re-read by DITM and INH staff during DITM supervisory visits . For EQA of PCR all clinical samples were collected in pairs and were simultaneously tested at INH ( DRB-PCR ) and DITM ( standard IS2404 PCR , confirmatory IS2404 qPCR on negative samples ) . Results were entered in the web-based database . In case of discordant results both laboratories repeated PCR analyses . If the result did not alter , DNA extracts of the respective samples were exchanged and re-tested at both laboratories . In accordance with a previous study on EQA for the laboratory diagnosis of BUD in Ghana [23] microscopy positivity rates ( i . e . number of positive samples divided by the total number of samples tested ) at CHR , INH , and DITM , PCR positivity rates at INH and DITM , rates of false negative and false positive results compared to DITM results and inter-laboratory concordance rates between CHR/INH/DITM for microscopy and INH/DITM for PCR were determined for the initial and transitional phases . In addition , case confirmation rates ( i . e . number of laboratory confirmed BUD patients divided by the total number of suspected BUD cases ) were determined for CHR ( microscopy ) , INH and DITM ( microscopy and PCR ) . To assess the impact of the local reference laboratory and continuous EQA measures on BUD control , the clinical parameters “type of lesion” , “category of lesion” , and “duration of disease before clinical diagnosis” ( i . e . the mean duration of disease in days based on the time from first recognition of clinical symptoms by patients and availability of the clinical diagnosis BUD ) were analyzed and data obtained from the current study cohort from January 2011 through April 2012 after implementation of the national reference laboratory were compared to data obtained in a previous study from September 2007 through December 2010 . INH forwards all laboratory results directly to CHR , the subsequent reporting chain includes regional CLTs , district CLTs , ICPs , and ASCs . Laboratory confirmed BUD patients are subjected to treatment . In case of negative laboratory results in general the treatment decision is referred to the BUD team . For the purpose of documentation , lesions of all confirmed patients are photographed; the material is available for training and sensitization activities . The study design was non-randomized and cross-sectional . Approximative tests ( χ2-tests ) including analysis for linear trends in proportions and t-tests as parametric test were conducted using Stata software , version 9 . 0 . ( Stata Corporation , College Station , TX ) and EpiInfo , version 3 . 3 . 2 . ( Centers for Disease Control and Prevention , Atlanta , GA ) . Significant differences were defined as not overlapping of 95 percent confidence intervals ( 95% CI ) of proportions .
Altogether 16 workshops with 559 participants ( “CLT teams” as well as other medical and paramedical staff ) addressing clinical picture , laboratory diagnosis and treatment of BUD were held in the regions “Maritime” and “Central” . Since 2011 , the CLT teams conducted sensitization activities in 1027 villages and screened a population of approximately 110 , 000 . Out of 192 persons with lesions suspicious for BUD identified in the field , 82 suspected BUD cases were finally referred to USPs or CHR . ( Table 1 ) During the initial phase , 17 slides ( swab , n = 6; FNA , n = 11 ) obtained from 16 suspected BUD cases ( ten non-ulcerative lesions: one FNA sample per lesion; six ulcerative lesions , one swab sample per lesion and one additional FNA sample from one lesion with scarred edges ) were analyzed at CHR and subjected to EQA at DITM . During the transitional phase , 72 slides ( swab , n = 24; FNA , n = 48 ) obtained from 66 suspected BUD cases ( 38 non-ulcerative lesions: one FNA sample per lesion; 28 ulcerative lesions: one swab sample each from 18 lesions , one swab and one FNA sample each from six lesions , one FNA sample each from four lesions ) were analyzed at CHR and subjected to EQA at INH and DITM . ( Table 2 ) During the initial phase positivity rates of microscopy were 41 . 2% ( 7/17 ) at CHR and 47 . 1% ( 8/17 ) at DITM with 5 . 9% ( 1/17 ) false negative results from CHR , and an inter-laboratory concordance rate of 94 . 1% ( 16/17 ) between CHR and DITM . During the transitional phase positivity rates of microscopy were 47 . 2% ( 34/72 ) at CHR , 48 . 6% ( 35/72 ) at INH and 55 . 6% ( 40/72 ) at DITM . The rate of false negative test results was 9 . 7% ( 7/72 ) at CHR and 6 . 9% ( 5/72 ) at INH , and 1 out of 72 slides ( 1 . 4% ) was read false positive at CHR . Concordance rates between laboratories were 94 . 4% ( 68/72 ) for CHR/INH , 88 . 9% ( 64/72 ) for CHR/DITM and 93 . 1% ( 67/72 ) for INH/DITM . The concordance rate between CHR and DITM for both phases was 89 . 9% ( 80/89 ) . ( Table 3 ) During the initial phase , 35 samples ( swab , n = 6; FNA , n = 16; punch biopsy , n = 13 ) obtained from 16 suspected BUD cases were subjected to standard PCR at DITM , all negative samples ( n = 12 ) were additionally subjected to qPCR . During the transitional phase , 99 sample pairs ( swab , n = 33; FNA , n = 44; punch biopsy , n = 22 ) obtained from 66 suspected BUD cases were subjected to PCR at INH and DITM , which equals a mean rate of 3 . 0 ( 198/66 ) samples tested per patient . All negative samples ( n = 30 ) were additionally subjected to qPCR . ( Table 4 ) During the initial phase the positivity rate of standard PCR at DITM was 65 . 7% ( 23/35 ) . Confirmation of two out of 12 negative samples by qPCR provided an additional diagnostic yield of 5 . 7% . During the transitional phase positivity rates of conventional PCR assays were 65 . 7% ( 65/99 ) at INH and 69 . 7% ( 69/99 ) at DITM . The rate of false negative test results at INH was 4 . 0% ( 4/99; 1 swab sample and 3 FNA samples ) , there were no false positive results , and the inter-laboratory concordance rate was 96 . 0% ( 95/99 ) . Confirmation of 6 out of 30 negative samples by qPCR provided an additional diagnostic yield of 6 . 1% . ( Table 5 ) The case confirmation rates for microscopy were 31 . 3% ( 5/16 ) at CHR and 37 . 5% ( 6/16 ) at DITM during the initial phase , and 43 . 9% ( 29/66 ) at CHR , 47 . 0% ( 31/66 ) at INH , and 53 . 0% ( 35/66 ) at DITM during the transitional phase . In total 50 . 0% ( 41/82 ) of the suspected BUD cases were confirmed by microscopy . ( Table 3 ) The case confirmation rates for PCR were 75 . 0% ( 12/16 ) at DITM during the initial phase , and 71 . 2% ( 47/66 ) at INH and 78 . 8% ( 52/66 ) at DITM ( including two cases additionally confirmed by qPCR ) during the transitional phase . In total 78 . 1% ( 64/82 ) of the suspected BUD cases were confirmed by PCR . ( Table 5 ) Out of 64 laboratory confirmed BUD patients , 51 . 6% ( 33/64 ) had non-ulcerative lesions ( plaque , n = 17; nodule , n = 10; papule , n = 1; edema , n = 5 ) and 48 . 4% ( 31/64 ) had ulcerative lesions , 48 . 4% ( 31/64 ) were male , and 48 . 4% ( 31/64 ) were in age group 5–14 years ( age range 2–68 years , mean 18 . 1 years , median 13 years ) . Figure 2 The confirmed BUD patients originated from four districts of region “Maritime” ( Yoto , n = 37; Zio , n = 22; Vo , n = 1; Golfe , n = 1 ) , two districts of region “Plateaux” ( Anié , n = 1; Ogou , n = 1 ) and one district of region “Savanes” ( Dapaong , n = 1 ) . The categories of lesions according to WHO classification [3] were as follows: 43 . 8% ( 28/64 ) category I , 40 . 6% ( 26/64 ) category II and 15 . 6% ( 10/64 ) category III . ( Table 1 ) All patients with suspected BUD ( n = 82 ) who presented in Togo during the study period were included ( no refusals to participate ) and clinical samples were collected and analyzed from all of them . All laboratory confirmed BUD patients ( n = 64 ) received a full course of treatment with rifampicin and streptomycin; in addition , six patients , despite negative laboratory results , were subjected to antimycobacterial treatment based on strong clinical suspicion of BUD . Although no regular outreach activities were conducted in region ”Plateaux” and ”Savanes“ patients from both regions were referred to CHR for treatment . The number of patients with non-ulcerative lesions among all PCR-confirmed patients increased significantly ( p<0 . 01 ) from 37 . 0% ( as determined for the study cohort from 2007–2010 , 119 patients ) to 50 . 0% ( current study cohort from January 2011 through April 2012 , 52 patients ) . Compared to the previous study category I lesions increased from 36 . 9% ( 95% CI: 28 . 3–45 . 6 ) to 44 . 2% ( 95% CI: 30 . 7–57 . 7 ) , category II lesions increased from 32 . 8% ( 95% CI: 24 . 3–41 . 2 ) to 36 . 6% ( 95% CI: 23 . 5–49 . 6 ) and category III lesions decreased from 30 . 3% ( 95% CI: 22 . 0–38 . 5 ) to 19 . 2% ( 95% CI: 8 . 5–29 . 9 ) . The mean duration of disease before clinical diagnosis decreased from 51 . 8 ( 95% CI: 19 . 0–84 . 7 ) to 35 . 0 ( 95% CI: 23 . 5–46 . 5 ) days ( no significant difference ) among patients with non-ulcerative lesions , and significantly from 182 . 6 [95% CI: 119 . 2–245 . 9] to 82 . 1 [95% CI: 51 . 3–112 . 8] days among patients with ulcerative lesions . ( Table 6 )
Laboratory confirmation of suspected BUD cases , in particular by molecular diagnostic tests , plays a crucial role for clinical management , disease control and research on M . ulcerans . To achieve the targeted PCR confirmation rate of more than 50% of suspected BUD cases worldwide , WHO has set up a network of external and local PCR reference laboratories [36] . Whereas until the early 2000s laboratory diagnostic services for endemic countries were mainly provided by external reference laboratories , until 2011 six African countries ( Ivory Coast , Ghana , Benin , Cameroon , Central African Republic , Democratic Republic of Congo ) installed their own reference laboratories upon increasing demand for local diagnostic capacity [6] , [10] , [11] , [18] , [20]–[26] , [29] , [30] , [32] , [37] , [44]–[46] . Due to the absence of laboratory facilities a number of countries still require support from external reference laboratories; in general however , the role of external reference laboratories has shifted to development of improved laboratory techniques for application in endemic countries , technical support and training of local laboratory staff , as well as external quality assurance for newly established reference laboratories [6] , [11] , [21] , [23]–[32] , [37]–[40] , [43] . As well known from other studies , the implementation of reference level laboratory facilities necessitates multiple provisions in terms of logistics , trained personnel and quality management [11] , [23] , [47] , [48] . In the case of Togo , extensive preparatory work conducted in the context of previous research projects by DAHWT and DITM [13] , vast expertise gained from a longstanding cooperation with partners in Ghana [21] , [23] , [25] , [26] , [29] , [40] , as well as continuous exchange of information with other “BuruliVac” partners [6] , [32] facilitated the implementation of a national reference laboratory considerably . Excellent technical skills of INH laboratory staff in conventional and molecular microbiological diagnostic techniques allowed starting laboratory training at an advanced level . All training activities took place at INH; basic laboratory training according to the concept of short-term “training of trainers” workshops in Europe as successfully applied by other external reference laboratories was not required . In consideration of the existing quality management systems at DITM and INH , special emphasis was given to standardization of all relevant procedures . SOPs defined the interaction of the laboratory with external partners in the field and the external reference laboratory in Germany , as well as all processes within the laboratory , and granted a smooth workflow from the beginning of the project . Standardized documentation of all analyses and results in standardized laboratory forms and the project-specific web-based database facilitated rapid retracing of errors for local and external reference laboratory and allowed targeted training measures . To measure the quality of diagnostics conducted at INH , we determined concordance rates between local and external reference laboratories . Compared to a previous study [13] , the concordance rate for microscopic analysis between CHR and DITM ( initial and transitional phase ) increased from less than 70% to 90% , and the concordance rate between INH and DITM was over 90% during the transitional phase , suggesting a high standard of microscopy at both , CHR and INH . Compared to previous findings [13] , also the case confirmation rate for microscopy increased from 30% ( CHR ) to 43% ( CHR ) and 47% ( INH ) , respectively . Likewise , concordance rates between INH and DITM for PCR of swab and punch biopsy samples were over 95% . In this study , instead of testing the same sample subsequently at both laboratories , sample pairs were collected and one sample each was sent to DITM and INH to allow quality control for both , extraction efficiency and amplification . As already observed in other studies , parallel samples – even if collected from the same site of the lesion - may show an inhomogeneous distribution of mycobacteria and may increase the normal inter-laboratory variation regularly observed for weakly positive samples ( [23] , [49] , unpublished data ) . Therefore , the findings suggest high quality of PCR conducted at INH . With 93% the inter-laboratory concordance rate for FNA samples was slightly lower which may be attributable to dividing FNA samples in two pieces for microscopy and PCR at INH ( whereas the entire parallel sample was subjected to PCR at DITM ) . Consequently , also the case confirmation rate at INH was a little lower ( 71% ) than at DITM ( 76% ) . Future EQA of PCR diagnostics is conducted on DNA extracts only , therefore both confounders ( sample pairs and divided samples ) are excluded . In addition to conventional gel-based PCR , DITM applied IS2404 qPCR on negative samples which resulted in laboratory confirmation of two additional cases . As real-time PCR facilities are available at INH , implementation of IS2404 qPCR is envisaged for 2013 . Laboratories in endemic countries without access to real-time PCR may consider forwarding at least samples from patients with strong clinical suspicion but negative conventional PCR result to an external reference laboratory for confirmatory IS2404 qPCR . The study also attempted to measure the impact of local laboratory capacity and quality management on BUD control . The increase of the rate of non-ulcerative lesions by 13% , the significant reduction of the diagnostic delay by more than 100 days for patients with ulcerative lesions as compared to a previous study [13] and the reduction of category III lesions from 30 . 3% to 19 . 2% may be attributed to an extended quality management system also comprising patient related procedures outside the laboratory and intensified training measures . Already during the previous study period from 2007 through 2010 CLTs , ICPs , ASCs and other field staff had been trained in 28 workshops with 152 participants . Since 2011 , however , training measures achieved a roughly five-fold increase in coverage , and training of teams instead of individuals resulted in a multiplier effect in terms of knowledge transfer which became noticeable also in areas without regular outreach activities through referral of patients to CHR . The availability of trained CLT teams in 11 districts , in particular the ASCs , increased the coverage of sensitization activities and allowed to conduct extensive “information , education and communication” ( IEC ) campaigns under the guidance of DAHWT and PNLUB-LP in regions “Maritime” and “Central” accompanied by regular outreach activities to identify suspected BUD cases in the field . Finally , supervision of CLT teams by the CHR BUD team in terms of re-examining these patients provided continuous on-site training for CLT teams and enhanced the diagnostic skills of all field staff involved . Feed- back of laboratory results through a newly established reporting chain from INH to community level not only provides the basis for targeted case finding activities in the environment of confirmed patients , but is also conceived as confidence-building measure by ASCs as well as patients and their families . Altogether , the outreach system implemented in 2011 allowed to realize key components of BUD control in the field of early case detection , diagnosis and treatment as defined by the WHO [7] , and more than 90% of BUD cases are currently detected through active case finding ( opposed to roughly 60% in the previous study ) . Whereas these outreach activities resulted in a constant flow of diagnostic samples from suspected BUD cases from peripheral health facilities in region “Maritime” via the regional hospital ( CHR ) to INH , and the first cases from region “Plateaux” and “Savanes” have been identified , to date no cases from region “Central” have been confirmed . Since June 2012 , a cooperation agreement between the “Faculté Mixte de Médécine et de Pharmacie” of the University of Lomé , Togo and the Faculty of Medicine of the Ludwig-Maximilians-University , Munich , Germany , has reinforced the existing diagnostic network through initiation of a collaboration with the “Laboratoire de Biologie Moléculaire et d'Immunologie” ( BIOLIM ) , “Département des Sciences Fondamentales et Biologiques” . BIOLIM will support ongoing EQA measures in the field of quality control , academic and in-service training of local laboratory staff , thus contribute to maintaining sustainable standards in laboratory confirmation of BUD . Furthermore , access to a nationwide laboratory network established in the context of research on HIV and other infectious diseases conducted by BIOLIM will enable operational research on decentralised diagnostics and increase the efficiency of BUD control . [7] , [48] , [50]
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Buruli ulcer disease ( BUD ) , the third most common mycobacterial disease worldwide , is treated with standardized antimycobacterial therapy . According to WHO recommendations at least 50% of cases should be laboratory confirmed by polymerase chain reaction ( PCR ) . In a previous study PCR analysis of clinical samples from suspected BUD cases from Togo and external quality assurance ( EQA ) for local microscopy were conducted at an external reference laboratory in Germany . The relatively poor performance of local microscopy as well as time and effort associated with shipment of clinical samples abroad necessitated the availability of a local BUD reference laboratory and the implementation of stringent EQA measures . All processes in the laboratories as well as in the field were defined by standard operating procedures , microscopy conducted at regional facilities was subjected to EQA at national and external reference level , and PCR samples were analyzed in parallel at national and external reference laboratories . Inter-laboratory concordance rates of >90% and case confirmation rates of 50% ( microscopy ) and >70% ( PCR ) respectively suggest high standards of BUD diagnostics . Furthermore , an increase of non-ulcerative lesions and a decrease in diagnostic delay and category III lesions reflect the impact of comprehensive EQA measures also involving procedures outside the laboratory on the quality of BUD control .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"bacterial",
"diseases",
"infectious",
"diseases",
"buruli",
"ulcer",
"neglected",
"tropical",
"diseases"
] |
2013
|
Implementation of a National Reference Laboratory for Buruli Ulcer Disease in Togo
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Previous analyses of sera from a limited number of Ghanaian Buruli ulcer ( BU ) patients , their household contacts , individuals living in BU non-endemic regions as well as European controls have indicated that antibody responses to the M . ulcerans 18 kDa small heat shock protein ( shsp ) reflect exposure to this pathogen . Here , we have investigated to what extent inhabitants of regions in Ghana regarded as non-endemic for BU develop anti-18 kDa shsp antibody titers . For this purpose we determined anti-18 kDa shsp IgG titers in sera collected from healthy inhabitants of the BU endemic Densu River Valley and the Volta Region , which was so far regarded as BU non-endemic . Significantly more sera from the Densu River Valley contained anti-18 kDa shsp IgG ( 32% versus 12% , respectively ) . However , some sera from the Volta Region also showed high titers . When interviewing these sero-responders , it was revealed that the person with the highest titer had a chronic wound , which was clinically diagnosed and laboratory reconfirmed as active BU . After identification of this BU index case , further BU cases were clinically diagnosed by the Volta Region local health authorities and laboratory reconfirmed . Interestingly , there was neither a difference in sero-prevalence nor in IS2404 PCR positivity of environmental samples between BU endemic and non-endemic communities located in the Densu River Valley . These data indicate that the intensity of exposure to M . ulcerans in endemic and non-endemic communities along the Densu River is comparable and that currently unknown host and/or pathogen factors may determine how frequently exposure is leading to clinical disease . While even high serum titers of anti-18 kDa shsp IgG do not indicate active disease , sero-epidemiological studies can be used to identify new BU endemic areas .
Buruli ulcer ( BU ) , a severe necrotizing skin disease , is caused by the environmental pathogen Mycobacterium ulcerans ( M . ulcerans ) . Globally , it is the third most prevalent mycobacterial disease that affects immunocompetent individuals after tuberculosis and leprosy [1] . Currently more than 30 countries , mainly in the Tropics and sub-Tropics , are known to report BU cases [2] . The main countries that are severely affected lie along the Gulf of Guinea and include Ivory-Coast , Ghana , Togo , Benin and Cameroon . In the highly endemic countries BU is second after tuberculosis as the most prevalent mycobacterial disease [2] , [3] . However , the global burden of BU is not clear , because efficient and comprehensive reporting systems are lacking in many of the BU endemic countries . One characteristic of BU is its focal distribution within highly endemic countries . Most cases occur in remote villages with limited access to the formal health sector , prompting affected people to seek health at traditional healers [4] . Even today , not all affected communities may be known to the National BU Control Programs . Therefore reliable tools to detect and monitor the presence of BU in communities are urgently needed . The disease presentation , which varies between individuals , starts either as a papule , nodule , plaque or edema and if these non-ulcerative early forms are not treated , extensive tissue destruction leads to the formation of large ulcerative lesions with characteristic undermined borders . Extensive tissue destruction frequently causes disfigurement and long lasting deformities such as loss of limbs and essential organs , like the eye [5] , [6] . Many features of BU such as the mode of M . ulcerans transmission and risk factors for an infection with the pathogen are not clearly understood . However , BU is known to occur mainly in children less than 15 years of age and affects people in wetlands and disturbed environments [3] , [7] . The pathology of BU is primarily associated with the secretion of the cytocidal and immunosuppressive polyketide toxin mycolactone [8] . Current methods for a laboratory confirmation of clinical BU diagnosis include microscopic detection of acid fast bacilli ( AFB ) , culture of M . ulcerans , histopathology and detection of M . ulcerans DNA by PCR . Currently , PCR detection of the M . ulcerans specific insertion sequence IS2404 is the gold standard for BU diagnosis [9] . Yet , PCR requires elaborate infrastructure and expertise and therefore make it out of reach for primary health care facilities in BU endemic low resource countries . Serology represents a more attractive approach for the development of a simple test format that can be applied to facilities treating BU in low resourced countries . Unfortunately , various studies have shown that serological tests targeting M . ulcerans antigens are not suitable to differentiate between patients and exposed but healthy individuals as both groups may exhibit serum IgG titers against these antigens [10] , [11] . However , serology may be a useful tool for monitoring exposure of populations to M . ulcerans , although great antigenic cross reactivity between M . ulcerans , M . tuberculosis , BCG and other environmental mycobacteria complicates this approach . We previously profiled an immunodominant 18 kDa small heat shock protein ( shsp ) absent from M . tuberculosis and M . bovis as a suitable target antigen for sero-epidemiological studies . In spite of the presence of sequence homologues in M . leprae and M . avium , Western blot analyses , using a limited number of sera indicated that this protein can be used to distinguish between M . ulcerans exposed and non-exposed populations [10] . Here we have extended these studies with larger sets of sera . These sero-epidemiological studies identified a BU index case in a region of Ghana that was regarded , so far , as BU non-endemic .
Ethical clearance for the study was obtained from the institutional review board of the Noguchi Memorial Institute for Medical Research ( Federal-wide Assurance number FWA00001824 ) . Written informed consent was obtained from all individuals involved in the study . Parents or guardians provided written consent on behalf of all child participants . One part of this study was conducted in five districts of the Eastern Region including East-Akim ( EA ) , New-Juaben ( NJ ) , Suhum-Kraboa-Coaltar ( SHC ) , Akwapim South ( AS ) and Akwapim North ( AN ) as well as two districts of the Greater-Accra Region comprising Ga-West ( GW ) and Ga-South ( GS ) . While EA and NJ report no BU cases and AN only occasionally , the remaining four districts have communities that report BU regularly to the NBUCP . GW reports the highest number of cases with an annual average number of 100 new cases , followed by AS , GS and the SKC . This study focused on selected communities within these districts , which are all located along the Densu River . The other part of this study was carried out in three communities of the Volta Region , namely Torgorme , Gblornu and Kasa . These communities are situated along the banks of River Volta in the North Tongu district of the Volta Region , which was so far regarded as BU non-endemic . Torgorme , Gblornu and Kasa have an estimated population of about a 1700 , 350 and 160 , respectively . Initial community entry was done by first meeting community opinion leaders , which included the disease control officer responsible for the area , the assembly man and chiefs in order to explain the importance of the activity and to solicit their cooperation . A rough sketch and count of houses along the length and breadth of the community was carried out by walking through the community in order to estimate an approximate number of houses to be surveyed . The area was then divided into two blocks , with one research team being responsible for one block . Each habitable structure within a block was then numbered serially . A house to house survey was carried out and interviews involving the head of a house were done . A data collection chart was used to collect information on the number of people in the house , healed and active BU cases and if active cases were found , samples were collected for confirmation of BU . Collected data of the BU patients included age and sex , when the disease was contracted and GPS coordinates of their houses . Two milliliters of blood were collected into vacutainer tubes ( BD ) from participants of ten different villages within a 5 km radius along the Densu River; six and four of the communities were confirmed as BU endemic and non-endemic , respectively , using active search and mapping activities as described above . The endemic communities were: Kojo-Ashong and Otuaplem in the GW , Kwame Anum and Ayitey Kortor in the GS , Sakyikrom and Tetteh Kofi in the AS district , respectively . The non-endemic communities were Obuotumpan and Abotanso in the NJ and Abesim Yeboah and Ntabea in the EA district . The study participants aged between 5 and 90 years recruited from these communities were individuals with no history of BU . 188 participants were from the non-endemic villages ( 94 each females and males ) ; age range 5–84 years , arithmetic mean of 28 . 6 years , median 19 years and mode of 15 . 294 participants were from endemic villages ( 139 and 155 were male and females , respectively ) ; age range 5–90 years , mean age of 26 . 8 years , median 20 and mode 12 years . In addition , whole blood samples were also collected from 99 community members in three villages along the Volta River in the Volta Region which has so far been considered one of the non-endemic regions in Ghana . The three communities Torgorme , Gblornu and Kasa were selected as having never reported leprosy in the past five years according to data of the North Tongu District Directorate of Health Services . Blood samples were transported immediately at ambient temperature to the laboratory for separation of serum by centrifugation at 2 , 000 g for 10 mins to remove the clot . Sera were stored at −80°C until analysis . 25 µg of recombinant M . ulcerans 18 kDa shsp protein was separated on NuPAGE® Novex 4–12% Bis-Tris ZOOM™ Gels , 1 . 0 mm IPG well ( Invitrogen ) using NuPAGE ® MES SDS Running Buffer ( Invitrogen ) under reducing conditions and transferred to nitrocellulose membranes . Membranes were blocked with 5% skim milk in phosphate-buffered saline ( PBS ) , 0 . 1%Tween 20 ( PBS-T ) and cut into strips . Protein strips were incubated with serum samples at a 1∶500 dilution in PBS-T for 1 . 5 hrs . Strips were washed with 0 . 3 M PBS , 1% Tween 20 and incubated with alkaline phosphatase-conjugated AffiniPure F ( ab′ ) 2 fragment goat anti-human immunoglobulin G ( IgG , Milian ) . Nitro blue tetrazolium ( NBT ) and 5-bromo-4-chloro-3-indolyl phosphate ( BCIP ) ( BioRad ) were used for color development . 96-well Nunc-Immuno Maxisorp plates ( Thermo Scientific ) were coated with 0 . 5 µg recombinant 18 kDa shsp per well in 100 µl PBS . Plates were incubated at 4°C overnight . Plates were washed with dH2O , 2 . 5% Tween 20 ( dH2O-T ) and blocked for 1 h with 200 µl blocking buffer ( 5% skim milk in PBS ) at 37°C . Serial 2-fold dilutions of serum from 1∶100 to 1∶12800 in 50 µl blocking buffer per well were incubated for 1 . 5 hrs at 37°C . The wells were washed with dH2O-T . 50 µl of 1∶6000 diluted goat anti-human IgG ( γ-chain specific ) coupled to horseradish Peroxidase ( HRP , SouthernBiotech ) was added to each well and incubated for 1 h at room temperature . After the last washing step with dH2O-T , 100 µl TMB Microwell Peroxidase Substrate ( KPL ) was added . The reaction was stopped after 5 min . The absorbance was measured using an ELISA plate reader ( Sunrise , Tecan ) at 450 nm . Each ELISA plate contained two-fold dilutions of a negative control comprising a pool of 5 negative sera from people living in BU non-endemic communities in Ghana and a positive control consisting of 5 medium positive sera from people living in BU endemic areas . The cut-off value for positivity was considered to be the mean optical density ( OD ) of negative and positive control at a 1∶100 serum pool dilution . Statistically , data were analyzed using GraphPad Prism version 5 . 0 ( GraphPad Software , San Diego California USA ) . The nonparametric Kruskal-Wallis test with Dunn's post-test was used to compare OD values for the different groups . Sampling was done from aquatic environments and from communities . Water , insects , fish , snails , dominant vegetation ( both dead and living ) and soil were collected randomly from the ground and edges of rivers at various locations in both endemic and non-endemic communities . Soil , vegetation and animal droppings were collected from various locations within both endemic and non-endemic communities . All collected samples were transported on the same day to the laboratory , stored at 4°C and analyzed within a week of collection . DNA was extracted from about 200 mg portions of all the environmental samples using the FastDNA Spin kit for soil ( MP Biomedical ) according to the manufacturer's instruction . For insect samples additionally glass beads were added to the lysing matrix and the breaking step with the Fast Prep instrument was substituted by heating specimens at 95°C for twenty minutes followed by vortexing full speed for two minutes . The extracted DNA was stored at −20°C until analysis by real-time PCR . TaqMan real-time PCR was performed using primers and procedures as previously described with some modifications in reaction conditions [12] . The primers and TaqMan MGB probes detecting IS2404 , IS2606 and the ketoreductase ( KR ) domain were obtained from Applied Biosystems ( Foster City , CA , USA ) . IS2404 real-time PCR mixtures contained 1X Qiagen master mix ( containing HotstarTaq plus DNA polymerase , dNTP mix and PCR buffer ) 1 µl of extracted template DNA , 0 . 5 µM concentrations of each primer and 0 . 2 µM probe , 1× TaqMan exogenous internal positive control ( IPC ) and probe reagents ( Applied Biosystems ) , in a total volume of 20 µl . Amplification and detection were performed with the Rotor-Gene Q ( Qiagen ) using the following program: 1 cycle of 95°C for 5 min , 40 cycles of 95°C for 15 s and 60°C for 15 s . Each PCR run contained 2 non-template controls and an IS2404 positive control . Analysis for IS2606 and KR was in a multiplex PCR using KR and IS2606 probes with FAM and VIC fluorescence labels respectively and reaction conditions as above . Initial BU survey results were entered in Microsoft Access and exported for integration using Quantum Geographic Information System ( GIS ) for analyses . Google Earth aerial images of communities were obtained , geo-referenced and linked to ground contours , features and other characteristics . The prevalence of BU was calculated by counting all individuals in the community with a classical BU scar , together with those with laboratory confirmed active disease , divided by the total number of persons examined within a community . The rate was expressed as a percentage .
We determined M . ulcerans 18 kDa shsp-specific serum IgG titers in 482 sera from people living in the BU endemic Densu River Valley in the Gar and Eastern Region , 99 sera from people living in the BU non-endemic Volta Region and 20 sera from European controls without travel history to Africa ( figure 1A ) . Based on the defined ELISA OD cut-off values , a sero-positivity rate of 32% was observed for the sera from the Densu River Valley . The sero-positivity rate of people living in the Volta Region ( 12% ) , as well as the mean ELISA readouts obtained with their sera were significantly lower ( p<0 . 001 ) . None of the sera from European controls exhibited a significant titer ( figure 1A ) . Sero-positive individuals from the Volta region were re-visited and interviewed . It was determined that all of them have lived entirely or at least for most of their life in their home communities in the Volta Region . One of the sero-positive participants from the village Torgorme reported at the interview to have a non-healing chronic wound on the leg ( figure 1B ) . The wound was clinically diagnosed by an experienced physician as BU and clinical diagnosis was laboratory reconfirmed by positive IS2404 PCR of swab specimens . The serum of this reconfirmed BU patient had the highest anti-18 kDa shsp-specific serum IgG titer of all participants from the Volta Region tested ( figure 1A ) . Following the identification of this index case , the health directorate of the Volta Region sent us specimens from eleven other individuals with suspected BU lesions . Six of these , were reconfirmed as IS2404 PCR-positive BU by our laboratory at the Noguchi Memorial Institute for Medical Research , which is one of the BU reference laboratories in Ghana . While sera of two laboratory confirmed BU patients contained anti-18 kDa shsp IgG , four patients were sero-negative . Active case search surveys were performed to determine the prevalence of BU along the Densu River ( figure 2 ) . The average prevalence of BU in endemic communities with 3 km buffer was 3 . 4% . While in some communities upstream no BU cases were found , the disease burden increases as the River runs downstream ( figure 2A ) . Of the ten communities included in the sero-epidemiological study , four ( Ntabea , Abesim-Yeboah , Obotanso and Obuotupan ) were confirmed as non-endemic , as both the passive surveillance by the National BU control program and our active case search identified neither healed nor active cases . The total prevalence rate , including both healed and active cases , of the six endemic communities ranged from 1% to 19% with Tetteh Kofi , Otuaplem and Sode having the highest rates ( 4 . 8% , 14 . 9% and 19 . 1% , respectively ) . The prevalence of active cases ranged from <1% to 2 . 4% , with Sode also having the highest active case prevalence rate . When 18 kDa shsp-specific serum IgG titers of 295 sera from BU endemic and of 187 sera from non-endemic communities were analyzed by ELISA ( figure 1A ) , comparable sero-positivity rates ( 33% versus 31% , respectively ) were found . ELISA results were reconfirmed by Western blot analysis with a randomly chosen subset of sera . There was good agreement between Western blot band intensities and ELISA titers with a few discrepancies related to a higher sensitivity of the ELISA method ( data not shown ) . Sero-responders were found in all age groups ( >5 years ) tested , but sero-negative individuals dominated throughout ( figure 3 ) . 211 environmental samples were collected randomly from both aquatic and dry land environs . The sampled BU endemic communities included Kojo-Ashong ( KA ) , Sode ( SD ) , Amasaman and surrounding hamlets ( AS ) , and Kudeha and surrounding hamlets ( KD ) located in the GW and GS districts . Samples from non-endemic communities were collected in Abesim-Yeboah ( AY ) , Obuotumpan ( OB ) and Ntabea ( NB ) located in the EA and NJ districts further up-stream of the Densu River ( figure 2A ) . M . ulcerans DNA in an environmental sample was confirmed by the presence of all three tested loci ( IS2404 , IS2606 and KR ) as revealed by positive results with all three PCR tests performed . In all , 19/211 ( 9 . 0% ) of the samples tested were positive , including 5/19 aquatic snails , 5/28 sand samples collected from the communities , 4/30 samples from river water and river bed sand , 2/30 samples from aquatic vegetation , 1/6 sand samples collected from farms , 1/12 aquatic insects and 1/1 millipedes . As shown in Table 1 the average positivity rates for samples from endemic communities were 13 . 4% ( 7 . 7% , 13 . 3% , 14 . 3% and 18 . 5% in KA , AS , SD and KD , respectively ) and 6 . 2% for samples from non-endemic communities ( 26 . 0% , 2% and 1 . 8% for NB , OB and AY , respectively ) .
Broad antigenic cross-reactivity between mycobacterial species represents a major challenge for the development of a serological test that is specific and sensitive enough to monitor immune responses against M . ulcerans in populations where exposure to M . tuberculosis and BCG vaccination is common . In our earlier work , we have identified the M . ulcerans 18 kDa shsp as an immunodominant antigen , which has no homologues in M . tuberculosis and M . bovis [10] . However , interspecies cross-reactivity of this protein with an 18 kDa protein of M . leprae as well as a 20 kDa protein of M . chelonae was detected . In the same study we evaluated the use of measuring anti-18 kDa shsp IgG titers for assessing the exposure of a population to M . ulcerans on the basis of a limited number of BU patients , household contacts and people living in areas where BU is not endemic [10] . Since sera from inhabitants of BU non-endemic regions showed largely no reactivity with the 18 kDa protein of M . ulcerans , immune responses against environmental mycobacteria , such as M . chelonae , do not seem to compromise the developed serological test for M . ulcerans exposure . Here we have extended our previous analysis by comparing sera from areas of Ghana , which rarely report leprosy cases , but differ in their reported BU endemicity . In Ghana , a national case search performed in 1999 yielded a crude national BU prevalence rate of 20 . 7/100 , 000 and hence demonstrated that BU is the second most common mycobacteriosis in the country after tuberculosis [13] . In this study diagnosis of both active and healed lesions was based solely on clinical grounds without any microbiological confirmation . Since the creation of the national control program , 32 of the 166 nation-wide districts continuously report BU . Through this passive surveillance system , over 11 , 000 cases have been reported between 1993 and 2006 ( http://www . who . int/mediacentre/factsheets/fs199/en/ ) from mainly six of the ten regions of Ghana . No BU cases have been reported from the Volta , Northern , Upper East and West regions , giving the impression that those four regions do not harbor BU cases and therefore are non-endemic . However , in our analysis of sera from the Volta Region , a relatively small , but significant number of serum samples contained anti-18 kDa shsp IgG . Follow-up visits and interviews revealed that one of the sero-positive individuals had a chronic wound which was subsequently laboratory confirmed as BU [14] . After identification of this index case , additional laboratory confirmed BU cases were found by active case search in the Volta Region . In our previous analyses [10] , only part of the sera from laboratory reconfirmed BU patients were tested postitive for anti-18 kDa shsp IgG . In accordance with these findings , not all of the BU patient sera from the Volta region were sero-positive . These data clearly show that anti-18 kDa shsp IgG titers are no indication for active disease . A large epidemiological survey is now required to determine the prevalence of BU over the entire Volta Region . Until today no serological test allows for a distinction of BU patients and healthy individuals , which are exposed to M . ulcerans . However , our results demonstrate that sero-epidemiological studies can be used to complement active case search in regions , where data about the BU prevalence are lacking . Future longitudinal sero-epidemiological studies are planned in order to monitor the exposure of certain populations to M . ulcerans over a longer period of time . At this stage we cannot conclude how timing and frequency of exposure influences antibody titers against the pathogen . The prevalence of 18 kDa shsp sero-positive individuals within populations along the Densu River was >30% . This confirms our earlier conclusion that a large proportion of healthy individuals living in endemic communities who have responded immunologically to M . ulcerans exposure do not develop overt disease . While the percentage of 18 kDa shsp sero-positive individuals was higher compared to that found using a Burulin skin test in healthy controls , it is comparable to that obtained for the serologic response to M . ulcerans culture filtrate [15] , [16] . Diverse outcome of infection with the causative agents of the main mycobacterial diseases such as tuberculosis seems to be a common feature of their natural history . Not all exposed individuals show immunological evidence of infection and of those who get infected by M . tuberculosis estimations indicate that only 10% will ever develop overt disease [17] , [18] . Manifestation of the disease ranges from self-limited pulmonary infection to localized extra-pulmonary infection and disseminated disease [19] . Factors accounting for the diversity in outcomes are not entirely known , but may relate to both host and pathogen factors . Even though clinical M . ulcerans isolates from Africa are clonally related and genetically largely monomorphic [20]–[27] , differences in virulence among African M . ulcerans strains cannot be ruled out completely . Hence , the percentage of M . ulcerans infected individuals who proceed to develop BU remains to be established . BU is known to develop in all age groups with a nearly equal gender distribution but most cases occur in children 15 years of age or younger [28] . In our study we found anti-18 kDa shsp sero-responders in all age groups ( >5 years ) analyzed . A future cohort study with infants could provide important insight , at which age these immune responses start to emerge . Both in endemic and non-endemic villages of the Densu River Valley we found M . ulcerans PCR positive environmental samples . This is indicative for the presence of M . ulcerans or of closely related environmental bacteria all along the Densu River . Our findings are consistent with earlier findings of Williamson et al . [29] in the same region . Since the mode of M . ulcerans transmission and risk factors for the exposure to the pathogen are still not entirely elucidated , it is not clear , whether the types of environmental samples that were PCR positive have direct relevance for infection with M . ulcerans . Hence methods for the routine isolation and characterization of M . ulcerans from the environment need to be developed . Hypotheses on risk factors and the mode of infection with M . ulcerans include contamination of wounds from an environmental reservoir , inhalation of vaporized contaminated water and inoculation by insects [30]–[32] . Our molecular epidemiological studies have recently demonstrated a focal transmission pattern for M . ulcerans [27] . This may help to explain one of the mysteries of BU transmission , the close proximity of endemic and non-endemic villages . As indicated in figure 2 , while M . ulcerans is endemic in some villages within the Suhum-Kraboa-Coaltar district , through active case search we did not find any case ( both healed and active ) in neighboring districts located at the upper part of the river , such as East-Akim and the New-Juaben . In contrast , communities of the four districts , which are situated downstream ( Akwapim South , Akwapim North , Ga-West and Ga-South ) regularly report BU cases . BU endemic and non-endemic communities along the Densu river differ in terms of their vegetation . Upstream , within the wet semi-equatorial zone , the vegetation is predominantly moist semi-deciduous rain forest , which gradually changes downstream into a short stretch of Guinea Savannah around Nsawam and ends with coastal scrub and savannah grassland in the Ga districts . In addition , there is a variation in the features of the Densu River , which takes its source from the Atewa Forest Range near Kibi and flows for 116 km into the Weija Water Reservoir before entering the Gulf of Guinea through the Densu Delta Ramsar site . While upstream the river flows fast , has clear water and the river bed consists of rocky stones , downstream the river flows sluggishly , has a muddy river bed , and the water is turbid . We did not find significant differences in anti-18 kDa shsp IgG seropositivity rate or titers between people living in communities in the Densu River Valley that were classified based on active case search as BU endemic or non-endemic . These findings could imply at least one of the following: 1 ) people in the non-endemic communities in the upper Densu River Valley may be exposed to M . ulcerans lineages with low virulence; 2 ) currently unknown host genetic , behavioral or socio-economic factors trigger the development of subclinical M . ulcerans infection to clinical disease; 3 ) in the non-endemic communities 18 kDa shsp binding antibodies are triggered by subclinical infections with environmental mycobacteria harboring antigens that are cross-reactive with the 18 kDa shsp [10] .
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Sero-epidemiological analyses revealed that a higher proportion of sera from individuals living in the Buruli ulcer ( BU ) endemic Densu River Valley of Ghana contain Mycobacterium ulcerans 18 kDa small heat shock protein ( shsp ) -specific IgG than sera from inhabitants of the Volta Region , which was regarded so far as BU non-endemic . However , follow-up studies in the Volta Region showed that the individual with the highest anti-18 kDa shsp-specific serum IgG titer of all participants from the Volta Region had a BU lesion . Identification of more BU patients in the Volta Region by subsequent active case search demonstrated that sero-epidemiology can help identify low endemicity areas . Endemic and non-endemic communities along the Densu River Valley differed neither in sero-prevalence nor in positivity of environmental samples in PCR targeting M . ulcerans genomic and plasmid DNA sequences . A lower risk of developing M . ulcerans disease in the non-endemic communities may either be related to host factors or a lower virulence of local M . ulcerans strains .
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[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"immunology",
"biology",
"microbiology",
"population",
"biology"
] |
2012
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Sero-Epidemiology as a Tool to Screen Populations for Exposure to Mycobacterium ulcerans
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Talaromyces marneffei is an opportunistic dimorphic fungus prevalent in Southeast Asia . We previously demonstrated that Mp1p is an immunogenic surface and secretory mannoprotein of T . marneffei . Since Mp1p is a surface protein that can generate protective immunity , we hypothesized that Mp1p and/or its homologs are virulence factors . We examined the pathogenic roles of Mp1p and its homologs in a mouse model . All mice died 21 and 30 days after challenge with wild-type T . marneffei PM1 and MP1 complemented mutant respectively . None of the mice died 60 days after challenge with MP1 knockout mutant ( P<0 . 0001 ) . Seventy percent of mice died 60 days after challenge with MP1 knockdown mutant ( P<0 . 0001 ) . All mice died after challenge with MPLP1 to MPLP13 knockdown mutants , suggesting that only Mp1p plays a significant role in virulence . The mean fungal loads of PM1 and MP1 complemented mutant in the liver , lung , kidney and spleen were significantly higher than those of the MP1 knockout mutant . Similarly , the mean load of PM1 in the liver , lung and spleen were significantly higher than that of the MP1 knockdown mutant . Histopathological studies showed an abundance of yeast in the kidney , spleen , liver and lung with more marked hepatic and splenic necrosis in mice challenged with PM1 compared to MP1 knockout and MP1 knockdown mutants . Likewise , a higher abundance of yeast was observed in the liver and spleen of mice challenged with MP1 complemented mutant compared to MP1 knockout mutant . PM1 and MP1 complemented mutant survived significantly better than MP1 knockout mutant in macrophages at 48 hours ( P<0 . 01 ) post-infection . The mean fungal counts of Pichia pastoris GS115-MP1 in the liver ( P<0 . 001 ) and spleen ( P<0 . 05 ) of mice were significantly higher than those of GS115 at 24 hours post-challenge . Mp1p is a key virulence factor of T . marneffei . Mp1p mediates virulence by improving the survival of T . marneffei in macrophages .
Talaromyces ( Penicillium ) marneffei is an opportunistic thermal dimorphic fungus most prevalent in Southeast Asia [1–6] . Bamboo rats and the soil of their burrows are known to be important sources of T . marneffei . Since the 1980s , a marked increase in the number of infections caused by T . marneffei has been observed , primarily as a result of the HIV pandemic . In addition to tuberculosis and cryptococcosis , T . marneffei infection is one the most important indicator of AIDS in our locality . However , in recent years , there has been a surge in the number of T . marneffei infections in HIV-negative patients owing to the use of a variety of immunosuppressive therapies and also due to the increased recognition of underlying immunodeficiency syndromes [3 , 7–11] . The first line defense of the human body against T . marneffei infection is achieved mainly through tissue macrophages; however , the mechanisms by which T . marneffei evades host defense is not well understood [12 , 13] . In 1998 , we described the cloning of a cell surface and abundantly secreted immunogenic mannoprotein , Mp1p , in T . marneffei [14] . Mp1p is a 462-amino acid protein with two homologous domains , which are named as lipid binding domain 1 ( Mp1p-LBD1 ) and lipid binding domain 2 ( Mp1p-LBD2 ) . We demonstrated that Mp1p based enzyme-linked immunosorbent assays ( ELISAs ) can be used for antigen and antibody detection in patients with T . marneffei infections and showed that Mp1p has the ability to generate protective immunity in mice [15–17] . Through analysis of the genome sequence of T . marneffei , we observed the presence of 13 Mp1p homologs in its genome [18] . Moreover , the amino acid sequence of Mp1p in different strains of T . marneffei was found to be highly variable , especially in Mp1p-LBD1; and by using Mp1p and four additional Mp1p homologs , we constructed a multilocus sequence typing scheme for T . marneffei [19] . Recently , we have solved the X-ray crystallographic structure of Mp1p-LBD2 , the relatively more conserved LBD of Mp1p , and have shown that it is able to bind palmitic acid [20] . Since Mp1p is a surface protein that can generate protective immunity , we hypothesize that Mp1p and/or its homologs are virulence factors of T . marneffei . To test this hypothesis , we systematically knocked down MP1 and its 13 homologs in T . marneffei and examined their roles in virulence in a mouse model . We demonstrated that Mp1p , but not its homologs , is a key virulence factor of T . marneffei and its virulence is achieved by improving the survival of T . marneffei in macrophages .
The experimental protocols were approved by the Committee on the Use of Live Animals in Teaching and Research , The University of Hong Kong , in accordance with the Guidelines laid down by the NIH in the USA regarding the care and use of animals for experimental procedures . All fungal strains are summarized in Table 1 . T . marneffei PM1 and the genetically- modified derivatives of PM1 were grown on Sabouraud dextrose agar ( SDA ) ( Oxoid ) , while Pichia pastoris GS115 and its derivatives were grown on yeast extract peptone dextrose agar ( Sigma ) . Mp1p homologs in the T . marneffei genome were identified using TBLASTN searches with Mp1p as query [21] . Phylogenetic relationships of Mp1p homologs [MpLp1 ( Mp1p-Like protein 1 ) to MpLp13] and Mp1p were determined using maximum likelihood method with Mega 5 [22] . DNA extraction and plasmid construction were performed as previously described [23–25] . Expression vector pSilent-1 , which can express the short hairpin RNAs ( shRNA ) against target gene , was used to construct pKD-MP1 and pKD-MPLP1 to 13 for MP1 homolog knockdown . Firstly , the internal gene fragments ( sense ) were amplified using primers LPW9895 , LPW9896 , LPW11195 , LPW11196 , LPW11199 , LPW11200 , LPW11203 , LPW11204 , LPW11207 , LPW11208 , LPW11211 , LPW11212 , LPW11215 , LPW11216 , LPW11219 , LPW11220 , LPW11223 , LPW11224 , LPW11227 , LPW11228 , LPW11231 , LPW11232 , LPW11235 , LPW11236 , LPW11239 , LPW11240 , LPW11243 and LPW11244 ( S1 Table ) ( Invitrogen ) . The PCR mixture ( 25 μl ) contained T . marneffei DNA , PCR buffer ( 10 mM Tris-HCl pH 8 . 3 , 50 mM KCl , 2 mM MgCl2 and 0 . 01% gelatin ) , 200 μM of each dNTPs and 1 . 0 U Taq polymerase ( Applied Biosystem ) . The mixtures were amplified in 32 cycles of 95°C for 30 seconds , 56°C for 30 seconds and 72°C for 40 seconds , and a final extension at 72°C for 10 minutes ( Applied Biosystem ) . The PCR products were purified using the QIAquick Gel Extraction kit ( Qiagen ) , digested with XhoI and HindIII , and cloned into the XhoI-HindIII site of the pSilent-1 plasmid , resulting in pKD-MP1-1 and pKD-MPLP1-1 to pKD-MPLP13-1 . Second , the internal gene fragments ( antisense ) were amplified with primers LPW 9897 , LPW10358 , LPW11197 , LPW11198 , LPW11201 , LPW11202 , LPW11205 , LPW11206 , LPW11209 , LPW11210 , LPW11213 , LPW11214 , LPW11217 , LPW11218 , LPW11221 , LPW11222 , LPW11225 , LPW11226 , LPW11229 , LPW11230 , LPW11233 , LPW11234 , LPW11237 , LPW11238 , LPW11241 , LPW11242 , LPW11245 and LPW11246 ( S1 Table ) , using the PCR conditions described above . Amplified fragments were purified as described above , digested with BglII and KpnI , and cloned into the BglII-KpnI sites of pKD-MP1-1 and pKD-MPLP1-1 to pKD-MPLP13-1 respectively , resulting in pKD-MP1 and pKD-MPLP1 to 13 . pKD-MP1 and pKD-MPLP1 to 13 were linearized using EcoICRI and transformed into PM1 respectively . Transformation of T . marneffei was achieved by heat shock using the yeast form of T . marneffei . T . marneffei yeast cells obtained from cultures grown on SDA at 37°C for 10 days were used to inoculate 50 ml yeast extract peptone dextrose ( YPD ) broth in a 250 ml conical flask with shaking in a gyratory shaker and were further incubated at 37°C with shaking at 200 rpm for 24 hours . T . marneffei yeast cells were harvested by centrifugation at 2 , 500 rpm for 5 minutes at 4°C and then washed with TE buffer ( 10mM Tris-HCl pH7 . 5 , 1mM EDTA ) and Li-TE buffer ( 0 . 1 M lithium acetate in TE pH7 . 5 ) . T . marneffei yeast cells were resuspended in 200 μl Li-TE buffer and 50 μl of yeast cells were used in each reaction . Three hundred microliters of 40 weight/volume percent ( w/v % ) freshly prepared polyethylene glycol ( PEG ) 4000 ( Sigma ) , 5 μl of 10 mg/ml single-stranded sheared salmon sperm DNA ( Invitrogen ) , and 1–2 μg linearized plasmid were sequentially added and mixed with T . marneffei yeast cells and the reactions were subsequently incubated at 30°C for 30 minutes and then at 42°C for 40 minutes . After the heat shock process , yeast cells were collected by three short spins at room temperature and the yeast pellets were resuspended in 10 ml of YPD broth and incubated at 37°C with shaking at 200 rpm for 24 hours . T . marneffei transformants were plated onto SDA containing 150 μg/ml hygromycin B ( Invitrogen ) and incubated at 37°C for 10–14 days for selecting the knockdown strains MP1 knockdown mutant and MPLP1-13 knockdown mutant . The RNA of the respective transformants was extracted , reverse transcribed , and checked by real-time quantitative RT-PCR ( qRT-PCR ) . The relative gene expression levels of each knockdown mutant compared to PM1 were calculated using 2-ΔΔCT method [26] . For deletion of MP1 , pKO-MP1 was generated using a homologous recombination method as previously described [27] . Two DNA fragments , comprising the 1313-bp upstream and the 1406-bp downstream flanking sequences of MP1 , were generated by PCR using LPW2558/2559 and LPW2560/2561 respectively ( S1 Table ) . The PCR products of upstream/downstream flanking fragments were ligated into BglII/HindIII sites of vector pAN7-1 that harbored the hygromycin B resistance gene to generate plasmid pKO-MP1 , which was then linearized with AspEI and used for transformation . Hygromycin-resistant colonies were screened for homologous recombination by amplification of two fragments which harbored partial genomic sequence , MP1 upstream/downstream fragment and vector sequence using primers LPW2815/LPW2575 and LPW392/LPW2816 , whereas one set of gene-specific primers ( LPW2562/LPW2772 ) was used to confirm successful target gene knockout ( S1 Table ) . Western blot analysis was performed as previously described [15] . Twenty micrograms of protein from the cell lysates of T . marneffei was loaded onto a sodium dodecyl sulfate–10% polyacrylamide gel and the proteins were subsequently onto a nitrocellulose membrane ( Bio-Rad ) . The blot was incubated with 1:1000 dilution of guinea pig anti-Mp1p antibodies , followed by 1:4000 dilution of goat anti-guinea pig IgG ( H+L ) secondary antibody conjugated with horse radish peroxidase ( HRP ) . Antigen-antibody interaction was then detected with an enhanced chemiluminescence fluorescence system ( GE healthcare ) . ELISA was performed as previously described [28] . Briefly , microwell plates ( Corning ) were coated with 100 μl/well of Mp1p monoclonal antibodies by incubation overnight at 4°C followed by incubation with a blocking reagent containing 2 . 5 g casein sodium salt , 1 . 21 g Tris-base , 2 g gelatin , 20 g sucrose , 0 . 2 g merthiolate , and 5 ml Tween 20 in 1000 ml dH2O ( Sigma ) . The blocking solution was then removed and 100 μl of culture filtrates of wild type or mutant T . marneffei was serially diluted in 1:10 in 0 . 1% bovine serum albumin and incubated at 37°C for 1 hour . After the plates were washed , biotinylated monoclonal antibody ( 100 μl/well ) was added and the plates were incubated for 30 minutes at 26°C . Following incubation with streptavidin-HRP ( Sigma ) , 3 , 3′ , 5 , 5′-tetramethylbenzidine substrate was added . The reaction was stopped after 10 minutes by addition of 0 . 3 N sulfuric acid , and the plates were examined in an ELISA plate reader ( Bio-Tek ) at 450 nm . Southern blot analysis was performed as previously described [29] . For MP1-knockout mutant , homologous recombination at the desired locus was confirmed by Southern blot analysis of SpeI-digested genomic DNA probed with a 625-bp PCR product , generated by primers LPW5140/5141 ( S1 Table ) , located at the 5’ upstream flanking region of MP1 . Deletion of MP1 was further confirmed by Southern blot analysis with a 680-bp PCR product , generated by primers LPW5142/2772 ( S1 Table ) , which targeted nucleotides 191–650 of the MP1 gene . To examine whether the virulence properties of Mp1p can be restored in MP1 knockout mutant , the MP1 gene was complemented in the MP1 knockout mutant . Plasmid pAN8-1 was used to construct pAN8-1MP1 for MP1 complementation . The promoter region of A . nidulans gpd gene and the terminator region of the A . nidulans trpC gene were ligated to the 5’ and 3’ ends of MP1 gene respectively . The MP1 fragment containing promoter and terminator was cloned into NarI and NdeI sites of vector pAN8-1 that harbored the Streptococcus hindustanus phleomycin resistance gene using primers LPW19020/ LPW18915 to give plasmid pAN8-1MP1 ( S1 Table ) . The pAN8-1-MP1 was linearized with PciI and used for transformation . T . marneffei strain MP1 knockout mutant was transformed with linearized pAN8-1MP1 , using 100 μg/ml phleomycin ( Invivogen ) for selection , generating MP1 complemented mutant . Successful complementation of MP1 gene and Mp1p production were confirmed by PCR , Western blot and ELISA respectively . P . pastoris GS115 expressing Mp1p was generated using the Multi-Copy Pichia Expression Kit ( Invitrogen ) . The coding region of MP1 was amplified using primers in S1 Table , digested with EcoRI and XhoI and cloned into the EcoRI-XhoI sites of pPIC9K ( Invitrogen ) to generate pPIC9K-MP1 . The plasmid was first transformed and propagated in Escherichia coli BL21 ( DE3 ) , followed by transformation into GS115 to generate GS115-MP1 . Mp1p expression was induced with buffered methanol complex medium at 30°C with shaking at 300 rpm for 24 hours and expression was confirmed by western blot . Total RNA was extracted using RiboPure-Yeast ( Ambion ) . Extracted RNA was eluted in 70 μl of RNase-free water and then used as the template for real-time qRT-PCR . Reverse transcription was performed using the SuperScript III kit ( Invitrogen ) . Real-time qRT-PCR was performed as described previously [30] , with primers as listed in S1 Table and using actin primers LPW20631/LPW20160 for normalization . cDNA was amplified in a LightCycler 2 . 0 ( Roche ) with 20 μl reaction mixtures containing FastStart DNA Master SYBR Green I Mix reagent kit ( Roche ) , 2 μl cDNA , 2 mM MgCl2 and 0 . 5 mM primers at 95°C for 10 minutes followed by 50 cycles of 95°C for 10 seconds , 57°C ( 55°C for actin gene ) for 5 seconds and 72°C for 23 seconds ( 36 seconds for actin gene ) . All experiments were performed in triplicates . Balb/c ( H-2d ) mice ( 6-8-week-old , 18–22 g ) were housed under standard conditions as described previously [23 , 31] . Ten mice each were challenged intravenously with 8×106 spores of PM1 , MP1 knockout mutant , MP1 complemented mutant , MP1 knockdown mutant and the MPLP1-13 knockdown mutants; and 1×107 spores of GS115 and GS115-MP1 according to viable counts . Mice survival was recorded daily for 60 days . All experiments were performed in triplicates . Five mice from the four groups challenged with PM1 , MP1 knockout mutant , MP1 complemented mutant and MP1 knockdown mutant were sacrificed on day 12 post-challenge . Five mice from the two groups challenged with GS115 and GS115-MP1 were sacrificed at 24 hours post-challenge . One half of each organ was homogenized in 1× PBS for colony counts , and the other half fixed in 10% neutral buffered formalin and embedded in paraffin . Paraffin-embedded sections were stained with hematoxylin & eosin ( H&E ) , Grocott’s methenamine silver ( GMS ) or Periodic acid-Schiff ( PAS ) . Murine macrophage-like cell line J774 ( ATCC no . TIB-67 ) was maintained in Dulbecco's Modified Eagle's Medium ( DMEM ) ( Gibco ) supplemented with 10% heat-inactivated fetal bovine serum ( Gibco ) in 5% CO2 at 37°C in 75 cm2 tissue culture flask ( Cellstar ) . J774 macrophages were differentiated by treatment with 0 . 32 μM phorbol myristate acetate ( PMA ) for 72 hours prior to the antifungal assay [23] . PMA-differentiated J774 cells were seeded in duplicates in a 24-well plate at 1×105 cells/well in complete medium . Spores of T . marneffei strains PM1 , MP1 knockout mutant , MP1 complemented mutant and MP1 knockdown mutant were harvested and inoculated into J774 cells at 2×105 spores/well ( multiplicity of infection of 2 ) and incubated at 37°C in 5% CO2 incubator for 2 hours for phagocytosis . After phagocytosis , cell monolayers were washed sequentially with 240 U/ml nystatin ( Sigma ) and warm PBS to remove non-phagocytized Spores and maintained in DMEM supplemented with 1 μg/ml of lipopolysaccharides from E . coli serotype O111:B4 ( Sigma ) and 400 U/ml of recombinant mouse interferon-γ ( R&D System ) and further incubated for 48 hours . J774 cells were then harvested and lysed with 1% ( w/v ) Triton X-100 ( Sigma ) for colony forming unit ( CFU ) count at 2 hours , 8 hours , 16 hours , 24 hours and 48 hours post-inoculation . Macrophages lysed with 1% Triton X100 which consisted of the phagocytized yeasts were plated in serial dilutions in duplicate in SDA and incubated for 5 days at 37°C . The results were expressed as mean CFU ± standard deviations from three different experiments . Means between groups were compared with Student’s t-test . Survival of mice was tested by Kaplan-Meier method and Log-rank test .
Using TBLASTN searches and the amino acid sequence of Mp1p as query , we observed 13 additional open reading frames in the T . marneffei ( strain PM1 ) draft genome ( AGCC00000000 ) [18] that encodes for putative homologs of Mp1p [MpLp1 to MpLp13 ( Table 2 and Fig 1 ) ] . Unlike Mp1p which possesses two LBDs ( Mp1p-LBD1 and Mp1p-LBD2 ) , MpLp1 to MpLp13 have only one LBD each . Phylogenetically , Mp1p-LBD1 and Mp1p-LBD2 were clustered with high bootstrap support ( Fig 2 ) , suggesting that they are results of duplication of the Mp1p-LBD ancestor during its evolution in T . marneffei . Similar to Mp1p , most of these Mp1p homologs also contain putative signal peptides , variable numbers of putative N-glycosylation and O-glycosylation sites and glycosylphosphatidylinositol ( GPI ) anchors , and they are all expressed in both the yeast and mold phases of T . marneffei ( S1 Fig ) . We challenged Balb/c mice intravenously with spores of wild-type T . marneffei strain PM1 , MP1 knockout mutant , MP1 complemented mutant , MP1 knockdown mutant , and knockdown mutants of each of the 13 Mp1p homologs ( MPLP1 to MPLP13 knockdown mutant ) , respectively . Site-specific knockout of MP1 was confirmed by PCR , Southern blot , western blot and ELISA ( S1 Table and S2–S5 Figs ) . Complementation of MP1 was confirmed by PCR , western blot and ELISA ( S1 Table and S3–S5 Figs ) . Knockdown of MP1 and its homologs MPLP1 to MPLP13 were confirmed by the corresponding real-time qRT-PCR . All mice died 21 and 30 days after being challenged with PM1 and MP1 complemented mutant respectively ( Fig 3A ) . None of the mice died 60 days after challenge with MP1 knockout mutant ( P<0 . 0001 ) . Seventy percent of mice died 60 days after challenge with MP1 knockdown mutant ( P<0 . 0001 ) , showing a dose-response effect . All mice died after challenge with MPLP1 to MPLP13 knockdown mutant , suggesting that only Mp1p played a significant role in virulence . At day 12 post-challenge , five mice from each of the PM1 , MP1 knockout mutant , MP1 complemented mutant and MP1 knockdown mutant groups were sacrificed for fungal counts and histopathological studies . The mean fungal loads of PM1 and MP1 complemented mutant in the liver , lung , kidney and spleen were significantly higher than those of MP1 knockout mutant and those of PM1 in the liver , lung and spleen were significantly higher than those of MP1 knockdown mutant ( Fig 3B ) . In the liver , the mean fungal loads of PM1 were >10-fold higher than those of MP1 knockdown mutant and >100-fold higher than those of MP1 knockout mutant . Histopathological studies showed a higher abundance of yeast in the kidney , spleen , liver and lung with more marked hepatic and splenic necrosis in mice challenge with PM1 compared to MP1 knockout mutant and MP1 knockdown mutant ( Fig 3C ) . It also showed an abundance of yeast in the liver and spleen of mice challenged with MP1 complemented mutant compared to MP1 knockout mutant ( Fig 3C ) . To examine whether Mp1p can improve the intracellular survival of T . marneffei in murine macrophages , we measured the survival of PM1 , MP1 knockout mutant , MP1 complemented mutant and MP1 knockdown mutant in murine macrophages . PM1 and MP1 complemented mutant survived significantly better than MP1 knockout mutant at 48 hours ( P<0 . 01 ) post-infection ( Fig 4 ) , suggesting that Mp1p mediates virulence by improving the survival of T . marneffei in macrophages , the primary defensive mechanism against the fungus . To determine if Mp1p can improve the survival of P . pastoris in mice , we cloned MP1 into expression plasmid pPIC9K and transformed into P . pastoris GS115 ( GS115-MP1 ) and challenged Balb/c mice with GS115 and GS115-MP1 respectively ( Table 1 ) . The mean fungal counts of GS115-MP1 in the liver ( P<0 . 001 ) and spleen ( P<0 . 05 ) of mice were significantly higher than those of GS115 at 24 hours post-challenge , indicating a gain-of-function ( Fig 5 ) .
In this study , we documented that Mp1p is a novel and key virulence factor of T . marneffei . In the literature , six genes ( sodA , cpeA , hsp70 , alb1 , pks11 and pks12 ) have been suggested to encode potential virulence factors of T . marneffei ( superoxide dismutase , catalase-peroxidase , heat shock protein 70 and polyketide synthases for their biosynthetic pathways ) [23 , 31–34] . Among these six genes , only those encoding the polyketide synthases for the biosynthesis of melanin , mitorubrinic acid and mitorubrinol , which we characterized recently , were shown to have virulence properties in an animal model . However , knocking down of the alb1 ( for melanin biosynthesis ) , pks11 ( for mitorubrinic acid biosynthesis ) or pks12 ( for mitorubrinol biosynthesis ) gene could only rescue 10–20% of the mice , suggesting that these are not major virulence factors of T . marneffei [23 , 31] . As for sodA , cpeA , and hsp70 , studies have demonstrated that the expression of their transcripts in T . marneffei was higher during macrophage infection , oxidative stress or mycelium to yeast phase transition , although these proteins have also been implicated as virulence factors in other fungi [32–34] . In the present study , a T . marneffei strain isolated from an HIV-negative patient with the typical clinical features and with genome sequence available was used [18] . Results showed that after knocking out of MP1 in T . marneffei , all mice survived ( Fig 3A ) . With partial knocking down of MP1 , a significant proportion of mice survived ( Fig 3A ) . Moreover , the virulence properties of T . marneffei were restored by complementation of the MP1 gene in its knockout strain . The deaths of the mice were a result of invasion of T . marneffei , as demonstrated by higher fungal counts with massive necrosis in the internal organs of mice challenged with wild-type T . marneffei as compared to both the MP1 knockout mutant and knockdown mutants ( Fig 3B and 3C ) . Further direct evidence to show that Mp1p is a bona fide virulence factor was shown by the cloning of MP1 into P . pastoris enhanced survival of the fungus in mice was observed , indicating a gain-of-function ( Fig 5 ) . The molecular mechanism of virulence for Mp1p remains to be determined . At the cellular level , Mp1p improved the survival of T . marneffei in macrophages ( Fig 4 ) , the key defensive cells against the fungus . Although we have shown previously that Mp1p is able to bind palmitic acid [20] , this does not seem to provide a direct clue to the molecular mechanism of virulence , as in vitro binding of a protein to other proteins , lipids or other molecules is not uncommon and may not have physiological roles . Since palmitic acid is a fatty acid , further experiments to examine the capability of Mp1p to bind other fatty acids as well as site-directed mutagenesis experiments to look for mutants that affect both the binding activities and virulence properties of T . marneffei will help shed light on the mechanism of virulence of Mp1p . It is noteworthy that the T . marneffei genome contains 13 Mp1p homologs in addition to Mp1p . Similar to Mp1p , these 13 Mp1p homologs are also expressed in significant amounts in both the mold and yeast phases of the fungus ( S1 Fig ) . Overall , their LBDs possessed 21–40% and 25–43% amino acid identities to those of Mp1p-LBD1 and Mp1p-LBD2 respectively and most of their LBDs are comparable in size to Mp1p ( Table 2 ) . Moreover , some of these homologs possess N-glycosylation sites , ST rich regions and GPI anchor , which are regions that are also found in Mp1p ( Table 2 ) . Interestingly , in contrast to Mp1p which is a strong virulence factor of T . marneffei , the other 13 Mp1p homologs present in the T . marneffei genome do not contribute significantly to virulence as demonstrated by the mice challenge experiments using the corresponding knockdown mutants ( Fig 3 ) . Further studies are required to determine the reason for the differential virulence properties of Mp1p and its homologs . The virulence property of Mp1p may also be present in Mp1p homologs found in other fungi . Phylogenetic analysis of the mitochondrial genomes of T . marneffei and other fungi showed that T . marneffei is closely related to the Aspergillus species [25] , which are highly virulent molds that cause high fatalities in patients with hematological malignancies , transplant recipients , HIV positive patients and patients on corticosteroid therapy [35 , 36] . We previously showed that A . fumigatus and A . flavus both possess Mp1p homologs ( Afmp1p and Afmp2p in A . fumigatus and Aflmp1p in A . flavus ) and these homologous proteins in A . fumigatus and A . flavus are also immunogenic proteins which can be used for serological diagnosis in the corresponding fungi [37–41] . Since the LBDs of these proteins are homologous to Mp1p ( Fig 2 ) , we speculate that they may also help the corresponding Aspergillus species to evade host immunity . Further experiments will reveal the virulence spectrum of Mp1p homologs in different fungal pathogens .
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Talaromyces ( Penicillium ) marneffei is an opportunistic thermal dimorphic fungus most prevalent in Southeast Asia . Our team has previously shown that Mp1p , a protein encoded by the MP1 gene , is an immunogenic surface and secretory protein of T . marneffei . In this study , we showed that mice challenged with T . marneffei with the MP1 gene died but those challenged with T . marneffei without the MP1 gene did not die . There was also significantly higher fungal load and more necrosis in organs of mice challenged with T . marneffei with the MP1 gene than T . marneffei without the MP1 gene . Furthermore , T . marneffei with the MP1 gene survived better in macrophages than T . marneffei without the MP1 gene and Pichia pastoris with the MP1 gene survived in mice better than P . pastoris without the MP1 gene . Our data support that Mp1p is a key virulence factor of T . marneffei and Mp1p mediates virulence by improving the survival of T . marneffei in macrophages .
|
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2016
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Mp1p Is a Virulence Factor in Talaromyces (Penicillium) marneffei
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A ubiquitous building block of signaling pathways is a cycle of covalent modification ( e . g . , phosphorylation and dephosphorylation in MAPK cascades ) . Our paper explores the kind of information processing and filtering that can be accomplished by this simple biochemical circuit . Signaling cycles are particularly known for exhibiting a highly sigmoidal ( ultrasensitive ) input–output characteristic in a certain steady-state regime . Here , we systematically study the cycle's steady-state behavior and its response to time-varying stimuli . We demonstrate that the cycle can actually operate in four different regimes , each with its specific input–output characteristics . These results are obtained using the total quasi–steady-state approximation , which is more generally valid than the typically used Michaelis-Menten approximation for enzymatic reactions . We invoke experimental data that suggest the possibility of signaling cycles operating in one of the new regimes . We then consider the cycle's dynamic behavior , which has so far been relatively neglected . We demonstrate that the intrinsic architecture of the cycles makes them act—in all four regimes—as tunable low-pass filters , filtering out high-frequency fluctuations or noise in signals and environmental cues . Moreover , the cutoff frequency can be adjusted by the cell . Numerical simulations show that our analytical results hold well even for noise of large amplitude . We suggest that noise filtering and tunability make signaling cycles versatile components of more elaborate cell-signaling pathways .
Cells rely on chemical interactions to sense , transmit , and process time-varying signals originating in their environment . Because of the inherent stochasticity of chemical reactions , the signals transmitted in cell-signaling pathways are buried in noise . How can cells then differentiate true signals from noise ? We examine this in the context of a basic but ubiquitous module in signaling cascades: the signaling cycle . Whereas an individual signaling cycle is simply an element of a large signaling network , understanding its response is an essential first step in characterizing the response of more-elaborate signaling networks to an external stimulus [1 , 2] . Each cycle consists of a substrate protein that can be in one of two states: active ( e . g . , phosphorylated ) or inactive ( e . g . , dephosphorylated ) , see Figure 1 . The protein is activated by a protein kinase that catalyzes a phosphorylation reaction . The protein gets inactivated by a second enzymatic reaction catalyzed by a phosphatase . The activity/concentration of the kinase can be considered as an input of the cycle . The response of the cycle is the level of phosphorylated substrate protein that is not bound to the phosphatase and can thus interact with any downstream components of the signaling pathway . Signaling cycles can also require multiple phosphorylations for activation . Furthermore , cycles of phosphorylation are frequently organized into cascades in which the activated substrate protein serves as a kinase for the next cycle . Activation of the first kinase in a cascade can be triggered by a receptor that has received a specific stimulus ( ligand , photon , dimerization , etc . ) . In addition , feedback processes may be present . Furthermore , reactions may involve shuttling participating molecules between different cellular compartments , and other spatial effects . The dynamics of signaling cascades have been the subject of active research using modeling and experiments . Theoretical and computational studies of eukaryotic signaling cascades span a broad range of questions , such as those concerning the dynamics of the epidermal growth factor receptor ( EGFR ) [3] or apoptosis signaling pathways [4] , the propagation of noise and stochastic fluctuations [5–7] , the role of feedback [8–11] and scaffolding proteins [12 , 13] , the contribution of receptor trafficking [14] and spatial effects [10 , 15 , 16] , the origin of bistability [17–19] and oscillations [6 , 20 , 21] , and the consequences of multiple phosphorylations [6 , 20–27] . In this paper , our focus will be on the statics and dynamics of the basic , singly modified signaling cycle , with no spatial effects . The seminal contribution of Goldbeter and Koshland considered the steady-state response of this basic cycle and demonstrated that , under appropriate conditions , the response can be in a highly sigmoidal , ultrasensitive regime , or in a hyperbolic regime [28] ( see below ) . Most modeling studies have assumed that all signaling cycles operate in the ultrasensitive regime; a few studies have also considered the hyperbolic regime [29 , 30] . Here , we demonstrate that there are actually four major regimes , with the ultrasensitive and hyperbolic regimes being two of them . Several previous studies that treat signaling cycles as modules have focused on the steady-state response to a constant input , largely ignoring responses to time-varying stimuli ( see , e . g . , [23 , 28 , 31] ) . A study of Detwiler et al . [29] considered the dynamic response of the cycle in the hyperbolic regime ( when both forward and backward reactions are first-order ) , and found low-pass filtering behavior . We also recently examined the dynamic response of these two regimes and compared them in their robustness to intrinsic and extrinsic noise [32] . Here , we systematically consider both the steady-state response and the dynamic response to time-varying stimuli . To model the enzymatic reactions in the signaling cycle , we use the total quasi–steady-state approximation ( tQSSA ) [33] . The tQSSA is valid more generally than the Michaelis-Menten ( MM ) rate law , which assumes the enzyme to be present in much smaller concentration than its substrate , an assumption that is not generally valid in signaling pathways . We then use our model to examine possible regimes of the cycle , and to identify two new steady-state regimes , for a total of four different behaviors , each being potentially useful in different signaling applications . Although these four regimes are defined at extreme parameter values , we numerically show that , in fact , together they cover almost the full parameter space . We obtain analytic approximations to the steady-state characteristics of each of the four regimes , and refine the conditions under which the two regimes identified by Goldbeter and Koshland are in fact achieved . To obtain a fuller picture of the signaling cycle and its function , we then analyze its response to time-varying kinase activity . We demonstrate analytically that the intrinsic architecture of the cycles makes them act—in all four regimes—as tunable low-pass filters for small-enough time-varying deviations of the kinase activity from baseline levels . Numerical simulations show that these analytical results continue to hold quite well even for bigger deviations from baseline level . The four different regimes of the signaling cycle make it a versatile element , able to perform various signaling functions , while its low-pass filtering enables it to operate in noisy environments . These properties may help explain why signaling cycles are so ubiquitous in cell signaling .
The signaling cycle is modeled by two enzymatic reactions , as illustrated in Figure 1: a forward enzymatic reaction catalyzed by kinases ( enzyme 1 , E1 ) produces active proteins ( A ) from the inactive ones ( I ) , and a backward reaction catalyzed by phosphatases ( enzyme 2 , E2 ) deactivates active proteins: Here , a1 ( d1 ) and a2 ( d2 ) are substrate–enzyme association ( dissociation ) rates , and k1 ( k2 ) is the catalytic rate of the forward ( backward ) enzymatic reaction . For notational convenience , we shall use the same symbol to denote a chemical species as well as its concentration . The input to the cycle is the total concentration of the active kinase , , whereas the output is the concentration of the free ( i . e . , not bound to phosphatase ) active protein A . Although such systems are usually studied using Briggs-Haldane or MM approximations ( see [34 , 35] ) , both can be inapplicable because they assume much lower concentration of the enzyme than of the substrate . In fact , substrates and enzymes of MAPK pathways are usually present at comparable concentrations in Saccharomyces cerevisiae and Xenopus oocyte cells ( as reported in [31] and consistent with data from the library of green fluorescent protein [GFP]-tagged proteins [36] ) . Instead , we rely on the total quasi–steady-state approximation ( tQSSA ) [33 , 37–39] ( see Methods ) to obtain the following equation for the concentration of the total active protein , : Here X denotes the concentration of an unbound chemical species and denotes the total concentration of bound and unbound forms; stands for the total concentration of substrate protein ( in both active and inactive forms ) ; and and are the MM constants for the kinase and the phosphatase , respectively . We have written ( t ) explicitly with its time argument t to emphasize that it is a dynamic variable; however , for notational simplicity , we will omit the time argument in the rest of the paper and simply write . The quantities , and are constant here ( although later in the paper , we consider the dynamic response to small variations in ) . Even though the above equation is written in terms of , the free active protein concentration A , which is of primary interest , is simply recovered through the expression ( see Text S1 ) . Equation 3 shows the dependence of the rate of production of the active protein on the number of kinases through the first term ( phosphorylation ) , and on the number of phosphatases through the second term ( dephosphorylation ) . In particular , when the total amounts of both kinase and phosphatase are small ( and ) , the two terms in Equation 3 reduce to the standard MM rates for the forward and backward enzymatic reactions of the cycle . The tQSSA has also been recently proposed and applied by Ciliberto et al . in [40] to model networks of coupled enzymatic reactions , including interconnections of phosphorylation cycles; their reduced tQSSA representation accurately reproduces behavior predicted by detailed mass action kinetics ( MAK ) models . Our key equation ( Equation 3 ) simplifies for extreme combinations of parameter values ( i . e . , regimes ) that are still of potential biological interest . This equation allows us to analytically examine ( 1 ) the possible cycle regimes of the system in steady state , and ( 2 ) the dynamic response of the system to time-varying inputs ( time-varying activation of the kinase ) . The numerical results we present here are not constrained by the quality of the approximation since they are based on direct simulation of the MAK equation for the full system of reactions of Equations 1 and 2 ( see Methods ) . Each enzymatic reaction can be in one of two qualitatively different regimes: a saturated one in which almost all the enzyme is bound to its substrate , and an unsaturated one [41 , 43] . The regime of the reaction depends on the relative concentrations of a substrate and the enzyme ( E ) , and on the MM constant ( K ) of the enzymatic reaction . The unsaturated ( first-order ) regime , in which the rate of reaction is linearly proportional to the substrate concentration , occurs when the substrate is much less abundant than the sum of the MM constant of the reaction and the enzyme concentration ( e . g . , for the second reaction , ) . In the saturated ( zero-order ) regime , the rate of reaction is almost independent of the substrate concentration and is proportional to the enzyme concentration . This occurs when the substrate is much more abundant than the sum of enzyme concentration and its MM constant ( e . g . , for the second reaction , ) . Since the signaling cycle is built of two enzymatic reactions , it can exhibit four regimes of signaling ( see Figure 2 ) , corresponding to the two regimes of each reaction . The conditions for each of the four regimes are summarized in Table 1 . The steady-state behavior of two of the four regimes ( when the kinase and the phosphatase are either both saturated or both unsaturated , referred to as ultrasensitive and hyperbolic , respectively ) has been characterized earlier by Goldbeter and Koshland [28] . Using tQSSA , we are able to refine the range of parameter values for which these behaviors hold . The other two regimes have not been identified before , to the best of our knowledge . Signaling cascades in the cell are activated by receptors , which in turn get activated by ligand binding and inactivated by internalization and other mechanisms . All of these mechanisms produce time-varying signals , and are subjected to noise ( i . e . , rapid and stochastic fluctuations ) due to small numbers of molecules , diffusion , and other effects . How can a cell extract a time-varying signal from noisy stimuli ?
Significant effort has been put in the elucidation and characterization of signaling cascades and pathways ( see , e . g . , [2 , 16 , 44 , 45] for reviews ) . When put together , these pathways form an intricate network of cell signaling , where each node in the network corresponds to a different chemical species . Because of the complexity of the network , it is natural to split it into interconnected modules ( sets of nodes whose output depends only on its input and not on the network downstream of it ) and analyze possible behaviors arising from different interconnections of modules ( see , e . g . , [30 , 46 , 47] ) . What constitutes a module in the network , however , is still hard to define , and significant efforts are directed at tackling this problem ( e . g . , [48–52] ) . What constitutes a good general representation for an arbitrary module in the network is also an open question . Other efforts have been aimed at understanding properties of the network as a whole , such as identifying the number of equilibrium states ( e . g . , [53 , 54] ) . Using a deterministic model , we have attempted to provide a systems-level input/output understanding of the signaling cycle , ubiquitous in signaling pathways . After identifying four parameter regimes ( two of them not reported before , to our knowledge ) , their steady-state and dynamic behaviors were analyzed and numerically verified . The results indicate that cycles act as low-pass filters , and that each regime may be useful under different circumstances . Given the values for cycle parameters , one can use our results to determine the regime in which the cycle operates . Unfortunately , the scarcity of parameter values makes it hard to assess which of these regimes is more widely present in signaling pathways . The low-pass filtering behavior of the cycle demonstrates that inputs of the same magnitude , but changing at different speeds , may produce very different outputs , which argues in favor of studying the dynamical properties of signaling pathways . All physical systems stop responding to fast-enough inputs , but what makes the low-pass filtering behavior of the signaling cycle interesting is that it is first-order , with a single cutoff frequency , and that the cutoff frequency can be adjusted by evolution ( through changes in the enzymatic catalytic rates ) and by the cell ( through changes in gene expression ) . As such , the signaling cycle is a versatile module with simple dynamics that can be easily tuned for various noise-filtering needs and used to construct signaling networks with more-complicated functions and dynamics . Of the two newly identified regimes , the signal-transducing one is of particular interest because it appears ideal to transmit time-varying intracellular signals without distortion while filtering out high-frequency noise in the input . Furthermore , because it is linear , it opens up the possibility that at least parts of signaling pathways ( those built of signal-transducing signaling cycles , or other yet-unidentified linear signaling motifs ) may be amenable to linear system analysis , a powerful set of tools to understand the properties of arbitrary network structures and motifs ( for example , elucidating the roles of cascades , positive and negative feedbacks , etc . ) . If naturally occurring cycles operate in the signal-transducing regime , then analyzing networks built of these cycles becomes tractable as long as load effects can be neglected . Can naturally occurring signaling cycles operate in this regime ? Although it was demonstrated that certain kinases in S . cerevisiae and Xenopus operate in saturation ( with MM constant of approximately 5 nM and substrate concentrations of approximately 30–100 nM for yeast [31 , 55] ) , little is known about phosphatases . To explore the possibility that known signaling pathways operate in the signal-transducing regime , we manually collected values of MM constants from the biochemical literature . We then used data for intracellular protein concentrations measured using GFP-tagged proteins [36] . Phosphatases seem to have a broad specificity , with a relatively wide range of MM constants ( e . g . , 5 to 90 μM for the PP2C phosphatases ) , and appear to be present in large concentrations ( e . g . , [Ptc1] ≈ 1 , 520 molecules per cell , so ≈ 0 . 025 μM , whereas [Ptc2–3] ≈ 15 , 000 , so ≈ 0 . 025 μM , assuming a yeast cell volume of 0 . 1 pl [31] ) . Data on singly phosphorylated substrates are hard to find , but for a rough indication , consider the doubly phosphorylated protein Pbs2 of S . cerevisiae as an example . Pbs2 is measured to have about 2 , 000 molecules per cell so that ≈ 0 . 03 μM = 30 nM . If singly phosphorylated proteins were characterized by similar numbers , then their phosphatases could potentially be unsaturated , since . In contrast , kinases that act on Pbs2 are present at lower concentrations ( e . g . , [Ste11] = 736 , [Ssk2] = 217 , and [Ssk22] = 57 molecules per cell , or ≈ 1–3 nM ) . Such concentrations are consistent with kinases operating in saturation , since ( assuming K1 is in the same range as those measured for Ste7 , K1 ≈ 5 nM ) . Taken together , these numbers suggest the possibility of a signaling cycle operating in the signal-transducing regime . Different signaling cycles , however , may be operating in different regimes , raising two questions: first , which regime is chosen by the cell for a cycle in a particular position in a network for a specific signaling application ? Second , what are the advantages and disadvantages of each such regime ? To answer the first question , one approach is to determine in vivo concentrations and MM constants of involved enzymes . Unfortunately , these data are often unavailable or scattered throughout publications in the biochemical literature . The applicability of MM constants measured in vitro is also questionable . An alternative experimental approach to establishing what regime a cycle operates in would be to obtain its steady-state response curve and determine which of our four cases it corresponds to . Similarly , one could experimentally obtain the response of the cycle to stimuli of various frequencies and use our dynamic characterization to infer the operating regime . One may , furthermore , be able to estimate some of the biochemical parameters and concentrations of the participating molecules from these experimental response characteristics . The success of such measurements depends on , and hence is limited by , the availability of in vivo single-cell probes for the phosphorylation state of a particular protein . The second question , on advantages and disadvantages of each regime , can be addressed by systematic analysis of cycle properties: steady-state and dynamic response , robustness to fluctuations , etc . By matching these characteristics against the requirements of a particular signaling system , one can suggest the optimal regime for each signaling application . For example , one can think that signaling in retina cells shall be fast and graded , depending on the intensity of adsorbed light . Similarly , gradient sensing in motile cells has to provide graded responses on the timescales required to change direction of motion . On the other hand , signaling of cell fate–determining stimuli and signaling involved in various developmental processes may require an ultrasensitive ( “on/off” ) response , while imposing much softer constraints on the time it takes to switch the system from off to on state ( hours instead of the milliseconds needed in light-sensing ) . The performance of the signaling regimes in the context of cascades and feedbacks is also important for understanding the rules that govern the choice of a regime for each cycle . For cycles in signaling applications involving all-or-none decisions , such as differentiation , apoptosis , or the cell cycle , it has been argued that ultrasensitive cycles may be useful because they effectively generate a discrete output that is either high or low [25] . When such a cycle is tuned appropriately ( such that in the presence of the background input , it is close to its threshold ) [32] , it is the best cycle at recovering time-dependent signals buried in noise , because its gain for low-frequency inputs is the highest among the regimes . Therefore , an ultrasensitive cycle is desirable when the input signals are extremely noisy and/or have to achieve binary-level outputs . A signal-transducing cycle , on the other hand , is the best choice to transmit time-dependent signals without distortion , because its output is approximately a scaled , but otherwise undistorted , copy of low-frequency input signals , while noisy input components are filtered out . It is the only cycle that does not distort the input . What the other two regimes might be best at is not clear . The threshold-hyperbolic cycle , however , may prove useful in situations when no activation is desirable below a given input strength and when a graded response is desired for inputs above this threshold . We here considered the effect of temporal noise in kinase levels on the response of the signaling cycle . A more-detailed model should also take into account the intrinsic noise coming from the cycle itself , since it consists of chemical reactions in which the number of molecules per species is small , and thus a deterministic model based on MAK may be inadequate . For example , although the deterministic cycle is known to have a single steady-state solution , Samoilov et al . ( see [6] ) found that treating the cycle stochastically can give rise to a bimodal distribution for the phosphorylated protein . The “mass fluctuation kinetics” approach described in [56] may be useful in this regard ( see also [57 , 58] ) . Other sources of noise that should also be taken into account are fluctuations in molecule numbers from cell to cell , as has been well-documented for gene levels ( see [5 , 59 , 60] , for example ) . Lastly , some of the species of the cycle may be found only in the cellular membrane rather than in the cytoplasm , or may be localized within specific cellular compartments , or may move about the cell by diffusion or active transport in an activity-dependent manner ( e . g . , the yeast protein HOG1 that dwells in the cytoplasm unless doubly phosphorylated , when it translocates into the cell nucleus ) . The consequences of these spatial effects need to be understood ( see [16] for a recent review ) . Achieving a detailed understanding of signaling pathways is an important problem , but is highly limited by the lack of experimental data with enough resolution to support modeling efforts . Nevertheless , having coarse-grained functional characterizations of the possible operating regimes of constituent cycles may permit system-level modeling of networks built of such cycles , despite uncertainties and variations in underlying biochemical parameters and molecular concentrations . Perhaps identifying and analyzing other relevant modules of biological networks , as we have done here for a signaling cycle , will shed some light on their behavior . Similar explorations could be done , for example , on signaling cycles that require multiple phosphorylation events to become active , or on G-protein–coupled receptors . Although characterization of the component modules of a biological network is a necessary and important step toward understanding network operation , it should be kept in mind that the behavior of the network will undoubtedly be considerably richer than that of the individual modules .
All analytical expressions were obtained starting from Equation 3 , the tQSSA approximation of the cycle , the derivation of which is discussed in Text S1 . The full MAK description of the system ( again , see Text S1 ) was analyzed numerically to obtain the data used in all the plots . Therefore , although the analytical expressions in this paper depend on the accuracy of the tQSSA , the general results do not , because they have been numerically verified on the full system . The cycle equation ( Equation 3 ) corresponding to each regime is described in Text S2 . These equations were then used to obtain the steady-state expressions in Table 2; see Text S3 . The expression for the amplitude of the response to sinusoidal inputs ( Equation 4 ) was obtained from a small-signal approximation of Equation 3 , as described in Text S4 . There , we also outline the method to obtain the expressions in Table 3 . All numerical analysis was done in Matlab and , unless explicitly mentioned here , is based on the full MAK description of the cycle . The data in Figure 2 were obtained by setting the derivatives of the MAK model to zero and solving the resulting algebraic relations numerically . The data in Figure 3 are the only ones based on the tQSSA , and are described in Text S5 . Figures 4 , 5 , and S1 were obtained by numerically integrating the MAK equations for the given inputs using the stiff differential equation solver from Matlab ode23s . Finally , the data in Figure 6 were obtained by numerically integrating the MAK equations using the Runge-Kutta algorithm on inputs of the form E0 ( 1 + asinωti + η ( 0 , 1 ) ) ( where ti is any time point in the numerical integration , and η ( 0 , 1 ) is a normal random variable with unit variance and zero mean ) . All the code is available upon request . For all the dynamic simulations , the steady-state level of the input for the four cycles was chosen such that the steady-state output was about halfway to saturation to allow the cycles to respond as much as possible . Choosing other steady-state values where the slope of the steady state response curve is small would lead to little response . Particular care has to be taken with the ultrasensitive cycle , which has a very small range of inputs where its slope is non-zero , implying that this cycle needs to be finely tuned for it to transmit dynamic information ( see Text S4 ) .
The SwissProt ( http://expasy . org/sprot/ ) [61] accession numbers for the proteins mentioned in this paper are Pbs2 ( P08018 ) , Ptc1 ( P35182 ) , Ptc2 ( P39966 ) , Ptc3 ( P34221 ) , Ssk2 ( P53599 ) , Ssk22 ( P25390 ) , Ste7 ( P06784 ) , and Ste11 ( P23561 ) .
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A cell is subjected to constantly changing environments and time-varying stimuli . Signals sensed at the cell surface are transmitted inside the cell by signaling pathways . Such pathways can transform signals in diverse ways and perform some preliminary information processing . A ubiquitous building block of signaling pathways is a simple biochemical cycle involving covalent modification of an enzyme–substrate pair . Our paper is devoted to fully characterizing the static and dynamic behavior of this simple cycle , an essential first step in understanding the behavior of interconnections of such cycles . It is known that a signaling cycle can function as a static switch , with the steady-state output being an “ultrasensitive” function of the input , i . e . , changing from a low to high value for only a small change in the input . We show that there are in fact precisely four major regimes of static and dynamic operation ( with ultrasensitive being one of the static regimes ) . Each regime has its own input–output characteristics . Despite the distinctive features of these four regimes , they all respond to time-varying stimuli by filtering out high-frequency fluctuations or noise in their inputs , while passing through the lower-frequency information-bearing variations . A cell can select the regime and tune the noise-filtering characteristics of the individual cycles in a specific signaling pathway . This tunability makes signaling cycles versatile components of elaborate cell-signaling pathways .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods",
"Supporting",
"Information"
] |
[
"biophysics",
"biochemistry",
"eukaryotes",
"computational",
"biology"
] |
2007
|
Operating Regimes of Signaling Cycles: Statics, Dynamics, and Noise Filtering
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Various factors contribute to the urbanization of the visceral leishmaniasis ( VL ) , including the difficulties of implementing control measures relating to the domestic reservoir . The aim of this study was to determine the prevalence of canine visceral leishmaniasis in an urban endemic area in Brazil and the factors associated with Leishmania infantum infection among seronegative and PCR-positive dogs . A cross-sectional study was conducted in Belo Horizonte , Minas Gerais , Brazil . Blood samples were collected from 1 , 443 dogs . Serology was carried out by using two enzyme-linked immunosorbent assays ( Biomanguinhos/FIOCRUZ/RJ and “in house” ) , and molecular methods were developed , including PCR-RFLP . To identify the factors associated with early stages of infection , only seronegative ( n = 1 , 213 ) animals were evaluated . These animals were divided into two groups: PCR-positive ( n = 296 ) and PCR-negative ( n = 917 ) for L . infantum DNA . A comparison of these two groups of dogs taking into consideration the characteristics of the animals and their owners was performed . A mixed logistic regression model was used to identify factors associated with L . infantum infection . Of the 1 , 443 dogs examined , 230 ( 15 . 9% ) were seropositive in at least one ELISA , whereas PCR-RFLP revealed that 356 animals ( 24 . 7% ) were positive for L . infantum DNA . Results indicated that the associated factors with infection were family income<twice the Brazilian minimum salary ( OR 2 . 3; 95%CI 1 . 4–3 . 8 ) , knowledge of the owner regarding the vector ( OR 1 . 9; 95%CI 1 . 1–3 . 4 ) , the dog staying predominantly in the backyard ( OR 2 . 2; 95%CI 1 . 1–4 . 1 ) , and a lack of previous serological examination for VL ( OR 1 . 5; 95%CI 1 . 1–2 . 3 ) . PCR detected a high prevalence of L . infantum infection in dogs in an area under the Control Program of VL intervention . Socioeconomic variables , dog behavior and the knowledge of the owner regarding the vector were factors associated with canine visceral leishmaniasis ( CVL ) . The absence of previous serological examination conducted by the control program was also associated with L . infantum infection . It is necessary to identify the risk factors associated with CVL to understand the expansion and urbanization of VL .
Human visceral leishmaniasis ( HVL ) constitutes a public health problem that affects millions of people throughout the world [1] . Over the past decade , there has been an average of 3379 cases of HVL per year in Brazil , with an incidence of 1 . 9 cases per 100 , 000 inhabitants [2] . During this period , however , an increase in the prevalence of the disease has been observed in several urban areas , and this phenomenon may be attributed to high population density , increased migration , environmental changes , inadequate living conditions and vector adaptation [1] , [3] . In South America and Europe , the causative agent of HVL is Leishmania ( Leishmania ) infantum , a protozoan parasite transmitted by sand flies of the Phlebotominae family , which are widely distributed in both wild and domestic surroundings [4] . Dogs are the main urban reservoirs and represent the major source of contagion for the vector by virtue of their high prevalence of infection and intense cutaneous parasitism [5] . Furthermore , it has been estimated that more than 50% of seropositive dogs are asymptomatic [6] and may remain free of clinical symptoms for several years or even throughout life [7] . The prevalence of canine visceral leishmaniasis ( CVL ) in endemic areas of Brazil ranges between 5 . 9 and 29 . 8% [8]–[13] , although the serological methods employed in the detection of CVL exhibit low sensitivities and may underestimate the true value [14]–[15] . The Brazilian Ministry of Health , through the Control Program of Visceral Leishmaniasis ( CPVL ) , has instituted specific measures to control the dissemination of the disease , and these include early diagnosis and treatment of human cases , identification and elimination of seropositive infected dogs , control of insect vectors and health education [2] . To date , however , the actions of CPVL have had little impact , and this negative outcome has been ascribed to delays in detecting and eliminating infected dogs , the tendency to replace infected dogs by susceptible puppies , and the low sensitivity of the available serological methods [16]–[18] . Although serological techniques lack the sensitivity required to detect Leishmania in the initial stages of infection , polymerase chain reaction ( PCR ) based assays can disclose the presence of protozoan DNA very early on , even before seroconversion [19]–[20] . Epidemiological studies employing modern molecular techniques have revealed that the prevalence of CVL in endemic areas in Europe is far greater than serological methods had previously suggested [15] , [21]–[22] . According to De Andrade et al . [14] , it is possible that as many as 62% of Brazilian dogs showing negative serological and parasitological tests for L . infantum would be classified as CVL-positive according to PCR and restriction fragment length polymorphism ( RFLP ) assays . A cohort study conducted by Oliva et al . [20] showed that most of the animals had PCR-positive results months before seroconversion . In addition , experimentally infected dogs have been found to be positive by conjunctival PCR by 45 days of infection [23] . To understand the expansion and urbanization of VL , it is necessary to identify the risk factors associated with human and/or canine infection . A number of publications have considered the factors influencing HVL [24]–[26] , but the potential risk factors of the canine disease have received far less attention . Information concerning animal susceptibility and its association with race , size , type of hair and age is available [8] , [27]–[28] . However , factors relating to the domiciliary and peridomiciliary environment , the socioeconomic status of the owner , the type of care provided for the animal , and specific animal behavior must be investigated to explain the importance of dogs in the maintenance of CVL in urban areas . In view of the aforementioned problems an investigation was undertaken to look into the prevalence of L . infantum infection using PCR followed by RFLP and serological methods ( ELISA ) . The factors associated with L . infantum infection among seronegative ( determined by enzyme-linked immunosorbent assay - ELISA ) and PCR-RFLP–positive dogs were also assessed . The L . infantum infection criterion proposed herein prioritizes CVL early onset . This study was conducted in Belo Horizonte , the capital of the State of Minas Gerais , located in Southeastern Brazil , which is considered an area of active transmission [29] .
The study was approved by the Committees of Ethics in Animal Experimentation of the Universidade Federal de Ouro Preto ( protocol no . 083/2007 ) , of the Universidade Federal de Minas Gerais ( protocol no . 020/2007 ) , and of the City Council of Belo Horizonte ( protocol no . 001/2008 ) . All procedures in this study were according to the guidelines set by the Brazilian Animal Experimental Collage ( COBEA ) , Federal Law number 11794 . Owners of the dogs participating in the project were informed of the research objectives and were required to sign the Informed Consent Form before sample and data collection . The cross-sectional study was conducted in 2008 in the northwest sanitary district of Belo Horizonte , which covers an area of 36 . 874 km2 ( Fig . 1 ) . According to the census by the Instituto Brasileiro de Geografia e Estatística in 2007 , the human population of this area is 360 , 000 . The canine population comprised 20 , 883 animals , according to the Zoonosis Control Management of the northwest sanitary district . At the time of the study , the prevalences of CVL in Belo Horizonte and its northwest sanitary district were 7 . 6 and 7 . 8% , respectively [30] . With an expected prevalence of CVL in the study area of between 5 and 10% , the 95% confidence interval , and an estimated precision of 1 . 5% , the appropriate sample size for the study was calculated to be approximately 1500 animals . Because of the high prevalence of seropositive dogs and the presence of human cases , the activities of the CPVL , including canine surveys ( diagnosis and culling seropositive dogs ) , have been carried out in the study area annually . The present field work was done in close collaboration with the Municipality Health Service , and the data were collected during the canine survey census , conducted by the health agents , as part of CVLP's routine . The studied area was selected within the northwest sanitary district by convenience and was chosen because at that moment ( 2008 ) a canine survey was beginning in this area . The households visited by the CVLP in an area that comprised of 37 census tracts ( according to the Brazilian Institute of Geography and Statistics ) [31] were included in the present study . A total of 918 households were included in this study , and all dogs within selected houses were sampled . A trained research team interviewed the owners of the study animals using a previously tested , structured questionnaire that sought information regarding the following groups of variables: ( i ) knowledge about the disease ( i . e . , form of transmission and clinical signs of HVL ) ; ( ii ) knowledge about the vector ( characteristics and presence in the domicile and peridomicile ) ; ( iii ) knowledge about the host ( epidemiological importance of the host , clinical signs of leishmaniasis , and care of the dog ) ; ( iv ) socioeconomic characteristics of the owner ( per capita/family income , and schooling ) ; ( v ) characteristics of the domicile , annexes and surroundings [i . e . , structure of roof , floor and walls; number of rooms , including bedrooms; number of residents; presence of trees ( particularly banana trees ) ; rubble; exposed garbage; dead leaves; and vegetable garden]; ( vi ) method of garbage disposal ( collected , burnt or buried ) ; and ( vii ) presence of other domestic animals ( birds , cats and cattle ) . The knowledge about the disease was validated according to self-reporting of the mainly symptoms of LVC and LVH . Vector recognition was acknowledged by self-reporting and validated by the showing of different diptera species samples ( Lutzomyia longipalpis and Aedes aegypti ) to the participants . The following information on the dogs was collected on an appropriate form: age , sex , size , hair type , breed , behavior ( habits related to the place where the dog sleeps spends most of its time , i . e . in the street , in the residence , in the backyard ) , dog care , clinical examinations , past history of vaccination and serological exams previous to leishmaniasis . Some characteristics were defined by the health agents , such as breed , dog size , hair type and clinical evaluation . These characteristics are routinely obtained and registered in a standardized form used by CPVL in the canine survey . The hair type was defined according to the breed , i . e . , collie was classified as long-furred hair , Doberman as short-furred . Dog size also was defined according to the breed , i . e . , pinscher was categorized as small size , poodle as medium size and German shepherd as large size . According to the absence/presence of clinical infection signs , the dogs were categorized as asymptomatic , with no signs suggestive of disease , and symptomatic , with characteristic clinical signs of visceral leishmaniasis , such as opaque bristles , severe loss of weight , onychogryphosis , cutaneous lesions , apathy and keratoconjunctivitis . A sample of peripheral blood ( 5 mL ) was collected by puncture of the brachiocephalic vein and an aliquot transferred to a glass vial containing sufficient anticoagulant ( ethylenediaminetetraacetic acid; EDTA ) to give a final concentration of 1 mg/mL . The blood sample was centrifuged ( 1500–1800×g; 20 min ) , the buffy coat containing the leukocytes removed , resuspended in 10 mM Tris-HCl buffer ( supplemented with 1 mM EDTA ) in the proportion of 1∶1 , and stored at −80°C until required for PCR-RFLP . The remaining portion of the blood sample was transferred to two separate filter papers for subsequent analysis by enzyme-linked immunosorbent assay ( ELISA ) . ELISA was performed in parallel in the Laboratory of Immunopathology of Universidade Federal de Ouro Preto ( LIMP ) and the Laboratory of Zoonosis of the Prefeitura Municipal de Belo Horizonte ( LZOON ) . The presence of IgG against Leishmania in blood samples was determined using an “in-house” ELISA procedure performed at the LIMP . Soluble Leishmania chagasi ( MHOM/BR/1070/BH46 ) antigen ( SLA ) was prepared by the method of Reis et al . [32] from promastigotes harvested from stationary-phase liver infusion tryptose cultures . The concentration of protein in the SLA solution was determined as previously described [33] and adjusted to 1000 µg/mL . Diluted SLA was divided into small portions and stored at −70°C until required for assay . In the ELISA procedure , 96-well MaxiSorp™ microplates ( Nalge Nunc Int . , Rochester , NY , USA ) were coated with SLA ( 2 µg/well ) and maintained overnight at 4–8°C . Wells were then washed , and eluates from blood dried on filter paper were added at 1∶80 dilution . To perform the reaction , filter paper was thawed and 5-µm-diameter spots eluted in casein-PBS for testing by ELISA . The wells were washed again prior to the addition of peroxidase-conjugated sheep anti-dog IgG ( anti-heavy chain specific; Bethyl Laboratories Inc . , Montgomery , TX , USA ) . After further washes , chromogenic substrate ( O-phenylenediamine; Sigma–Aldrich , St . Louis , MO , USA ) was added , and the absorbance was read on an automatic EL 800G ELISA microplate reader ( Bio Tek Instruments , Winooski , VT , USA ) at 492 nm . The anti-IgG conjugate concentration employed ( 1∶16 , 000 dilution ) was determined by a block titration method employing positive and negative standard sera . The cut-off value was established as the mean absorbance value +2 SD from 20 eluates from blood of uninfected dogs dried on filter paper . Duplicate filter papers were submitted to ELISA at LZOON using a kit developed by Fundação Oswaldo Cruz , EIE – Ensaio Imunoenzimático para diagnostico da leishmaniose visceral canina Bio-Manguinhos ( Rio de Janeiro , RJ , Brazil ) and applied according to the supplier's instructions . DNA was extracted from buffy coat fractions using Wizard™ Genomic DNA purification kits ( Promega , Madison , WI , USA ) according to the manufacturer's instructions . The primers used to amplify the conserved region of the Leishmania kDNA minicircle were as follows: forward: 5′-GGG ( G/T ) AG GGG CGT TCT ( G/C ) CG AA-3′; reverse: 5′- ( G/C ) ( G/C ) ( G/C ) ( A/T ) CT AT ( A/T ) TTA CAC CAA CCC C-3′ [34] . The reaction mixture consisted of 1× buffer [10 mM Tris-HCl , 50 mM KCl ( pH 8 . 8 ) ] , 1 . 5 mM MgCl2 , 2 . 0 µM dNTP , 1 . 0 pmol of each primer , 0 . 76 U of Taq polymerase ( Sinapse , São Paulo , SP , Brazil ) , 2 . 5 µL DNA and Milli Q water to a final volume of 12 . 5 µL/well ( MicroAmp® Fast Optical 96-Wells , Applied Biosystems , Foster City , CA , USA ) . PCR reactions were performed in a 96-well Verit Thermal Cycler ( Applied Biosystems ) using the following program: initial denaturation at 94°C for 1 min , followed by 40 cycles of 30 s at 93°C , 1 min at 64°C and 1 min at 72°C , with a final extension at 72°C for 7 min . DNA from L . chagasi ( strain MHOM/BR/1972/BH46 ) , obtained from the DNA reference library at LIMP , was used as positive control , while DNA from non-infected dogs , raised in the experimental kennels at UFOP , was used as negative control . PCR amplicons ( 5 µL ) were digested for 3 h at 37°C in 1 U of Hae III ( Invitrogen , Carlsbad , CA , USA ) in 1× buffer [10 mM Tris-HCl , 10 mM MgCl2 ( pH 7 . 5 ) ] and Milli Q water to a final volume of 15 . 0 µL/well ( MicroAmp® Fast Optical 96-Well , Applied Biosystems ) [35] . Restriction fragments , together with a 25 bp DNA ladder ( Invitrogen ) , were electrophoresed in 10% polyacrylamide gels at 40 mA in 89 mM Tris base ( pH 8 . 0 ) , 89 mM boric acid and 2 mM EDTA . Bands were detected by silver staining , and the patterns were compared with those obtained using DNA from L . ( L . ) amazonensis ( MHOM/BR/1973/M2269 ) , L . ( Viannia ) braziliensis ( MHOM/BR/1975/M2903 ) and L . ( L . ) chagasi ( MHOM/BR/1972/BH46 ) from the DNA reference library at LIMP . Samples with very faint bands in PCR were extracted again and assayed by PCR to obtain better bands in the RFLP profile . All samples that did not show similar profiles to L . infantum DNA were excluded from the present study . Dogs were classified as seronegative if ELISA results were negative in both laboratories ( LIMP and LZOON ) . The seronegative animals were categorized as ( i ) infected group: animals presenting positive PCR-RFLP for L . infantum; and ( ii ) non-infected group; animals presenting negative PCR-RFLP for L . infantum . These two groups were analyzed to identify factors associated with infection . Databases were generated using EpiData version 3 . 2 ( EpiData Association , Odense , Denmark ) by double entry of the results , and they were subsequently corrected , compared and analyzed using STATA version 11 . 0 software ( Stata Corp . , College Station , TX , USA ) . To investigate the factors potentially associated with L . infantum infection , the infected and non-infected groups of animals were compared . A mixed logistic regression model [36] was employed to evaluate the association between the independent and dependent variables . This model was chosen on the basis that the sampling process included all dogs within a studied household , and it incorporated the underlying assumption that observations obtained from dogs in the same household were mutually dependent while observations from dogs in different households were independent . The xtmlogit function provide by Stata was used to perform the analysis and the household was included as a random effect . Univariate analysis using the mixed logistic regression model was conducted for all variables collected , and those that attained a p value<0 . 25 were included in the multivariate models . Hierarchical analysis levels were established on the basis of a hypothetical canine infection model that took into account the collected variables . The inclusion of variables in the model was based on a conceptual framework describing the hierarchical relationships between risk factors and canine L . infantum infection [37] . The variables were grouped in four levels: socioeconomic conditions; household and outside-home conditions; knowledge of vector and host; and dog characteristics and behavior ( Fig . 2 ) . Variables with a significance of p<0 . 15 in each hierarchical level were maintained in the next level . Variables presenting statistical significance at each level but with either collinearity or low frequency were excluded from the multivariate analysis , while categorical variables were transformed into dummy variables . Backward analyses were used to construct intermediate and final models , and likelihood ratio tests were used to adjust these models . Variables with significance levels of p<0 . 05 were maintained in the final model .
Of the 1443 dogs studied , 230 ( 15 . 9% , 95% CI 14 . 1–17 . 9 ) were seropositive according to at least one ELISA . The results in each laboratory were 12% ( LIMP ) and 9 . 4% ( LZOON ) . PCR-RFLP analyses revealed that 356 ( 24 . 7%; 95% CI 22 . 5–26 . 0 ) of the dogs studied were positive for L . infantum DNA . Only three showed molecular bands similar to L . braziliensis , and they were not included in the present study . Among 1087 PCR-negative and 356 PCR-positive animals , 170 dogs ( 15 . 6% ) and 60 dogs ( 16 . 8% ) , respectively , were seropositive in at least one ELISA . To investigate factors associated with L . infantum infection , those animals that were positive in at least one ELISA test ( n = 230 ) were excluded . Therefore , among the 1 . 213 seronegative dogs , two groups were set up , according to PCR-RFLP: 296 ( 24 . 4% ) positive and 917 ( 75 . 6% ) negative . Within the group of 1213 dogs included in the evaluation of associated factors , female ( 53 . 7% ) , medium-sized ( 52 . 3% ) and short-haired ( 54 . 7% ) animals predominated . The mean age was 54 . 2 months ( SD 39 . 8 ) , and the median was 48 months ( IQR 24; 84 ) . Most of the dogs ( 58 . 9% ) had received a check-up by a veterinarian . The majority of the animals ( 97 . 6% ) were asymptomatic ( no signs suggestive of disease ) , and most ( 52 . 6% ) had been acquired within the neighborhood of the owner's residence . Generally , the animals lived and slept in the backyard ( 83 . 7 and 77 . 7% , respectively ) , rather than inside the residence . Of the 918 dog owners who were interviewed , 903 ( 98 . 4% ) had some knowledge about leishmaniasis , and of these , 533 ( 59 . 0% ) knew about the forms of transmission . However , despite this rather widespread awareness , only 201 owners ( 21 . 9% ) were familiar with the symptoms of HVL , and only 103 ( 11 . 2% ) claimed to have knowledge of the vector of Leishmania . Around 4% of owners had seen the vector in their domicile and/or peridomicile . Concerning CVL , 328 owners ( 35 . 7% ) stated that they were aware of the importance of dogs in the transmission of leishmaniasis , and 417 ( 45 . 4% ) declared that they knew the symptoms of the disease in the dog . When asked about their views if their pet were found to be infected with Leishmania , 75 . 1% of owners stated that they would authorize euthanasia . Interestingly , of the 209 owners ( 22 . 8% ) who had dogs with CVL in the past , 162 ( 77 . 5% ) consented to euthanasia of their animal , whereas 37 ( 17 . 7% ) sought treatment for their pet . At least one case of CVL had been recorded in the vicinity ( same block ) of many ( 49 . 5% ) of the residences evaluated ( data not shown ) . A total of 918 households were selected . They had a mean of 1 . 57 ( SD 1 . 17 ) dogs per household ( 1–9 dogs/house ) and median of 1 ( IQR 1; 2 ) . The majority of the dwellings ( 563; 61 . 3% ) were detached houses , while 873 ( 95 . 1% ) had plastered walls , 754 ( 82 . 1% ) had a garden and 909 ( 99 . 0% ) were served by main sewage . Garbage was collected three or more times per week from 860 ( 93 . 7% ) residences . The mean numbers ( SD ) of rooms and bedrooms per house were 7 . 2 ( SD 2 . 8 ) and 2 . 7 ( SD 1 . 0 ) , respectively . Each dwelling had an average of 3 . 8 ( SD 1 . 8 ) residents . A comparison between the infected ( n = 296 ) and non-infected ( n = 917 ) groups of animals was performed by multivariate analysis using the variables obtained from the interviews with owners and the records of individual dogs . The results of preliminary selection of the variables from the univariate analysis ( p<0 . 25 ) are shown in Tables 1 and 2 . The variables selected to build the final model ( p<0 . 15 ) were knowledge of the owner regarding the vector ( yes/no ) , knowledge of bite from the vector ( yes/no ) , house treated with insecticide ( no/yes ) , family income ( <2 minimum salary/2–3 minimum salary/> 3 minimum salary ) , type of floor in the residence ( other materials/tiles or wood ) , type of neighborhood ( houses/houses with garden/lands ) , origin of dog ( another district/present neighborhood ) , dog stays predominantly in the backyard ( yes/no ) , where the dog sleeps ( indoors/outdoors ) , and lack of previous CVL serological examination ( no/yes ) . Infection with L . infantum ( as detected by PCR-RFLP ) was associated with a family income of less than twice the minimum salary ( OR 2 . 3; 95% CI 1 . 4–3 . 8 ) , knowledge of the owner regarding the vector ( OR 1 . 9; 95% CI 1 . 1–3 . 4 ) , dog staying predominantly in the backward ( OR 2 . 2; 95% CI 1 . 1–4 . 1 ) and lack of previous CVL serological examination ( OR 1 . 5; 95% CI 1 . 1–2 . 3 ) ( Table 3 ) .
The results in the present investigation show that the prevalence of L . infantum infection in dogs as determined by PCR-RFLP ( 24 . 7% ) is higher than that detected by serology ( 15 . 9% ) . Such divergent values are highly significant because they demonstrate that the magnitude of CVL in this study area , which is under constant CPVL intervention , has been underestimated . Factors associated with early L . infantum infection ( PCR-RFLP+ ) were the socioeconomic conditions of the owner , the behavior of the dog , knowledge of the owner regarding the vector and the care the dogs had received . These results are relevant because they allow better understanding of the transmission of VL in a large city such as Belo Horizonte where leishmaniasis is expanding [29] , [38] . Moreover , the diagnosis of canine infection by L . infantum was achieved through the application of PCR-RFLP , which indicated the early onset of CVL . Additionally , as the data originated directly from dog owners and their respective animals , it was possible to perform a detailed analysis of a range of information and to determine the factors associated with CVL . Studies in European endemic areas have also demonstrated an elevated prevalence of infection ( typically 60–80% ) by PCR in comparison with that indicated by serology ( generally<30% ) [15] , [39] . Species identification was essential , especially because Belo Horizonte is an area of the simultaneous occurrence of cutaneous and visceral leishmaniasis and the dog can be host for both parasites [40] . Among the examined samples , only three showed molecular bands similar to L . braziliensis , and they were not included in the present study . Approximately a quarter of seronegative dogs were infected by L . infantum according to PCR-RFLP . These false-negative animals were likely within an “immunological window” that occurs prior to seroconversion , during which period B lymphocytes do not secrete polyclonal antibodies , and consequently , serological methods are less sensitive at this stage of the infection [41] . It is possible that false-negative dogs remain in the community as undisclosed reservoirs and , thus , interfere with the effectiveness of control measures . Indeed , despite recent intense efforts to eliminate seropositive dogs , no reduction in the incidence of HVL or CVL has been observed in urban areas [42] . Little is known if seronegative/PCR-positive dogs are immunologically resistant to Leishmania [43] or if they will develop the disease . However , it is possible to state that such animals have had previous contact with the parasite . Such information is relevant because canine positivity for Leishmania is included among the indicators for the prioritization of target control areas by the Ministry of Health . Although molecular biology methods are more promising in identifying infection , their use in the field requires further standardization and optimization . HVL is favored by precarious socioeconomic and housing conditions , migratory movements and the presence of vector and reservoir in the domestic environment [24]–[26] , [44] . However , little is known about the risk factors that facilitate Leishmania infection in the main reservoir of the disease , namely , the domestic dog . To obtain a better understanding of these factors , comparisons were made between non-infected ( seronegative/PCR-RFLP negative ) animals and those infected ( seronegative and PCR-RFLP positive ) . The decision to use PCR-RFLP–positive and seronegative animals was due to the detection of L . infantum in the initial stage of infection before seroconversion [19]–[20] . Regarding socioeconomic conditions of the owner , animals belonging to families with incomes of less than twice the minimum salary were twice as likely to be infected in comparison with dogs of higher-income families ( three minimum salaries ) . In this context , family income is a proxy variable of socioeconomic status and is probably associated with the structure of the most vulnerable domiciles . Indeed , Oliveira et al . [26] demonstrated an association between HVL and family income following a study in the metropolitan area of Belo Horizonte . These data are also consistent with literature confirming that VL is more frequent in areas of precarious socioeconomic status [45] . In general , dog owners showed little knowledge of phlebotomine sand flies . Interestingly , however , dogs whose owners knew about the vector were twofold more likely to acquire the infection than those whose owners were not familiar with the insect . This variable can be understood as an indirect measure of exposure to phlebotomines and shows the importance of using proxy . A similar observation has been reported by Moreno et al . [44] , who noted that in the metropolitan area of Belo Horizonte , the likelihood of being infected by Leishmania is six times greater for people who have seen the vector than for those who have not . A high density of Lu . longipalpis was observed in the present study area [38] , so it is not surprising that the most respondents had noted the presence of the vector in their residences and neighborhood . Dogs that usually lived in the backyard were twice as likely to acquire the infection as those that remained inside the house . According to Galvez et al . [46] , living outdoors is significantly associated with serological positivity for the parasite among canines . In the recent survey performed in Granada , Spain , dogs that slept outdoors were at greater risk than those sleeping indoors because of vector density [47] . On the other hand , Cabrera et al . [48] reported that the risk of infection by CVL is similar for dogs that live within the perimeter of a residence and those that wander the streets or woods . To reduce the risk of CVL , some preventive measures may be adopted , including the maintenance of dogs in closed kennels during periods of intense vector activity , the reduction of microenvironmental factors that favor the development of the vector in the residence , and the use insecticide-impregnated collars [22] , [49] . However , the implementation of such measures depends not only on the degree of awareness of the dog owner about the disease but , mainly , on socioeconomic issues , because the most affected population could not afford to leave their dogs in kennels or buy impregnated collars . Only 35 . 5% of owners knew of the important role of dogs in the transmission of Leishmania , and 45 . 5% had knowledge of the symptoms of CVL , although 22 . 8% reported previous ownership of a dog that had contracted CVL . Animals serologically tested by the CPVL previously were less likely to be infected . This finding indicates that seropositive dogs have been removed regularly by the control measures and that dogs that remain seronegative in successive tests are more likely to be CVL-free . Unfortunately , however , the replacement of dogs within the study area is frequent , and these animals would be more susceptible to infection by L . infantum [50] . The mean age of infected dogs was 49 . 8 ( SD 41 . 3 ) months , whereas the mean age of non-infected dogs was 54 . 5 ( SD 39 . 0 ) months . One possible explanation for this result is that the CPVL had removed seropositive dogs during the canine survey . Therefore , PCR was detecting L . infantum infection early , in younger dogs . Although the univariate analysis was significant , dog age was not associated with L . infantum infection . Galvez et al . [46] examined the age at which seroprevalence showed a bimodal distribution , with one peak appearing in the young dogs ( 1–2 years ) and a second , more evident , peak among the older dogs ( 7–8 years ) . On the other hand , França-Silva et al . [8] observed that the prevalence of infection was not correlated with dog age . The emergence of leishmaniasis in Belo Horizonte dates from the late 1980s , when the disease spread from areas marked by poor socioeconomic conditions [51] . At the present time , the disease is increasing , and VL has been detected in all regions of the city [38] . Indeed , the urbanization of VL is a current reality in many Brazilian cities . We tried to identify domiciles that were most vulnerable to the presence of the vector and occurrence of infection . However , no variable related to households was maintained in the final model . In a study conducted in Northeastern Brazil , the risk of HVL was greater in residences that lacked sewage services and garbage collection [25] . In the present study , no influence of such factors on the prevalence of CVL was found , as 99% of domiciles were served by a main sewage connection and nearly all received garbage collection . Even though our sampling procedure was not probabilistic , the studied households were sampled from a census survey , and the investigated blocks are representative of the northwest sanitary district . This study was not designed to evaluate a representative sample of Belo Horizonte but to assess the prevalence of infection by PCR-RFLP in seronegative dogs and identify risk factors for infection in these animals . However , the northwest sanitary district is representative of the city , with buildings , commerce , residences and green areas . Moreover , the main limitation of a cross-sectional study in identifying risk factors is that it does not permit causal inferences because time factors were not evaluated . Although it is not easy to attribute the associated factors with new measures that can be adopted by CPVL , it is necessary to better investigate the factors associated with VL expansion in urban areas . Improved understanding of urbanization processes in large cites such Belo Horizonte can help the CPVL to adopt measures that are more effective at controlling the spread of the disease . It is important to emphasize that the control of HVL depends on the management of CVL because dogs are the main urban reservoir of Leishmania and represent the main source of phlebotomine infection . The Control Program in Brazil used ELISA for screening and IFAT as a confirmatory test to identify seropositive dogs which are them euthanized . Due to the low level of humoral immune response , some of the infected dogs by L . infantum could not be detected . Therefore , using only seronegative dogs , this paper focuses on those animals that are positive by PCR and are not identified by the control program . Considering that the currently available serologic methods lack sufficient sensitivity and/or specificity to accurately identify all infected dogs , the employment of molecular diagnosis to detect the CVL infection before antibody production could be an efficient alternative . This study showed for the first time the identification of factors associated with early stage of CVL in animals seronegative with PCR-positive for L . infantum and therefore could contribute to better understanding of the involvement of this reservoir in urban-VL epidemiology . Additionally , for better investigation of the factors associated with VL expansion in urban areas further studies are required using a cohort study approach .
|
Visceral leishmaniasis ( VL ) is a disease caused by the parasite Leishmania infantum , and dogs are the most important domestic reservoirs of the agent . During recent decades , VL has expanded to large Brazilian urban centers . In the present work , we have demonstrated by using molecular techniques that the rate of canine infection as detected by serology has been considerably underestimated . Two groups of seronegative dogs ( infected and non-infected according to molecular methods ) were further evaluated from data obtained through interviews with owners of the animals . The factors associated with Leishmania infection in dogs were a family income of less than two minimum salaries , the knowledge of the owner regarding the vector , the dog spending most of its time in the backyard and the dog never having had a previous serological examination . Awareness regarding the factors associated with canine infection will improve health services and the understanding of the disease's expansion in urban areas .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"public",
"health",
"and",
"epidemiology",
"infectious",
"disease",
"epidemiology",
"veterinary",
"diagnostics",
"veterinary",
"science",
"veterinary",
"medicine",
"veterinary",
"diseases",
"veterinary",
"epidemiology",
"veterinary",
"parasitology",
"molecular",
"epidemiology",
"zoonotic",
"diseases",
"epidemiology",
"public",
"health",
"leishmaniasis"
] |
2011
|
Prevalence and Factors Associated with Leishmania infantum Infection of Dogs from an Urban Area of Brazil as Identified by Molecular Methods
|
Human herpesvirus 6 ( HHV-6 ) is prevalent in healthy persons , causes disease in immunosuppressed carriers , and may be involved in autoimmune disease . Cytotoxic CD8 T cells are probably important for effective control of infection . However , the HHV-6-specific CD8 T cell repertoire is largely uncharacterized . Therefore , we undertook a virus-wide analysis of CD8 T cell responses to HHV-6 . We used a simple anchor motif-based algorithm ( SAMBA ) to identify 299 epitope candidates potentially presented by the HLA class I molecule B*08:01 . Candidates were found in 77 of 98 unique HHV-6B proteins . From peptide-expanded T cell lines , we obtained CD8 T cell clones against 20 candidates . We tested whether T cell clones recognized HHV-6-infected cells . This was the case for 16 epitopes derived from 12 proteins from all phases of the viral replication cycle . Epitopes were enriched in certain amino acids flanking the peptide . Ex vivo analysis of eight healthy donors with HLA-peptide multimers showed that the strongest responses were directed against an epitope from IE-2 , with a median frequency of 0 . 09% of CD8 T cells . Reconstitution of T cells specific for this and other HHV-6 epitopes was also observed after allogeneic hematopoietic stem cell transplantation . We conclude that HHV-6 induces CD8 T cell responses against multiple antigens of diverse functional classes . Most antigens against which CD8 T cells can be raised are presented by infected cells . Ex vivo multimer staining can directly identify HHV-6-specific T cells . These results will advance development of immune monitoring , adoptive T cell therapy , and vaccines .
Human herpesvirus 6 ( HHV-6 ) may be among the most prevalent persistent viruses in the human population . Antibodies to HHV-6 are present in 95–100% of healthy adults [1 , 2] . Like other herpesviruses , HHV-6 establishes a lifelong infection . HHV-6 is a group of two virus species known as HHV-6A and HHV-6B . Primary infection with HHV-6B , the more widespread species of the two , usually occurs before two years of age , and often causes a common childhood disease known as three-day fever or exanthema subitum [3 , 4] . The first infection with HHV-6A is thought to occur later and appears mostly asymptomatic [5] . Later in life , HHV-6 may be involved in a variety of diseases . HHV-6A is suspected of contributing to the pathogenesis of thyreoiditis Hashimoto [6] and to neuroinflammatory diseases such as multiple sclerosis [7] . HHV-6B is related to severe complications in immunocompromised patients . After allogeneic hematopoietic stem cell transplantation ( allo-HSCT ) , HHV-6B reactivation is associated with increased all-cause mortality , delayed engraftment , graft-versus-host disease , and damaging infection of the central nervous system [8 , 9] . Since no HHV-6-specific antiviral agents are available , treatment of infection after allo-HSCT usually involves drugs approved for use against cytomegalovirus ( CMV ) , but these come along with significant side effects such as kidney failure or bone marrow depression [5] . A potentially more efficacious and tolerable form of therapy aims at restoring antiviral T cell immunity , which is defective in patients who reactivate HHV-6 [10] . For other viral infections after allo-HSCT , many clinical investigations have shown that adoptive transfer of donor-derived virus-specific T cells is safe and effective [11] . Most of these studies focused on the herpesviruses CMV and Epstein-Barr virus ( EBV ) , but some have recently included HHV-6-specific T cells [12] . Further development of such immunotherapies and of HHV-6 vaccines will require a detailed understanding of the virus-specific T cell response in health and disease . Information on HHV-6-specific T cell responses is still limited , in particular regarding CD8 T cells [13] . It was shown early that healthy virus carriers have CD4 T cells that respond to HHV-6 lysate or infected cells [14 , 15] . Target antigens and epitopes of the specific CD4 T cell response were identified first in a study on six selected structural proteins [16] , and more recently by a proteomic approach that has identified ten viral antigens targeted by CD4 T cells [17] . Information on the targets of CD8 T cells has remained much more limited . Responses to five HHV-6B proteins have been investigated so far , and a number of epitopes from these proteins that are presented by infected cells were identified [18–21] . These proteins were chosen because of their ( mostly distant ) homology to CMV proteins that elicit CD8 T cell responses . However , HHV-6B encodes approximately 98 unique proteins [22] , and the hypothesis remains unproven that T cell responses to HHV-6 and CMV are similarly structured or directed to corresponding antigens . The biological differences between these viruses are significant despite their evolutionary relationship as β-herpesviruses , and widespread cross-reactivity of T cells to HHV-6 and CMV seems unlikely considering that most of their proteins have quite divergent sequences [21] . Individual HHV-6 epitope-specific CD8 T cell responses were described to be of low frequency in peripheral blood [18–21] , and it has remained unknown whether stronger responses exist . These open questions prompted us to devise a method to analyse the CD8 T cell response to HHV-6 in a more comprehensive , cross-sectional fashion . Screens with libraries of peptides have been particularly efficient in obtaining copious information on the CD8 T cell repertoire against complex viruses [23–25] . However , due to the large number of possible targets , each such study has necessarily neglected some aspects of analysis , either regarding antigen coverage , HLA allotype coverage , precision of epitope identification , or verification of T cell function in the context of infection . Since detection of ex vivo responses to artificial peptides is not sufficient to prove the presence of T cells that recognize functional viral epitopes [26] , it is of particular importance to verify recognition of infected cells by individual peptide-specific T cells . To obtain a cross-sectional overview of the truly functional repertoire of HHV-6B-specific CD8 T cells and their target antigens and epitopes , we chose to base our approach on the entirety of HHV-6B proteins , but to focus on only one HLA class I allotype . We considered HLA-B*08:01 to be particularly suitable for such a study , because of the clarity of its peptide anchor motif [27] and its tendency to present dominant CD8 T cell epitopes in human viral infections [23 , 28–30] . To verify T cell specificity and function , we established specific T cell clones wherever possible , and used these to verify HLA restriction and recognition of infected cells . Our results show that the HHV-6-specific CD8 T cell repertoire targets multiple epitopes from all phases of the viral life cycle . We identify potent epitopes and track them in patients . We discuss implications for improved immune monitoring , studies of viral pathogenesis , and immunotherapy designs .
We wished to obtain a cross-sectional overview of HHV-6B antigens targeted by CD8 T cells . The reference sequence for HHV-6B strain Z29 contains 98 unique protein-coding genes or annotated ORFs with a total of 43 , 836 amino acids . For reasons of feasibility , we decided to screen the viral proteome for specific T cells with only one representative HLA class I restriction . We chose HLA-B*08:01 , the second most frequent HLA-B allotype in populations of European origin [31 , 32] . We had two more reasons for this choice . First , T cell responses to HLA-B*08:01-restricted viral epitopes are often among the strongest that are observed in a particular virus . Examples of such epitopes are shown in Table 1 . Second , B*08:01-presented peptides recognized by such T cells mostly conform to a clear-cut consensus motif [27 , 33 , 34] . This motif demands basic anchor residues ( arginine or lysine ) in positions 3 and 5 and an aliphatic residue ( leucine , isoleucine , valine , or methionine ) in the C-terminal position of an octameric or nonameric peptide . The HHV-6B reference sequence ( strain Z29 , GenBank NC_000898 ) contains 146 octameric and 153 nonameric peptides with this B*08:01 epitope motif , and 77 of 98 nonredundant ORFs contained at least one candidate ( see Supporting S1 Table for a full list ) . These peptides were synthesized and used in the following experiments . First , we attempted to determine the frequency of T cells specific for these HHV-6B-derived peptides in peripheral blood of healthy carriers . We stimulated PBMCs ex vivo with pools of the 146 octameric or the 153 nonameric peptides in an IFN-γ ELISPOT assay . Responses to HHV-6B peptide pools were much weaker than those to an EBV peptide pool , and generally below 1 / 10 000 PBMCs ( Fig 1A ) . Therefore , we decided to enrich HHV-6B-specific T cells from peripheral blood by peptide stimulation . PBMCs from four B*08:01-positive healthy HHV-6B carriers were initially stimulated with octamer or nonamer peptide mixes , and then restimulated every week with autologous CD40-activated B cells loaded with the same peptide mixes . Fig 1B shows analyses of such cultures from donor 1 after six to eight weeks of cultivation . Specific reactivity was observed against five subpools of the octameric peptides and at least seven subpools of the nonameric peptides , suggesting the presence of T cells specific for at least twelve HHV-6B peptides in this donor . After six to eight weeks of cultivation , limiting dilution of the peptide-stimulated cultures was performed to generate T cell clones . Between 6% and 23% of T cell clones were specific for the HHV-6B peptide pool that was used for expansion ( Table 2 ) , as demonstrated by their specific IFN-γ secretion in response to peptide-loaded B cells ( Fig 1C ) . Most of these clones could be sufficiently expanded to determine their precise peptide specificity by testing with peptide subpools ( Fig 1D ) and individual peptides in IFN-γ ELISA assays . Collectively , T cell clones recognized 25 HHV-6B peptides from 19 proteins or open reading frames ( Fig 2 ) . For seven specificities , we verified restriction through HLA-B*08:01 by tests with B*08:01-transfected , peptide-loaded 293T cells and , for comparison , with peptide-loaded B*08:01-matched B cells . The very clear patterns of IFN-γ secretion indicated that all T cell clones tested were restricted through HLA-B*08:01 , as shown for four clones in Fig 3 . We analyzed whether specific T cell clones were able to recognize their cognate antigen on HHV-6B-infected cells . Primary CD4 T cells from B*08:01-positive donors were activated with phytohemagglutinin ( PHA ) and infected with HHV-6B strain HST . Infected cultures were combined with peptide-specific CD8 T cell clones to test for specific IFN-γ secretion . T cell clones with 17 peptide specificities could be tested . For 13 of these , we observed specific recognition of HHV-6B infection at day 6 , as shown in Fig 4A and summarized in Fig 2 . Since most of the HHV-6B peptides recognized by T cells were fully or closely homologous to corresponding sequences in HHV-6A ( Fig 2 ) , we also tested the reactivity of T cell clones with six specificities against HHV-6A-infected cells . All six T cell clones recognized infected cells ( Fig 4B ) . For three epitopes , we could demonstrate presentation by both HHV-6B-infected and HHV-6A-infected cells . Three additional specificities could only be tested against HHV-6A , due to limited cell numbers . Four of the six HHV-6A epitopes were identical to their HHV-6B counterparts , two differed in only one conservatively exchanged amino acid . Overall , these experiments demonstrated that 16 of the 25 candidate peptides against which T cell clones could be established ( Fig 2 ) were bona fide epitopes processed and presented by cells infected with HHV-6B or 6A . Four candidates were not recognized , and five candidates could not be tested because T cell clones did not sufficiently expand and survive . We also tested cytotoxic reactivity against HHV-6B-infected target cells , focusing on CD8 T cells specific for the DFK peptide from U86 . Both a DFK-specific CD8 T cell clone and a polyclonal CD8 T cell line , obtained by PBMC stimulation with the peptide DFK , displayed strong cytotoxic activity against HHV-6B-infected cells , but not non-infected cells; HLA-B8 expression of the target cells was required for this activity ( Fig 4C–4E ) . We proceeded to analyze recognition of target cells over a period of 12 to 18 days of infection with HHV-6B ( Fig 5 ) . Some T cell clones reached a maximum of reactivity at three days of infection , others at six days . Timing of maximal recognition did not appear to correlate with the described expression kinetics of HHV-6B antigens . For example , the SPR epitope from immediate-early ( IE ) antigen U86 was maximally recognized on day 3 , but other IE antigens [44] such as U79 and B4 reached a maximum of recognition on day 6 . Presumably , time to completion of antigen processing differed between antigens , or potential secondary cycles of virus production and re-infection within the infected CD4 T cell culture augmented the presentation of some antigens to specific CD8 T cells . We performed time-course T cell recognition assays with T cell clones of additional specificities , including an analysis of recognition of HHV-6A and an additional control condition in the presence of ganciclovir , an inhibitor of HHV-6 replication ( Fig 6 ) . Presentation of various IE , E , and L antigens showed distinct peaks of recognition on days 3 , 6 , or 9 . Recognition of all epitopes was partially or fully inhibited by ganciclovir . Taken together ( Fig 2 ) , a majority of the HHV-6B peptide-specific T cell clones tested against infected cells recognized their endogenously processed target epitope in the context of infection . Epitopes from antigens of all kinetic categories ( IE , early , late ) and of diverse functional roles ( regulation , DNA synthesis , virus assembly , structural proteins ) were presented by infected cells . No particular class of antigens appeared to be excluded from presentation . At least three epitopes were from proteins with unknown function or putative proteins; our results provide evidence that ORFs including U7 , U26 , and B4 are translated in infected cells . Multiple epitopes were found in antigens U38 ( the DNA polymerase ) , U41 ( the major DNA-binding protein ) , and U86 ( the transcriptional regulator IE2 ) . A panel of thirteen HLA-B*08:01/peptide multimers ( dextramers ) was commercially synthesized . For inclusion in this panel , 11 of the 16 epitopes in their HHV-6B variants were arbitrarily chosen . For two of these epitopes , multimers loaded with their variant HHV-6A peptide were also synthesized; these included the HHV-6A variant of EGR ( from U79 ) and the "EFK" variant of DFK ( from U86; compare Fig 2 ) . As a positive control , a multimer for the Epstein-Barr virus epitope RAKFKQLL ( RAK ) from the BZLF1 antigen was synthesized in parallel . All these multimers were used to stain PBMCs from eight healthy donors for analysis in flow cytometry , to determine ex vivo frequencies of HHV-6-specific CD8 T cells ( Fig 7 ) . As the examples in Fig 7A show , T cells that bound a multimer DFK/B*08:01 , carrying the DFK peptide from U86 , often had an elevated frequency and appeared as clearly distinct populations . T cells that bound other HHV-6 multimers were usually much less frequent . Overall , DFK-specific T cells were detectable in 7 of 8 healthy donors , with a median frequency of 0 . 09% of CD8+ T cells ( 0 . 005%– 1 . 11% ) . The second most frequent population were T cells specific for the SPR epitope , also from U86 ( median 0 . 025% of CD8+ T cells; 0 . 007%– 0 . 07% ) . Thus , while T cells specific for many HHV-6 epitopes were in most donors not detectable above a standard baseline of 0 . 01% , we identified an HHV-6B epitope , DFK , that regularly allowed clear ex vivo detection of specific T cells by multimer staining . In contrast , T cells specific for the HHV-6A variant of this epitope , EFK , were of low frequency or absent . DFK-specific T cells displayed a mixed phenotype ex vivo with respect to markers of central memory , effector memory or terminal differentiation ( S1 Fig ) . The median number per donor of different HHV-6 epitope specificities with a frequency higher than 0 . 01% was four ( Fig 7C ) , and the highest number was seven . However , this number is likely to underestimate the overall number of specificities including those of lower frequency that are present in a donor , considering that the number of different specificities in donor 1 and 2 that could be obtained as T-cell clones after specific expansion was 17 and 12 , respectively ( Table 2 ) . There were three HHV-6 epitopes that elicited responses higher than 0 . 01% in more than half of the donors ( Fig 7D ) . A complete set of FACS plots is provided as supporting information ( S2 and S3 Figs ) . We analyzed the frequency of HHV-6-specific multimer staining-positive CD8 T cells in peripheral blood of three patients after HLA-B*08:01-positive allo-HSCT from unrelated HLA-matched donors . Patient 1 had detectable HHV-6 in throat swabs at the time of transplantation . In the third to fifth week after transplantation , while in aplasia , this patient underwent an episode of HHV-6 reactivation , detectable in gastric biopsy and throat swabs , with symptoms of skin rash and nausea . Treatment with foscarnet was initiated . At day +54 , EBV reactivation was detected , and was treated with cidofovir and rituximab . Probably due to viral infection , engraftment was delayed until day +105 . Samples were available for analysis of specific T cells on days +57 and +68 , at a time when HHV-6 reactivation had subsided ( Fig 8A ) . DFK-specific T cells were detected at both times at similar levels , while QTR-specific T cells were increasing ( Fig 8A and 8B ) . Patient 2 showed HHV-6 reactivation at day +29 after allo-HSCT with detection of the virus in bronchoalveolar lavage ( BAL ) , performed due to a CT scan showing pneumonia . A concurrent infection with Aspergillus fumigatus was found , as well as EBV and adenovirus reactivation . Patient 2 developed a histologically proven post-transplant lymphoproliferative disorder ( PTLD ) at day +94 , and treatment with cidofovir and rituximab was performed . HHV-6-specific T cells targeting four of four different epitopes were detected in patient 2 at moderate frequencies in two of two samples after resolution of HHV-6 reactivation ( Fig 8C ) . Patient 3 , who suffered from severe aplastic anemia , was admitted to allo-HSCT with ongoing detection of HHV-6 in throat swabs after immunosuppressive treatment consisting of anti-thymocyte globulin ( ATG ) , corticosteroids , and cyclosporine A . No specific HHV-6-related symptoms were observed . During aplasia , a concurrent enteral adenovirus reactivation occurred , and progressed to disseminated adenovirus disease . The patient was treated with cidofovir and adenovirus-specific T cells , and fully recovered . Viral infection probably contributed to delayed engraftment . HHV-6-specific T cells could be analyzed in an early sample concurrent with ongoing HHV-6 reactivation ( day +56 ) and a late sample ( day +1221 ) . Only DFK and the EBV epitope RAK could be studied in the early sample due to a shortage of material . DFK-specific CD8 T cells were absent at the time of reactivation ( Fig 8D and 8E ) , but were well reconstituted at the late time point ( Fig 8D , 8F and 8G ) . In addition , there was evidence for low-frequency establishment of CD8 T cells specific for various other HHV-6 epitopes ( Fig 8F and 8G ) at the late time point in this patient , who has remained alive and well until now . Taken together , these data provide tentative evidence that reconstitution of CD8 T cells specific for HLA-B*08:01-restricted HHV-6 epitopes , notably DFK , may be associated with control of viral reactivation in patients after allo-HSCT . However , all patients were treated with cidofovir , which has high activity against HHV-6 . Studies in larger patient cohorts will be necessary to establish an association of particular HHV-6 T-cell specificities and control of infection . Our study identified a total of 16 HLA-B*08:01-restricted epitopes that were presented by infected cells to specific CD8 T cells , based on a set of 299 peptides as epitope candidates . This test set consisted of all HHV-6B peptides that conformed to a simplified HLA-B*08:01 motif ( Table 1 ) defined by the presence of three anchor residues , while any amino acid was allowed in other positions of the peptide . To find out if other internal or flanking sequences were non-randomly enriched for preferred residues or motifs , we aligned our 16 epitopes and their flanking regions in their proteins of origin ( Fig 9A ) and analyzed their amino acid content in each position , subdividing amino acids into broad categories according to their chemical characteristics ( Fig 9B ) . In our set of 16 confirmed epitopes , there were nine arginines and seven lysines each in anchor positions N3 and N5 , suggesting that there was no strong preference for either of these two . Each of the four permitted aliphatic residues was found in the C-terminal anchor position ( C1 ) of the nonameric epitopes , with a preference for leucine , which may simply mirror the higher frequency of this amino acid in the viral proteome ( L , 10 . 1%; I , 6 . 4%; V , 6 . 2%; M , 2 . 4% in the HHV-6B GenBank reference sequence NC_000898 ) . All six octamers had a leucine in C1 . A tendency for leucine to be enriched in N7 was noted . Other than that , there was no strong enrichment of particular amino acids within the peptide other than in the three pre-defined anchor positions , and at least five of the six chemical categories were represented in each internal non-anchor position . Somewhat more conspicuous patterns were seen in the regions flanking the peptide . N2' was often an uncharged polar amino acid ( C , S , T , N , Q ) or a basic amino acid ( R , K , H ) , C1' was often serine or another uncharged polar amino acid , and C2' was often a basic amino acid . We calculated the likelihood that such enrichments occurred by chance using Fisher's exact test , comparing the 16 epitopes to the rest of the 299 peptide candidates ( Table 3 ) . The lowest probabilities of enrichment by chance were calculated for uncharged polar or basic amino acids in position N2' ( p = 0 . 0010 ) , serines in C1' ( p = 0 . 0016 ) , polar uncharged amino acids in C1' ( 0 . 0006 ) , and lysine in C2' ( p = 0 . 0013 ) . Thus , the strongest tendency in HLA-B*08:01-restricted T cell epitopes to follow conserved motifs ( apart from the three pre-defined anchor residues ) was not found for peptide-internal positions , but for certain flanking positions .
PBMCs from anonymized healthy adult donors were purchased from the Institute for Transfusion Medicine , University of Ulm , Germany . PBMCs from patients after allo-HSCT were obtained at the Department of Internal Medicine III , Hematopoietic Stem Cell Transplantation , Klinikum der Universität München , Munich , Germany , with written informed consent . Anonymized cord blood samples were collected at the Department of Obstetrics and Gynecology ( Klinik und Poliklinik für Frauenheilkunde und Geburtshilfe , Klinikum der Universität München , Munich , Germany ) . The institutional review board ( Ethikkommission , Klinikum der Universität München , Munich , Germany ) approved these procedures ( project no . 071–06–075–06 , project no . 17–455 ) . Standard cell culture medium was RPMI 1640 ( Life Technologies/Invitrogen , Karlsruhe , Germany ) supplemented with 10% FCS ( Biochrom , Berlin , Germany ) , 100 U/ml penicillin , 100 µg/ml streptomycin ( Life Technologies/Invitrogen ) , and 100 nM sodium selenite ( ICN Biochemicals , Aurora , CO ) . 293T cells were cultivated in DMEM ( Invitrogen ) with the same supplements . Cells were all cultivated at 37°C and 5% CO2 . PBMCs were obtained by centrifugation on Ficoll/Hypaque ( Biochrom ) . High-resolution HLA typing was performed by PCR-based methods ( MVZ , Martinsried , Germany ) . HHV-6-specific , HLA-B*08:01-restricted T cell lines and clones were derived from four HHV-6 IgG-positive donors . Their full HLA class I types are as follows: donor 1 , HLA-A*02:01 , A*68:01 , B*07:02 , B*08:01 , Cw*07:01 , Cw*07:02; donor 2 , HLA-A*01:01 , B*08:01 , B*15:01 , Cw*03:03 , Cw*07:01; donor 11 , HLA-A*01 , A*11 , B*08 , B*15:01 , Cw*03:03 , Cw*07:01; donor 12 , HLA-A*01:01 , A*02:01 , B*08:01 , B*40:01 . In healthy donors , HHV-6 IgG was determined by immunofluorescence test at Max-von-Pettenkofer Institute , Munich , Germany , with the exception of donors 3 , 4 , 7 , and 8 , in whom it was determined using the HHV-6 IgG ELISA kit ( Abnova ) . Cell lines and cultures from these and other HLA-typed donors were used as antigen-presenting cells in T cell assays . Mini-lymphoblastoid cell lines ( mLCLs ) were generated by infection of PBMC with mini-Epstein-Barr viruses as described [47] . CD40-activated B-cell cultures were established as described [48] and maintained by weekly replating on irradiated ( 180Gy ) LL8 stimulator cells in the presence of 2 ng/ml rIL-4 ( R&D Systems ) . LL8 cells are murine L929 fibroblasts stably transfected with human CD40L [49] . Human embryonic kidney cells 293T ( partial HLA type: HLA-A*02:01 , B*07:02 ) were obtained from ATCC ( CRL-11268 ) . HHV-6-specific T cells were analyzed in peripheral blood samples from three adult patients after allo-HSCT . Transplant indication was severe aplastic anemia ( SAA ) in patients 1 and 3 , and acute myeloid leukemia ( AML ) in patient 2 . G-CSF-mobilized peripheral blood stem cells from an HLA-matched unrelated donor were used in patients 1 and 2; bone marrow donated by an HLA-matched unrelated donor was used in patient 3 . GvHD prophylaxis consisted of cyclosporine A plus sirolimus ( n = 2 ) or mycophenolate mofetil ( n = 1 ) . All patients and donors were HLA-B*08:01-positive and CMV-seronegative . Patients received standard antiviral prophylaxis with acyclovir . Viral infection/reactivation was monitored weekly by quantitative PCR in peripheral blood , including HHV-6 . Other specimens like stool , urine and throat swab samples were monitored for virus reactivation weekly on a routine basis as indicated . A detailed overview of the characteristics of patients , donors , and transplant procedures is provided ( Supporting S2 Table ) . Peptide sequences adhering to the HLA-B*08:01 anchor motif were extracted from the HHV-6B strain Z29 reference sequence ( GenBank NC_000898 ) using the text editor Tex-Edit Plus and a script in the AppleScript language . The 299 peptides of the HLA-B*08:01 candidate library were synthesized by JPT ( Berlin , Germany ) in a "Research Track" format . Each peptide was analyzed by liquid chromatography–mass spectrometry . Median purity of peptides was 77% . Nineteen peptides had a purity below 50% ( minimum 25 . 5% ) , none of these was recognized by any T cell clone . Peptides were reconstituted in 100% dimethyl sulfoxide ( DMSO ) and stored at –20°C . DMSO concentration in all T cell effector assays was kept below 0 . 1% ( vol/vol ) . PBMCs from HHV-6-positive donors were enriched for HHV-6B-specific T cells by stimulation with a mix of 146 octameric peptides or 153 nonameric peptides that represent HLA-B*08:01 candidate epitopes from HHV-6B , using a protocol employing autologous CD40-activated B cells . For peptide loading , PBMC ( first stimulation ) or CD40-activated B cells ( all later stimulations ) were coincubated with octamer or nonamer peptide pool ( 1 µg/ml for each peptide ) at 37°C for 2 h , and washed three times with PBS . The T cell stimulation protocol was initiated by peptide loading of PBMC , which were then plated at 5×106 cells in 2 mL per well of a 12-well plate . Ten to 14 days later , cells were pooled , counted using trypan blue staining , and restimulated at 3×106 cells in 2 mL medium per well with freshly irradiated ( 50 Gy ) autologous CD40-activated B cells , previously loaded with peptides , to reach an effector:stimulator ratio of 4:1 , in the presence of 25–50 U/mL recombinant IL-2 ( „Proleukin S“ , Novartis ) . Cells were restimulated every following week with peptide-loaded CD40-activated B cells in the same manner , with the exception that the IL-2 concentration was successively increased to 100 U/mL . Between stimulations , the T cell cultures were expanded using fresh IL-2-containing culture medium as seemed necessary , judging from the visual appearance of the cultures . For cytotoxicity analysis , PBMCs were stimulated with a single peptide ( DFK from U86 ) and autologous CD40-activated B cells in an analogous manner , and tested at day 29 of cultivation . For single cell cloning of polyclonal T cell cultures , 0 . 7 or 2 . 5 T cells/well were seeded into 96-well round-bottom plates , together with 2×104/well irradiated ( 50 Gy ) HLA-B*08:01-positive mini-LCLs loaded with the octameric or nonameric HHV-6B peptide pool , 3×105 cells/well of a mixture of irradiated ( 50 Gy ) allogeneic PBMCs from three donors , and 1000 U/mL IL-2 . Outgrowing T cell clones were expanded in 96-well round-bottom plates by restimulating every 2 weeks under equivalent conditions . Later , clones with known peptide specificity were restimulated in an analogous manner but using only the single specific peptide . HLA/peptide multimers in the form of phycoerythrin- ( PE ) -labeled HLA-B*08:01/peptide dextramers were purchased from Immudex , Copenhagen , Denmark . Dextramers contained one of thirteen HHV-6 peptides or the peptide RAK ( full sequence RAKFKQLL ) from the BZLF1 protein from Epstein-Barr virus . Dextramers covered the epitopes EAR , RSK , FEK , QTR , VVK , NVK , MAR , whose peptide sequences are identical in HHV-6A and HHV-6B; the epitopes TNK , EGR-6B and DFK from HHV-6B , which differ from their HHV-6A counterparts in one to three amino acids; the epitopes EGR-6A and EFK from HHV-6A ( EFK being the HHV-6A version of DFK ) ; and the HHV-6B epitope SPR , which has no HHV-6A counterpart . The sequences of HHV-6 peptides are provided in Fig 2 . For quantification of antigen-specific CD8+ T cells in peripheral blood from healthy donors using dextramers , a median of 7x105 PBMCs per staining was treated as follows . Cells were stained for 10 minutes at room temperature with 1 μl PE-labeled HLA/peptide dextramer . For negative controls , cells were processed identically , but dextramer was not added . After washing with PBS supplemented with 2% FCS , cells were counterstained on ice for 15 minutes with anti-CD4-FITC ( clone RPA-T4 ) , anti-CD3-PE-Cy5 ( clone HIT3a ) , and anti-CD8-APC ( clone RPA-T8 ) antibodies ( all BioLegend ) . Cells were then washed with PBS/FCS and resuspended in 1 . 6% formaldehyde ( Carl Roth ) in PBS for fixation , stored at 4°C , and analyzed within one day on a Becton Dickinson FACSCalibur flow cytometer . Data analysis was performed using FlowJo 9 . 5 . 3 software ( Tree Star ) : lymphocytes were gated in a forward/sideward scatter dot plot , then CD3+CD4– cells were analyzed for the proportion of multimer-positive cells within CD8+ T cells . For healthy donors 4 , 6 , 10 and transplantation patients , a variation of this protocol was used . Instead of anti-CD4-FITC , a "dump channel" mix of FITC-labeled antibodies anti-CD14 ( clone TÜK4 , Miltenyi Biotec ) , anti-CD19 ( clone LT19 , Miltenyi Biotec ) , and anti-TCR-γδ ( clone 5A6 . E9 , Life Technologies ) was used . Viable lymphocytes were gated according to forward/sideward scatter , and FITC-positive cells were excluded . For patient samples , only 3x105 PBMCs were used per staining . A range of differentiation markers was analyzed on DFK-specific T cells in donor 3 and 6 . Staining with dextramer DFK was combined with FITC-labeled anti-CD14 and anti-CD19 antibodies as above ( dump channel ) and CD3-Alexa Fluor 700 ( clone HIT3a , BioLegend ) ; additional antibodies in panel A were CD8-APC ( clone RPA-T8 ) , CCR7-PE-Cy7 ( clone G043H7 ) , and CD45RA-Pacific Blue ( clone HI100; all BioLegend ) ; additional antibodies in panel B were CD8-APC-H7 ( clone SK1 , BD ) , CD27-APC ( clone O323 , BioLegend ) , CD28-PE-Cy5 ( clone CD28 . 2 , BD Pharmingen ) , and CD57-Pacific Blue ( clone HCD57 , BioLegend ) . Dot plots displaying flow cytometry data in Figs 7 and 8 , S2 and S3 Figs span , in both dimensions , a range from 1 to 10000 arbitrary fluorescence units in a logarithmic scale . Data in S1 Fig are presented in a biexponential scale spanning a range from 10−3 to 105 arbitrary units in both dimensions . To verify the HLA restriction of HHV-6B-specific T cell clones , 293T cells were transfected with a HLA-B*08:01 expression plasmid ( kindly provided by Josef Mautner , Munich ) by calcium phosphate precipitation . Twenty-four hours later , cells were harvested , washed with PBS , loaded with single peptides at 1 µg/ml , washed three times , and used as targets in T cell assays . HHV-6B-specific T cell lines and T cell clones were analyzed for antigen-specific IFN-γ secretion in ELISA or ELISPOT assays . Effector cells ( 104 , unless noted otherwise ) were cocultivated overnight ( 16–18 h ) with target cells ( 2x104 , unless noted otherwise ) in 200 μL per well of a 96 V-well plate at 37°C and 5% CO2 . Then supernatants were harvested , and an IFN-γ ELISA was performed according to the manufacturer’s protocol ( Mabtech , Nacka , Sweden ) . IFN-γ ELISPOT assays were used to determine the frequency of specific T cells in freshly isolated PBMCs and polyclonal T cell lines . They were performed according to the reagent manufacturer’s protocol ( Mabtech , Nacka , Sweden ) in 96-well MultiScreen-HA plates ( Millipore ) in 200 μL medium per well , with an overnight incubation period of 16–18 hours at 37°C and 5% CO2 . To analyze PBMCs , 250 , 000 cells were distributed to each well and directly loaded with antigenic peptide . To analyze T cell lines , autologous CD40-stimulated B cells were loaded with antigenic peptides , washed , and co-incubated at 5x104/well together with the T cells at 10 , 000 cells/well . Spots were developed using the AP Conjugate Substrate Kit from Bio-Rad . Spots were counted in an automated ELISPOT reader ( CTL ) . To determine the cytotoxic activity of HHV-6B-specific T cells against HHV-6B-infected cells , calcein release assays were performed . HHV-6B-infected target cells ( see below ) were loaded with Calcein AM ( 5 μg/ml , Molecular Probes ) for 30 min at 37°C in standard medium . Cells were washed three times and resuspended in RPMI medium without phenol red supplemented with 5% FCS . Effector T cells were washed once and resuspended in the same medium . T cells and target cells were combined in V-bottom 96-well plates ( 200 μl total volume per well ) , with 5 , 000 target cells per well and 5 , 000–80 , 000 T cells per well ( effector: target ratio 1:1 to 16:1 ) , in four replicates of each condition . After 3 . 5 hours at 37°C and 5% CO2 , supernatants ( 100 μl per well ) were collected , and fluorescence was measured ( excitation 485 nm , emission 535 nm ) . Specific lysis was calculated relative to maximal lysis ( 100% , targets incubated with 0 . 5% Triton X-100 ) and minimal lysis ( 0% , targets incubated in the absence of T cells ) . HHV-6A ( strain U1102 ) and HHV-6B ( strain HST ) were purchased from NCPV , UK , and serially propagated on phytohemagglutinine ( PHA ) -activated cord blood mononuclear cells . Fresh or cryoconserved cord blood cells at 2x106 cells in 2 ml per well of a 24-well plate were stimulated with 5 μg/ml PHA-M ( Calbiochem ) . Three days later , cells were infected with virus suspension from previous passages ( 230 μl/well ) . After 5–7 days , when the cytopathic effect appeared maximal , cell cultures were harvested , cells were pelleted by centrifugation at 300 g for 10 min , and supernatants were stored in aliquots at -80°C . Peripheral blood cells from adult donors with known HLA types were used to prepare HHV-6A/B-infected target cells for the analysis of T cell recognition . CD4 T cells were positively isolated from PBMC using anti-CD4-coupled paramagnetic beads ( Miltenyi Biotec ) , and 2x106 CD4+ cells were activated in 2 mL per well of a 24-well plate using 5 μg/mL PHA . After 3 days , the cells were pooled , counted , replated at 2x106 cells/well , and infected with 230 μl/well of HHV-6A or HHV-6B virus stocks . Thereafter , infected T cell cultures were resupplied with fresh medium every 3 days on average . At different time points after infection , cells were used as targets for HHV-6-specific T cell clones in cytokine secretion assays . At every time point , infected cells were harvested , washed and counted in Trypan Blue solution immediately before they were combined with HHV-6-specific T cells at constant numbers ( 104 effector T cells , 2 × 104 infected cells or control targets ) . In selected experiments as indicated , ganciclovir ( 20 μg/mL , Roche ) was added immediately before infection .
Here we present a cross-sectional analysis of the CD8 T cell response to HHV-6 , and an overview of antigens recognized by this response . For one exemplary HLA class I molecule , HLA-B*08:01 , we identified candidate epitopes all across the HHV-6B proteome , and tested which of these represent bona fide epitopes . A large set of T cell clones was established to assess and correlate epitope specificity and antiviral reactivity with precision . Frequencies of specific T cells in healthy donors and allogeneic transplant patients were determined by multimer staining . A majority of peptides against which we could raise T cells were presented by infected cells , and epitopes from all classes of viral antigens were presented . Ex vivo frequencies of specific T cells were low for most epitopes . However , U86-specific T cells were readily detectable ex vivo in most donors and patients . U86 is thus a candidate for an immunodominant CD8 T cell antigen of HHV-6 . Moreover , we describe the presence of HLA-B*08:01-restricted HHV-6-specific T cells in patients who were able to control episodes of HHV-6 reactivation after stem cell transplantation . Taken together , the present work provides a cross-sectional overview of the structure of the HHV-6-specific CD8 T cell response at two levels . It shows that multiple viral antigens of different functional and kinetic classes furnish epitopes for T cell recognition; and it describes the quantitative contributions of the different specificities to the T cell repertoire , including identification of a prominent antigen . Our study extends earlier investigations of the HHV-6-specific CD8 T cell response that were limited to the analysis of responses to five pre-chosen proteins: four virion proteins ( U11 , U14 , U54 , U71 ) and the IE-1 transactivator U90 [18–21] . The motivation to choose those antigens was their correspondence to immunogenic proteins of human CMV . The present work employed a method that was independent of such criteria and targeted CD8 T cell epitopes across the viral proteome . HLA-B*08:01-restricted epitopes were identified in 12 proteins of varied function and from all phases of the viral replication cycle . No epitope was derived from any of the five antigens studied earlier , although 12 candidates from these proteins were included in our analysis . This suggests that immunity to HCMV antigens has limited power to predict the specificity of CD8 T cell responses to HHV-6 . We cannot exclude , however , that CD8 T cells that target additional epitopes , including such from the five proteins mentioned , may exist in the T cell repertoire . Of note , U86 attracted the strongest T cell responses among the antigens described here , and its CMV counterpart IE-2/UL122 is a strong CD8 T cell antigen [25] . This suggests that certain commonalities between recognition patterns of CMV and HHV-6 antigens may exist . However , the overall composition and diversity of the HHV-6-specific CD8 T cell repertoire , as characterized here , appears to stand in marked contrast to the best-studied herpesviruses , CMV ( a β-herpesvirus like HHV-6 ) and EBV ( a γ-herpesvirus ) . Contrary to HHV-6 , CMV elicits very large CD8 T cell responses , amounting to an average of 10% of the peripheral CD8 T cell repertoire of healthy carriers [25] . HLA-B*08:01-restricted T cells make a strong contribution to this response [23 , 50] . EBV-specific CD8 T cells account for a smaller proportion of total CD8 T cells in healthy donors [51] , but , for example , the HLA-B*08:01-restricted RAKFKQLL epitope ( Table 2 ) is recognized by a median of about 2% of CD8 T cells , and frequencies above 5% are no rarity [29 , 52] . CD8 T cell responses to CMV further increase in the elderly [53 , 54] , and EBV-specific CD8 T cells are strongly elevated in patients with symptomatic primary EBV infection [55] . On the other hand , the diversity of the CD8 T cell response to CMV or EBV appears restricted . For example , the database IEDB [56] currently lists only three HLA-B*08:01-restricted epitopes from CMV and four from EBV , counting strain variants as one epitope . In CMV carriers , a median of eight out of 213 ORFs is recognized by CD8 T cells [25] , and a majority of EBV antigens appear exempt from CD8 T cell recognition [51] . Thus , it appears that the diversity of epitopes and antigens available for presentation by infected cells is distinctly larger in HHV-6 than in the two paradigmatic human herpesviruses . However , it cannot be excluded that more diverse repertoires of low-frequency CD8 T cell specificities in EBV or CMV have so far escaped detection , possibly because their responses were masked by more dominant CD8 T cell populations . In contrast , CD8 T cells specific for other herpesviruses such as varicella-zoster virus ( VZV ) or herpes simplex virus ( HSV-1 ) are maintained at relatively low frequencies in healthy carriers [57–59] . In HSV-1 , CD8 T cells appear to target multiple antigens from different phases of infection , whereas IFN-γ responses to individual epitope peptides ex vivo have frequencies of 1 in 104 PBMCs or lower . A large number of potential epitopes from HSV-1 were described , with up to 13 sharing the same HLA class I restriction [57] , although it is not clear yet whether a majority of these is presented by infected cells . This structure of the T-cell repertoire appears comparable to the one described here for HHV-6 . Less is known about the VZV CD8 epitope repertoire , but available data are compatible with a highly diverse repertoire which is in part shaped by cross-reactivity of CD8 T cells to HSV and VZV [60] . Potential reasons for differentially structured antiviral CD8 T cell repertoires may be sought in the patterns of cellular tropism of these viruses . CMV resides latently in monocytes and myeloid precursors and is reactivated upon their differentiation to dendritic cells [61] , whereas EBV infects B cells in diverse activation states [62] . Infection of professional antigen-presenting cells by CMV and EBV may favour competitive clonal expansion and selection of immunodominant T cells into the repertoire [63 , 64] . In other herpesviruses , tropism for professional antigen-presenting cells is less predominant [5 , 65]—although HHV-6 was shown to infect monocytes and other antigen-presenting cells in vivo [5] . Also , it appears that the repertoire of viral immunoevasive molecules that directly interfere with steps in the HLA class I presentation pathway is larger for CMV or EBV [49 , 66 , 67] than for other herpesviruses [58 , 68] . Co-expression of many immunoevasive functions in CMV and EBV may limit the number of epitopes that escape such regulatory mechanisms [69 , 70] , and this may lead to competitive advantage and immunodominance of T cells that recognize their epitopes in the context of infection . Our analysis of T cell epitopes was limited to only one allotype , HLA-B*08:01 , and extrapolations to the CD8 T cell repertoire in general must be made with caution . More comprehensive studies on the entire CD8 T cell repertoire to HHV-6 will be necessary to strengthen our present suggestions . However , available information on CD8 T cell responses to other complex viruses indicates that HLA-B*08:01 , wherever studied , rarely fails to be an effective presenter of epitopes , as shown by the examples in Table 1 . The groove of MHC class I molecules accomodates peptides for presentation to CD8 T cells [71–73] . Particularly important for stable binding are certain anchor residues [74] whose side chains reach into dedicated pockets in the peptide-binding groove . Allelic variants of MHC class I demand anchor residues required for peptide binding that can differ in their chemical nature and their position in the peptide [27 , 33 , 74] . Our identification of HLA-B*08:01-restricted T cell epitopes consisted in a functional screen of all HHV-6B-derived peptides that contained a motif of three required anchor residues [27 , 75] , as depicted in Table 1 , while any amino acid was permitted in other positions of the peptide . Application of such a simple anchor-motif based algorithm ( SAMBA ) is supported by the observation that a majority of well-characterized , independently verified , and potent CD8 T cell epitopes from infectious pathogens perfectly adhere to this motif , whereas amino acid usage in all other positions is more variable ( see Table 1 and the references therein ) . Full conformity to this motif was also shown for abundant self-derived peptides eluted from HLA-B*08:01 in a seminal study [75] . Subsequent studies have , however , increasingly identified B*08:01-binding self peptides that partially deviated from the motif [76 , 77] . In these cases , peptides were eluted from cells co-expressing several HLA class I molecules , and their HLA-B*08:01 restriction was retrospectively inferred from the peptide sequence . Deviation from the classical motif was also observed when predicted B*08:01-restricted epitopes were validated in ex vivo ELISPOT assays with blood cells loaded with peptide [78] . However , such approaches carry the risk of identifying responses to peptides that are not endogenously processed [26] or not presented by the predicted HLA allotype [69] , if those aspects are not independently tested . Prediction of MHC class I epitopes currently relies on machine-learning algorithms trained on ever increasing datasets of MHC binders or epitopes [79 , 80] . To the extent that such datasets may contain a growing number of candidates whose HLA restriction and qualities as epitopes have not been verified , further progress in predicting optimal epitopes may be difficult to achieve . Therefore , in our view it is as important as ever to rigorously verify HLA restriction and endogenous presentation , optimally with target cells infected with the pathogen of interest . Reliance on T cell clones increases the accuracy of epitope identification and validation , since this ensures that the very same T cells recognize peptide and infected cells , and minimizes the likelihood of accidental cross-reactivities . Peptide-based functional screens of epitope candidates ex vivo have been successful in identifying CD8 T cell reactivities that were later confirmed to be viral epitopes , for example in CMV [23 , 54] , but such approaches are likely to be less robust when proportions of specific T cells are low , such as for HHV-6 . This study identified sixteen HLA-B*08:01-restricted HHV-6 epitopes–defined as peptides presented by infected cells–out of 299 candidates . We took advantage of this dataset to compare amino acid usage in internal and flanking sequences of epitopes and non-epitopes . In the C-terminal anchor position ( C1 ) , Leu appeared to be favoured among admitted aliphatic residues , clearly so in octameric epitopes . Leu was also enriched in the C2 position . Otherwise , no restrictions of amino acid usage in non-anchor positions in HHV-6 epitopes were apparent , which is in line with the idea of distinct functional roles for anchors and non-anchors , and retrospectively supported our use of a SAMBA approach . However , stronger enrichment of certain amino acids was found in peptide-flanking positions ( N2' , C1' , and C2' ) . The C terminus of most MHC I ligands is generated by the proteasome [81] . Cut site preferences of human proteasomes have been identified by in vitro digestion of model proteins [82–85] , and coincide well with the requirements of many MHC I allotypes ( such as HLA-B*08:01 ) to bind peptides with a bulky hydrophobic residue in the C-terminal position . Downstream of the cut site , amino acid preferences partially diverge between model proteins [82–85] . We found uncharged hydrophilic amino acids , particularly Ser , to be enriched in the C1' position ( called P1' in analyses of proteasome function ) . Ser in this position was also enriched after degradation of HIV Nef by the constitutive proteasome [83] and of prion protein by the immunoproteasome [84] . Increased frequency of Arg [83 , 85] and depletion of bulky hydrophobic amino acids [82 , 83 , 85] also agreed with our findings , whereas enrichment of Ala or Pro [82–85] did not . Although limited in size , our dataset suggests an influence of amino acid identity in C1'/P1' on effective proteasomal processing of HHV-6 epitopes . In the C2'/P2' position , we observed basic amino acids to be enriched; no clear tendency in that regard is found in the literature [84 , 85] . Formation of the N terminus of MHC I ligands is in many cases a complex multistep process comprising proteasomal degradation , processing by cytosolic aminopeptidases , TAP-mediated transport to the ER , and final trimming by ER aminopeptidases [86] . Nontheless , an N-terminal processing motif of MHC I ligands could be defined [86] . Consistent with our findings , this motif has the basic amino acids Lys and Arg somewhat enriched in the N2' position [86] . Basic amino acids in N-terminal overhangs of MHC I ligand precursors may favour processing by cytosolic or ER aminopeptidases [86] , although the ER peptidase ERAP1 does not appear to have this preference [87] . Moreover , basic amino acids close to the N terminus of MHC I ligand precursors may support effective TAP-mediated transport to the ER [88] . Thus , amino acid usage in regions flanking HHV-6 epitope peptides is compatible with some of the described N- and C-terminal MHC I processing preferences . However , such motifs represent tendencies rather than strict criteria , so improving epitope prediction by considering processing motifs remains difficult [79] . Nontheless , we speculate that simplified peptide-flanking motifs may be useful to design screening approaches that prioritize efficiency over completeness . Identification of multiple CD8 target antigens and epitopes as undertaken here will advance immune monitoring and immunotherapy of HHV-6 . Since our study identifies an epitope ( DFK from U86 ) that allowed detection of specific T cells ( sometimes at high frequencies of up to 1 . 1% ) in 7/8 healthy carriers and 3/3 patients after allo-HSCT , multimer staining based on this epitope will be a convenient tool for monitoring and monospecific approaches to antiviral T cell therapy [89 , 90] . HHV-6-specific T cell transfer after allo-HSCT is attractive and feasible . In patients who received allo-HSCT , HHV-6 reactivation and disease is associated with a lack of virus-specific T cells [10 , 91] and the use of transplantation procedures that lead to imperfect T cell reconstitution [92] . In a first clinical application of HHV-6-specific T cell transfer to allo-HSCT patients , T cells specific for U11 , U14 , and U90 were part of a protocol that employed multivirus-specific peptide-stimulated T cells derived from the transplant donor [12] . In two patients , HHV-6 reactivation was cleared after transfusion of multivirus-specific T cells , in connection with an emergence of HHV-6-specific T cells in peripheral blood [12] . Partial remissions of HHV-6 infection were also observed in a third-party approach based on similarly prepared T cells [93] . These promising initial results encourage further application and development of HHV-6-specific adoptive immunotherapy . Since multiple epitopes are targeted by HHV-6-specific CD8 T cells , a multiepitope approach [94] may be particularly promising for selection of effective HHV-6-specific T cells for immunotherapy . If TCR-transgenic T cell therapy [95] is considered , HHV-6 antigens from diverse functional classes may be suitable targets .
|
This paper deals with the immune response to a very common virus , called human herpesvirus 6 ( HHV-6 ) . Most people catch HHV-6 in early childhood , which often leads to a disease known as three-day fever . Later in life , the virus stays in the body , and an active immune response is needed to prevent the virus from multiplying and causing damage . It is suspected that HHV-6 contributes to autoimmune diseases and chronic fatigue . Moreover , patients with severely weakened immune responses , for example after some forms of transplantation , clearly have difficulties controlling HHV-6 , which puts them at risk of severe disease and shortens their survival . This can potentially be prevented by giving them HHV-6-specific "killer" CD8 T cells , which are cells of the immune system that destroy body cells harboring the virus . However , little is known so far about such T cells . Here , we describe 16 new structures that CD8 T cells can use to recognize and kill HHV-6-infected cells . We show that very different viral proteins can furnish such structures . We also observe that such T cells are regularly present in healthy people and in transplant patients who control the virus . Our results will help develop therapies of disease due to HHV-6 .
|
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"Results",
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"Discussion"
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2018
|
Cross-sectional analysis of CD8 T cell immunity to human herpesvirus 6B
|
The emergence of the Chikungunya virus ( CHIKV ) is currently expanding . In 2015 , 38 , 332 cases of Chikungunya were reported to the Brazilian epidemiological surveillance system . Eighteen months after notification of the first case in the city of Feira de Santana , we conducted the first serosurvey to define the magnitude of transmission in a rural community in Brazil . The serosurvey was conducted in a random sample of 450 residences in the Chapada district , located 100 kilometers from Feira de Santana . We administered questionnaires and tested 120 sera from Chapada district residents for CHIKV IgM- and IgG-specific antibodies . An individual with CHIKV infection was defined as any person with CHIKV IgM or IgG antibodies detected in the serum . One Hundred cases of Chikungunya were reported after prolonged rainfall , which reinforced the relationship between the rainfall index and CHIKV transmission . Eighteen months after the start of the outbreak , we identified a seroprevalence of 20% ( 95% CI , 15 . 4–35% ) . CHIKV IgG- and IgM-specific antibodies were detected in 22/120 ( 18 . 3% ) and 6/120 ( 5 . 0% ) individuals , respectively . Among seropositive patients , 13/24 ( 54 . 2% ) reported fever and joint pain over the previous two years ( p<0 . 01 ) . The rate of symptomatic CHIKV infection was 40 . 7% . We identified a moderate seroprevalence of Chikungunya in the Chapada district , and in half of the confirmed CHIKV infections , patients reported arthralgia and fever over the previous two years .
The Chikungunya virus ( CHIKV ) belongs to the genus Alphavirus of the family Togaviridae and has a single-stranded RNA genome and positive polarity [1] . The virus was first isolated in blood samples obtained during an epidemic of a "dengue-like" disease that occurred between 1952–1953 in Tanzania [2 , 3] . To date , four CHIKV genotypes have been identified , two of which were initially isolated in Africa: the East-Central-South-African genotype ( ECSA ) , the West African genotype , the Asian genotype and the more recently identified Indian Ocean Lineage [4 , 5] . Clinical disease manifestations emerge after an incubation period that lasts an average of 2 to 4 days . The first symptom is usually a high fever , followed hours later by myalgia and generalized arthralgia and arthritis , which are often incapacitating and accompanied by headache and back pain . Polyarthralgia/polyarthritis is usually bilateral and symmetrical and occurs more often in the hands , wrists , interphalangeal joints , feet and ankles but may also affect large joints , such as the shoulders and knees . Periarticular swelling is frequently observed [6 , 7] . A maculopapular rash and facial swelling are present in approximately 40 to 50% of patients [8] . In children , vesicobullous eruptions with intense subsequent flaking may occur , along with petechiae and gingivorrhagia . Ocular manifestations can also occur and generally achieve satisfactory resolution in six to eight weeks [6 , 9] . Chikungunya is usually self-limiting , with clinical manifestations regressing within a few weeks . However , in a percentage of those infected , which can vary from 30 to 40% , polyarthralgias may persist for months or even years [7] . Although Chikungunya is usually benign , there have been increasingly frequent reports associating Chikungunya with the decompensation of pre-existing diseases , especially heart , kidney and liver diseases , diabetes , hypertension and systemic lupus erythematosus , among others [10] . During the epidemics in the Caribbean and Americas between 2013 and 2014 , 65 patients were admitted to intensive care units ( ICUs ) with Chikungunya , of whom 54 had pre-existing illnesses , and 27 were admitted due to the exacerbation of comorbidities [11] . In October 2013 , autochthonous CHIKV circulation was found on the island of Saint Martin and spread to different countries and territories of the Caribbean and the Americas [12] . In Brazil , the first autochthonous cases of CHIKV were identified in the cities of Oiapoque , Amapá State and Feira de Santana , Bahia State , in September 2014 [4] . The Asian genotype , which had already been isolated in Caribbean and Central American countries and territories , was identified in the first location . In the city of Feira de Santana , the detected genotype was ECSA [4 , 13] . Between late 2013 and July 2016 , approximately two million cases were reported in more than forty countries and territories in the Americas and the Caribbean , with Brazil also having registered tens of thousands of cases [14] . A Chikungunya epidemic began in Riachão do Jacuípe , Bahia in the second half of 2014 [15] . Chapada is a district in Riachão do Jacuípe that reported a high number of cases in 2014 and 2015 . The Chapada district is a small , restricted geographic region with 100% basic health care coverage , which would facilitate the performance of a seroepidemiological survey . Therefore , to determine the prevalence of anti-CHIKV antibodies after the first wave of the epidemic , a serosurvey was conducted in the District of Chapada , a rural community of Riachão do Jacuípe .
Riachão do Jacuípe has an area of 1 , 190 . 215 km2 and is located 100 km from the city of Feira de Santana , where the first case was identified in Northeastern Brazil . It has an estimated population of 33 , 000 inhabitants and a Human Development Index ( HDI ) of 0 . 628 . The Chapada district is 17 km from the administrative headquarters of Riachão Jacuípe and has a population of 2 , 303 inhabitants [16] . The population is mainly rural , and farm work is the main economic activity of its inhabitants . This serosurvey was conducted in April 2016 . The Chapada district has a total of 505 households , with 2 , 303 individuals . Cluster sampling was performed , using the household as a sample unit . To calculate the sample size , we estimated a prevalence of 20% with a variation of ± 10 , 80% power , type I error of 5% , and a correction for survey design by an aggregate of 2 . Therefore , it was necessary to select 120 individuals , increasing this number by 20% for losses . We conducted a random selection of 45 households and selected 160 individuals to compose the sample . Blood samples were collected by venipuncture , and serum was separated by centrifugation ( 3 . 000 rpm/5 minutes ) . The serum was stored at—20°C until use . The search for anti-CHIKV IgM- and IgG-specific antibodies was performed using a commercial ELISA ( enzyme-linked immunosorbent assay ) according to the manufacturer's instructions ( Euroimmun , Lübeck , Schleswig-Holstein , Germany ) . An individual with CHIKV infection was defined as any person with IgM or IgG CHIKV antibodies detected in the serum . An individual was considered to have had symptomatic infection if he/she had experienced fever and arthralgia in the previous two years and if a test for IgM and/or IgG detection had yielded positive results . To calculate the symptomatic rate of Chikungunya , we subtracted the rate of individuals who had fever , arthralgia and negative CHIKV serology from the rate of individuals who had fever , arthralgia and positive CHIKV serology . Chikungunya is a mandatory reportable disease , and the reporting system relies on the notification of all suspected cases at public and private health facilities based on the attending clinician’s initial clinical diagnosis ( not laboratory confirmed ) . A suspected case is defined as illness in a person from an area of Chikungunya transmission or Aedes aegypti mosquito infestation who has symptoms of Chikungunya ( sudden onset fever greater than 38 . 5°C and arthralgia or acute arthritis , unexplained by other conditions ) . The Sistema Nacional de Informação do Ministério da Saúde ( SINAN ) is the Brazilian Ministry of Health's National Information System for entering and processing the data for reported Chikungunya cases throughout Brazil , and the notification rate is calculated by dividing the number of Chikungunya cases reported to SINAN by the total population . The interviews were conducted with the aid of a notebook . Data were entered directly into Research Electronic Data Capture ( REDCap ) , hosted at the Federal University of Grande Dourados [17] , and were analyzed using the statistical program SAS 9 . 2 ( SAS Institute , Cary , NC , USA ) . The incidence and prevalence of Chikungunya are expressed as percentages with 95% confidence intervals . Dichotomized and categorical data were analyzed with the chi-squared test . The following data were collected on the questionnaire: age , sex , educational attainment , previous fever or joint pain in the last two years and medical history of dengue , Chikungunya and Zika . The participant’s race/ethnicity ( i . e . , white , black , indigenous , Asian or mixed ) was self-reported . All eligible participants provided written informed consent prior to study participation . The study was approved by the Research Ethics Committee at the State University of Feira de Santana ( CAAE study number: 49946515 . 6 . 0000 . 0053 . Institutional review board approval number: 1 . 450 . 762 ) .
The first suspected case of Chikungunya in the Chapada district was reported in October 2014 . By October 2015 , over 100 cases had been reported , with 35% serologically confirmed and 30 anti-CHIKV IgM-positive cases and 5 anti-CHIKV IgG-positive cases ( Fig 1 ) . The notification rate over the one-year period was 4 . 3% ( 95% CI , 3 . 5%-5 . 3% ) , and all of the reported cases involved joint pain and fever . The notification rate in in the Chapada district was similar from the notification rate over the same period in Riachão do Jacuípe ( 6 . 7%; 95% CI , 6 . 4%-7 . 0% ) . In the serosurvey , of the 160 individuals selected , 120 were present in their homes and agreed to participate in the study ( Fig 2 ) . Randomly recruited individuals did not significantly differ from the general population of the Chapada district with respect to age and gender ( S1 Table ) . Differences in the age and sex of the individuals who agreed and declined to participate in the study were not observed . Most individuals were female , 62/120 ( 51 . 7% ) , and young , with a mean age of 36 . 6±20 . 9 , were black , 45/117 ( 38 . 5% ) , or of mixed race , 69/117 ( 59 . 0% ) , and were not working , 30/120 ( 25% ) , at the time of the survey . Of the individuals evaluated , 15/120 ( 12 . 5% ) reported a previous clinical diagnosis of Chikungunya , and 44/120 ( 36 . 7% ) and 30/120 ( 25 . 0% ) reported at least one episode of fever or joint pain , respectively , in the previous 2 years ( Table 1 ) . Among the 120 individuals evaluated , 22/120 ( 18 . 3% ) were anti-CHIKV IgG-positive , and 06/120 ( 5 . 0% ) were anti-CHIKV IgM-positive . Three samples were borderline on the test used . Among the six patients who had CHIKV-specific IgM , four ( 66 . 7% ) also showed CHIKV specific-IgG antibodies , for an overall prevalence of 20% ( 95% CI , 15 . 4–35 . 7% ) ( Table 2 ) . Among the patients with positive serology , 16/24 ( 66 . 7% ) suffered fever episodes , and 13/24 ( 54 . 2% ) reported joint pain and fever over the previous two years ( p<0 . 01 ) . Among 96 patients with negative serology , 13 ( 13 . 5% ) had fever and joint pain . According to the criteria defined for this study , the rate of symptomatic CHIKV infection was 40 . 7% . Furthermore , 54 . 2% reported having had a clinical diagnosis of Chikungunya ( p<0 . 01 ) ( Table 3 ) .
This report describes the first serosurvey in a rural community in Northeastern Brazil . The seroprevalence found in the Chapada district was 20% , in other serosurveys , the prevalence of anti-CHIKV antibodies has ranged between 10 . 2% and 75% [18 , 19] . The difference between the prevalence found in this study and those reported by other serosurveys may be associated with local environmental conditions and/or vector competence in transmitting the ECSA strain of CHIKV , which was introduced in Brazil in 2013 [13] . In addition , the interval between the time the serosurvey was conducted and the peak of outbreak could have affected the prevalence rates reported in different cross-sectional studies [18 , 20 , 21] . The Chapada district has a population of 2 , 303 , and during the year in which the outbreak occurred , 100 Chikungunya cases were reported . The attack rate found in the Chapada district may eventually be applied to Riachão do Jacuípe and other cities that present climatic conditions and a similar Aedes infestation index in Brazil . Based on the number of notifications and serosurvey results , we can estimate that for every reported case , 1 . 9 cases of symptomatic CHIKV infection were not reported during the outbreak , demonstrating the difficulty that the surveillance system faces in identifying suspected Chikungunya cases . Nevertheless , the underreporting rate is still much lower than that recorded for dengue , wherein for each case reported , 12 are not reported in the Brazilian Ministry of Health's National Information System [22] . The Chapada district is located in a semi-arid region of Bahia , 100 km from Feira de Santana , where the first case of Chikungunya was identified in Brazil [4] . Interestingly , the peak in Chikungunya outbreak notifications occurred in September 2015 and was preceded by a very rainy period between April and June 2015 . This condition may have been crucial to the reproduction and multiplication of the Aedes vector and may consequently have affected the occurrence of the outbreak in 2015 . It must be taken in account that environmental factors are important in the occurrence of Chikungunya outbreaks . Studies conducted on the island of Saint Martin [18] and in Feira de Santana [23] and mathematical modeling studies conducted in American and Asian countries clearly demonstrate the relationship between temperature , rainfall and the increase in the number of Chikungunya notifications [24] . The majority of randomly selected individuals were unemployed , children and retirees ( 61 . 7% ) , and half of the population had less than four years of schooling 44 . 2% ( 53/120 ) . Of the 24 positive cases , 54 . 2% ( 13/24 ) reported significant arthralgia in the previous two years , and the rate of symptomatic Chikungunya infection was 40 . 7% . Chikungunya epidemics have become a serious public health problem , especially in the poorest communities , where health care systems are more precarious . The broad spectrum of post-Chikungunya musculoskeletal and rheumatic disorders , which can evolve chronically and are often disabling , [25] compromise the local work force and affect the family incomes of these most vulnerable populations . Arthralgia is one of the Brazilian Ministry of Health’s clinical criteria for case definition . Despite the fact that all 100 reported patients during the outbreak experienced this symptom , only 54 . 2% of patients with CHIKV-specific antibodies in the serosurvey reported the presence of this symptom in the previous 2 years , and 54 . 2% reported having had a clinical diagnosis of Chikungunya . Similar frequencies of patients without arthralgia have also been described in the ECSA lineage outbreaks that occurred in Thailand , Kenya and Saint Martin [18 , 21 , 26] . Asymptomatic and oligosymptomatic patients are fundamental to the maintenance of a local epidemic . There is always the possibility of recall bias in serosurveys . However , Chikungunya-induced arthralgia is usually marked; thus , we believe that information obtained retrospectively would not have suffered from any study design effect . This study has important limitations inherent to its design . A major example of these limitations is that 40 individuals ( 25% ) were not at home , which can lead to selection bias , as demonstrated by the higher proportions of women , children and elderly . Nonetheless , we observed no differences in relation to age and sex between the individuals who agreed and declined to participate in the study and compared to the general population . In summary , the incidence of CHIKV infection during an outbreak in the Chapada district was 20% ( 95% CI 15 . 4–35 . 7% ) . The outbreak was preceded by a 3-month rainy period , which is unusual for this district and which may have affected the reproduction and multiplication of the Aedes vector . In addition to the substantial suffering associated with persistent arthralgia , the outbreak in this community of rural workers likely had a significant economic impact on the region .
|
The Chikungunya virus ( CHIKV ) epidemic is currently expanding . In 2015 , 38 , 332 cases of Chikungunya were reported to the Brazilian epidemiological surveillance system . To date , no serosurvey has described the impact of the disease on rural communities in Brazil . We conducted a survey in which households in the Chapada district , located in Riachão do Jacuípe in Bahia , were randomly selected for serum collection to perform a CHIKV antibody test . More than 100 cases of Chikungunya were reported in the Chapada district following a three-month period of rain . Eighteen months after the start of the outbreak , we identified that 20% of the population had acquired CHIKV , and 13/24 ( 54 . 2% ) reported fever and joint pain over the previous two years . The rate of symptomatic CHIKV infection was 40 . 7% . We identified a moderate seroprevalence of Chikungunya in the Chapada district , and half of the patients reported arthralgia and fever over the previous two years .
|
[
"Abstract",
"Introduction",
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"Results",
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2017
|
Seroprevalence of Chikungunya Virus in a Rural Community in Brazil
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As cells grow and divide under a given environment , they become crowded and resources are limited , as seen in bacterial biofilms and multicellular aggregates . These cells often show strong interactions through exchanging chemicals , as evident in quorum sensing , to achieve mutualism and division of labor . Here , to achieve stable division of labor , three characteristics are required . First , isogenous cells differentiate into several types . Second , this aggregate of distinct cell types shows better growth than that of isolated cells without interaction and differentiation , by achieving division of labor . Third , this cell aggregate is robust with respect to the number distribution of differentiated cell types . Indeed , theoretical studies have thus far considered how such cooperation is achieved when the ability of cell differentiation is presumed . Here , we address how cells acquire the ability of cell differentiation and division of labor simultaneously , which is also connected with the robustness of a cell society . For this purpose , we developed a dynamical-systems model of cells consisting of chemical components with intracellular catalytic reaction dynamics . The reactions convert external nutrients into internal components for cellular growth , and the divided cells interact through chemical diffusion . We found that cells sharing an identical catalytic network spontaneously differentiate via induction from cell-cell interactions , and then achieve division of labor , enabling a higher growth rate than that in the unicellular case . This symbiotic differentiation emerged for a class of reaction networks under the condition of nutrient limitation and strong cell-cell interactions . Then , robustness in the cell type distribution was achieved , while instability of collective growth could emerge even among the cooperative cells when the internal reserves of products were dominant . The present mechanism is simple and general as a natural consequence of interacting cells with limited resources , and is consistent with the observed behaviors and forms of several aggregates of unicellular organisms .
As unicellular organisms grow and divide , they often form a crowded aggregate . As exemplified by bacterial biofilms [1–3] and slime molds [4 , 5] , these aggregates are not merely crowded passively , but sometimes form a functional cell aggregate , in which cells strongly interact with each other by exchanging chemicals , as demonstrated with quorum sensing [6] . Such a “multi-cellular aggregate” is often observed to form under a limited resource condition , which may indicate that formation of an aggregate is a universal strategy for a unicellular organism to survive in a severe environment and for cells to grow collectively and cooperatively . Interestingly , mutualistic behaviors , cell differentiation , and division of labor are ubiquitously observed in such aggregates with isoclonal cells [1–3 , 7–9] as well as with heterogeneous cells ( e . g . , bacterial ecosystem ) [1–3 , 10–12] . This raises the questions of how aggregates of identical cells achieve division of labor for cooperative growth , and what are the necessary conditions ? These questions are important to be addressed in order to understand the formation of multicellular aggregates , including the formation of biofilms , which has attracted much attention recently [1–3] . From this point of view , there are at least three characteristics required to achieve stably growing aggregates with division of labor . To achieve stable task differentiation , ( i ) cell differentiation through cell-cell interaction would be necessary , whereas ( ii ) cooperative growth is also required , since otherwise community formation through cell-cell communication would not be advantageous ( or might even be deleterious ) compared with the case of isolated cells without any interaction . However , simply achieving this cooperative growth does not necessarily imply that this state is robust , since if one cell type reproduces faster than any other type , the fastest type would dominate the population and the appropriate cell type ratio for division of labor would be easily lost . Therefore , ( iii ) coexistence of diverse cell types is also an important issue to be addressed for the stability and survival of a cell colony . Indeed , such characteristics have also been studied as a primitive form of multicellularity . In experimental evolution , aggregation of unicellular organisms with division of labor has been recently investigated with the use of yeast [13] and algae [14] . With respect to theoretical approaches , a related issue of the survival of an aggregate of cells has been conventionally discussed in multi-level evolution theory , by introducing a fitness parameter at the cellular and multi-cellular levels , and investigating how these two fitness values are aligned [15–21] . In most of the previous studies based on the prescribed fitness , however , the existence of differentiated cell types is presumed , and thus the capacity of cell differentiation and the fitness alignment are separated [15–23] . Related criticisms of these previous approaches are discussed in [24 , 25] . Specifically in [24] , intracellular dynamics is introduced as the optimization of resource allocation to different tasks under a given artificial fitness function , and it is shown that division of labor emerges when it increases the fitness . However , in nature , in general , cell differentiation does not result from optimization of a given fitness but rather results from intra-cellular metabolic reaction dynamics , and thus the division of labor is not guaranteed even when it increases fitness . The fitness , i . e . , the rate of cellular growth , is also obtained through the reaction dynamics . Therefore , it will be important to take cell differentiation and growth rate into account simultaneously , as a result of intra-cellular reaction dynamics , where the growth rate of each cell type is not predetermined , but rather changes according to the cellular states . Furthermore , this growth state also depends on the states of surrounding cells , which may alter the abundances of available resources and the strength of cell-cell interactions . To consider these issues that have not been addressed in the previous studies , we here present a dynamical-systems model of cells with intracellular reactions , cell-cell interactions , and uptake of resources , by which the fitness is determined as the cellular growth rate , rather than being prescribed in advance . In fact , such models of interacting and growing cells with intracellular reaction dynamics have been introduced previously , where the concept of isologous diversification [26] has been proposed , to address differentiation from a single cell type ( property ( i ) ) . A previous mathematical model [27] demonstrated that an ensemble of cells sharing a common genotype could achieve niche differentiation through cell differentiation , and thereby relax the strength of resource competition . Although this indirect cooperation through niche differentiation would be beneficial for cell aggregates , we here address cooperative growth in a stronger sense , where differentiated cells help each other so that interacting cells in an aggregate grow faster than the isolated undifferentiated cells ( unicellular organisms ) under the condition of limited resources ( property ( ii ) ) . For this purpose , we here consider an environment in which only a single resource exists , and in such situation , property ( ii ) is considered as the property of the cell ensemble to help each other achieve a higher growth than the isolated cells , rather than specializing to each resource . In the present paper , by using a simple model of cells that contain diverse components and interact with each other through the exchange of chemicals , we address the question of whether the above three characteristic behaviors are a necessary outcome of an ensemble of interacting cells . Specifically , we show that a cell ensemble under strong cell-cell interactions with limited resources fulfills cell differentiation , cooperative growth , and robustness in the cell type distribution .
We consider a mathematical model proposed in [26–29] , which describes a simple , primitive cell that consists of k chemical components {X0 , … , Xk−1} . As illustrated in Fig 1 , we assume that n cells globally interact with each other in a well-mixed medium , and each of them grows by uptake of the nutrient chemical X0 , which is supplied into the medium from the external environment . The internal state of each cell is characterized by a set of variables ( x 0 ( m ) , … , x k - 1 ( m ) , v ( m ) ) , where x i ( m ) is the concentration of the i-th chemical Xi , and v ( m ) is the volume of the m-th cell ( m = 1 , … , n ) . As a simple model , we consider a situation with only catalysts and resources , where these k components are mutually catalyzed for their synthesis , thus forming a catalytic reaction network . A catalytic reaction from a substrate Xi to a product Xj by a catalyst Xl , as Xi + αXl → Xj + αXl , occurs at a rate ϵxi ( m ) xl ( m ) α , where α refers to the order of the catalytic reaction and is mostly set as α = 2 . Here , ϵ is the rate constant for this reaction , and , for simplicity , all the rate constants are equally fixed at ϵ = 1 . The parameters and variables in this model are listed in Table 1 . Cell states change through intracellular biochemical reaction dynamics and the in- and outflow of chemicals , leading to cell-cell interactions via the medium . The medium’s state is characterized by concentrations ( x 0 ( m e d ) , … , x k - 1 ( m e d ) ) , and a constant volume Vmed . Then , the dynamics of the concentration of Xi in the m-th cell are represented as: d x i ( m ) d t = ∑ j , l = 0 k - 1 ϵ P ( j , i , l ) x j ( m ) x l ( m ) α - ∑ j , l = 0 k - 1 ϵ P ( i , j , l ) x i ( m ) x l ( m ) α + D σ i ( x i ( m e d ) - x i ( m ) ) - x i ( m ) μ ( m ) , ( 1 ) where P ( i , j , l ) takes the value 1 if there is a reaction Xi + αXl → Xj + αXl , and is 0 otherwise . In Eq ( 1 ) , the third term describes the influx of Xi from the medium , and the fourth term gives the dilution owing to the volume growth of the cell , and μ ( m ) denotes the cellular growth rate . Here , only a subset of chemical species is diffusible across the cell membranes with the rate of diffusion D . Xi is transported from the medium into the m-th cell at a rate D σ i ( x i ( m e d ) - x i ( m ) ) , where σi is 1 if Xi is diffusible , and is 0 otherwise . Therefore , the m-th cell grows in volume according to the rate μ ( m ) ≡ ∑ i = 0 k - 1 D σ i ( x i ( m e d ) - x i ( m ) ) by assuming that the cellular volume is in proportion to the total amount of chemicals . The volume dynamics are given by dv ( m ) /dt = μ ( m ) v ( m ) . As the abundances of chemicals are conserved by the intracellular reactions , with this form of volume growth , ∑ i = 0 k - 1 x i ( m ) = 1 is time-invariant [28] . The nutrient chemical X0 , which is necessary for cellular growth , is supplied into the medium from the external environment according to the rate D m e d ( C - x 0 ( m e d ) ) , where Dmed denotes the diffusion coefficient of the nutrient across the medium’s boundary , whereas C is the constant external concentration of the nutrient X0 ( for simplicity , the flow of the other diffusible chemicals to the outside of the medium is not included , although its inclusion does not alter the result below as long as their Dmed values are not large ) . Therefore , the temporal change of x i ( m e d ) is given by d x i ( m e d ) d t = D m e d σ 0 ′ ( C - x i ( m e d ) ) - ∑ m = 1 n D σ i ( x i ( m e d ) - x i ( m ) ) v ( m ) V m e d , ( 2 ) where σ 0 ′ takes unity only if i = 0 , i . e . , if Xi is the nutrient . For simplicity , Dmed was set as Dmed = D , though the results reported here do not greatly depend on the value of Dmed . According to these processes , each cell grows by converting nutrient chemicals into non-diffusible chemicals and storing them within the cell until its volume doubles , and then divides into two cells with almost the same chemical compositions . Here , the catalytic network in daughter cells is identical to that in their mother cell . As the initial condition , only a single cell exists with a randomly determined chemical composition . In addition , we set the carrying capacity of a medium N , which is an upper limit to the number of cells that can coexist in the medium . When the cell number exceeds its upper limit N due to cell division , the surplus cells are randomly eliminated . Hereafter , this model is referred to as the N-cell model .
We simulated the N-cell model over hundreds of randomly generated reaction networks . Each catalytic network is generated in the following manner . Each chemical is set to be diffusible with probability q = 0 . 15 and has ρ = 4 outward reaction paths to other chemicals; i . e . , each chemical works as a substrate in ρ reactions . Each reaction Xi + αXl → Xj + αXl ( i ≠ j , and Xj and Xl are not nutrients ) is randomly determined so that j ≠ l is fulfilled . We did not allow for autocatalytic reactions ( j = l ) as they are rare in nature . However , the described results were also obtained without these restrictions . We are particularly interested in if and how the cells differentiate , and whether the growth rate would increase as a result of differentiation . For this purpose , cell differentiation is defined as the emergence of cells with different chemical compositions within the population that share an identical catalytic network . For the case where the concentrations asynchronously oscillate in time , we evaluated whether cells have different compositions even after taking the temporal average over a sufficiently longer time scale than the oscillation period . To evaluate the growth enhancement , we compared two different situations , “interacting” ( N = 100 ) and “isolated” ( N = 1 ) cases , and then we computed Rμ , the ratio of the growth rate of interacting cells to that of isolated cells . Then the growth enhancement is defined as Rμ > 1 . The behavior of the N-cell model is classified into four categories . In category ( a ) , interacting cells differentiate into two or more types and grow faster than isolated cells , i . e . , Rμ > 1 ( Fig 2; see also Figure A in S1 Text ) In category ( b ) , interacting cells differentiate but their growth is slower than that of isolated cells ( Rμ < 1 ) ; in this category , as far as we have examined , cells of a certain type gain chemicals diffused from another type , which are used as catalysts for conversion to non-diffusible chemicals . The latter cell type has a composition similar to that of the isolated cell , and its growth is decreased by this cell-cell interaction ( see Figure B in S1 Text ) . Hence , the former cell type is considered to exploit the latter as it receives the unidirectional chemical inflow . In category ( c ) , cells do not differentiate with respect to the average composition , but chemical concentrations asynchronously oscillate in time . In category ( d ) , the behavior of each cell is identical , regardless of the presence or absence of cell-cell interactions , and therefore Rμ = 1 . Here , we are mainly concerned with category ( a ) , as this case enables both cell differentiation and cooperative growth . We found four common properties in this category . ( 1 ) A state with homogeneity among cells becomes unstable as the cell number increases , and is replaced by two ( or more ) distinct cellular states . ( 2 ) In differentiated cells , the compositions are concentrated for only a few chemicals , whereas the concentrations of the other chemicals are nearly zero; i . e . , each cell type uses only a sub-network of the total reaction network . ( 3 ) Different cell types share only a few common components , and each of the other components mostly exists in one cell type . ( 4 ) The components that predominate in one cell type diffuse to the other cell type , where they function as catalysts , and vice versa . Thus , the two cell types help each other to achieve higher cooperative growth . After examining a number of networks in category ( a ) , we extracted a common core structure in the reaction network topology , designated as networks 1-3 ( Fig 3A and 3B; see also Figure C in S1 Text ) . In these networks , cells in the N-cell model differentiate into two types , type-1 and type-2 , as exemplified in Fig 3C . In type-1 , x1 is high while x2 is close to zero , and in type-2 , x2 is high and x1 is close to zero . Accordingly , X3 ( X4 ) can be produced only in the former ( latter ) type , and the two types of cells complement each other by exchanging X3 and X4 . Consequently , the differentiated cells grow faster than the isolated cells ( Fig 3D ) . To analyze the mechanism of this cooperative differentiation , we reduced the N-cell dynamics to two effective groups of cells represented by ( x 0 ( i ) , … , x k - 1 ( i ) , v ( i ) ) , where v ( i ) denotes the total volume of each cell group ( i = 1 , 2 ) . Considering that the total cell number is sustained at its maximum N , the total cellular volume is also bounded . Therefore , v = v ( 1 ) + v ( 2 ) is regarded as a constant in the reduced version of interacting cells , termed the reduced-2cell ( r2cell ) model . This model obeys Eqs ( 1 ) and ( 2 ) with μ ( m ) ≡ ∑ i = 0 k - 1 D σ i ( x i ( m e d ) - x i ( m ) ) , and the effect of random cell elimination associated with cell division is implicitly incorporated into dilution due to volume growth . Besides , by considering the symmetry in networks 1-3 , we can assume v ( 1 ) = v ( 2 ) = v/2 for symmetric differentiation with the same number of cells of the two types , while the case with v ( 1 ) ≠v ( 2 ) will be discussed later . Likewise , we also consider the reduced-1cell ( r1cell ) model corresponding to the “isolated” case of the N-cell model , by ignoring cell division and assuming that the cellular volume is constant at v ( iso ) = v . The behavior of the r1cell and r2cell models ( i . e . , isolated and interacting cells ) can be classified into several phases , depending on parameters ( C , V , D ) , where V≡Vmed/v is the volume ratio between the cells and the medium . The phase diagram with network 1 for D = 1 is shown in Fig 4A , and Figure E in S1 Text shows phase diagrams of networks 1-3 for various D values . The blue area in Fig 4A represents phase ( I ) , in which the cells cannot differentiate , and always reach a single fixed point attractor in both the r1cell and r2cell models . In phase ( II ) , differentiation into two fixed points occurs in the r2cell model from a stable fixed point in the r1cell model , as shown in Fig 4B . In phase ( III ) , the r1cell model exhibits oscillation , while two cells in the r2cell model reach two distinct fixed points ( Fig 4C ) . In terms of dynamical systems theory , this loss of oscillation is referred to as oscillation death [30 , 31] . In phase ( IV ) , both “oscillation-death” differentiation and synchronous oscillation ( i . e . , non-differentiation ) can occur depending on the initial condition , whereas the r1cell model always exhibits oscillation . Thus , differentiation occurs in phases ( II ) - ( IV ) ( i . e . , at the left of the green line in Fig 4A ) , while stable differentiation without falling into synchronized oscillation is achieved only in phases ( II ) - ( III ) , i . e . , with small C and V values , representing a limited resource and strong cell-cell interaction condition . In Fig 4A , phases ( II ) - ( III ) are divided by the red line , and the red and green lines are determined according to linear stability analysis ( see S1 Text for details ) . With respect to the network structure , the catalytic reactions X1 + αX2 → X5 + αX2 and X2 + αX1 → X5 + αX1 function as two mutually repressive reactions , i . e . , forming a double-negative feedback loop . Further , the product X5 consumes X1 and X2 , and is maintained within the cell , which enhances the dilution of X1 and X2 . Thus , each of these reactions works as a composite negative feedback loop , leading to instability of the homogeneous cell state . Since nonlinearity is a necessary condition for multi-stability , a high order of catalytic reactions α tends to facilitate cell differentiation . Fig 5A shows the dependence of Rμ on parameters in network 1 , exemplifying that differentiation increases the growth rate . Surprisingly , this differentiation-induced growth enhancement was always observed for any set of parameters in networks 1-3 ( network 3 is shown in Figure C in S1 Text ) . We next sought to determine the mechanism contributing to the faster growth of differentiated cells . An intuitive explanation is as follows . On one hand , an isolated cell must contain all chemical components required for self-reproduction ( e . g . , X0-X5 in the upper panel of Fig 3A ) , leading to lower concentrations of each chemical on average . On the other hand , differentiated cells can achieve division of labor; each type of differentiated cell exclusively produces a portion of the required chemical species , and cells exchange these chemicals with each other . Since catalytic reactions occur only in a sub-network of the original network ( e . g . , a network in the lower panel of Fig 3A ) , the chemicals are concentrated on fewer components , which increases the efficiency of chemical reactions and promotes cellular growth . This suggests that stronger cell-cell interactions support higher growth . Indeed , Fig 5B shows that a smaller V , i . e . , stronger cell-cell interaction , causes larger Rμ . A smaller V also increases Rp , the ratio of the total production of X3-X4 in the r2cell model to that in the r1cell model ( Fig 5C ) ; that is , the production of exchanged chemicals is enhanced . To conclude , stronger cell-cell interactions reinforce the division of labor , whereby differentiated cells can grow more efficiently . The rate of growth enhancement through cell differentiation Rμ can be roughly estimated by recalling that the growth rate of a cell is given by the average influx of the nutrient chemical it receives . We compared the growth rate of an isolated cell μ ( iso ) to that of a differentiated cell μ ( dif ) by assuming that the concentration of each chemical species is equally distributed , except for the nutrient chemical . Considering that an isolated cell has a catalytic network with k chemical components and q reaction paths from the nutrient X0 , each concentration of X1-Xk−1 is calculated as x ( i s o ) = ( 1 - x 0 ( i s o ) ) / ( k - 1 ) . Hence , for the steady state , the growth rate is estimated by μ ( i s o ) = q x 0 ( i s o ) x ( i s o ) α / ( 1 + x 0 ( i s o ) ) . On the other hand , the sub-network in a differentiated cell is considered to have k′ chemicals and q′ reaction paths from the nutrient ( k′ < k , q′ < q ) . Then , each chemical concentration and the growth rate are given by x ( d i f ) = ( 1 - x 0 ( d i f ) ) / ( k ′ - 1 ) and μ ( d i f ) = q ′ x 0 ( d i f ) x ( d i f ) α / ( 1 + x 0 ( d i f ) ) , respectively . Here , we also assume that x 0 ( i s o ) ≃x 0 ( d i f ) , because these concentrations mostly depend on the supplied nutrient concentration C rather than on the internal dynamics of individual cells . From these assumptions , the growth ratio Rμ≡μ ( dif ) /μ ( iso ) is calculated as Rμ = ( q′/q ) [ ( k − 1 ) / ( k′ − 1 ) ]α . For example , with network 1 or 2 ( Fig 3A and 3B ) , k = 6 , q = 4 , k′ = 4 , q′ = 2 , and thus Rμ = ( 1/2 ) ( 5/3 ) α , which is greater than unity , at least when α≥2 . Although this estimate is not strictly accurate , it nevertheless demonstrates how cell differentiation can enhance cellular growth , which is facilitated by greater α . Even when the chemical concentrations were non-uniform , division of labor could accelerate growth when α was sufficiently large . The cells in our models achieved ( i ) cell differentiation and ( ii ) cooperative growth . However , if one cell type grows faster than the other type , the cooperation between the differentiated cells collapses . Thus , the third condition is necessary: the growth rate of each cell type needs to be in conformity , through mutual regulation by cell-cell interactions . Thus far , we have considered the case with equal populations of the two cell types by imposing the condition v ( 1 ) = v ( 2 ) . Here , we examine the case with v ( 1 ) ≠v ( 2 ) for fixed v ( 1 ) and v ( 2 ) , to evaluate whether the increases in cell volume ( or number ) are identical between the two types to meet the requirement ( iii ) . Therefore , Fig 6A and 6B show plots of the growth rate versus r ( 1 ) , where r ( i ) ≡v ( i ) / ( v ( 1 ) + v ( 2 ) ) is the volume proportion between type-1 and type-2 cells . Now , let us denote the dependence of μ ( 1 ) on r ( 1 ) by a function F ( r ( 1 ) ) . Then , the growth rate of the type-2 cell μ ( 2 ) is given by G ( r ( 2 ) ) = G ( 1−r ( 1 ) ) , which is equal to F ( 1−r ( 1 ) ) due to symmetry in the catalytic network . Since differentiated cells help each other , balanced growth would be expected; if the volume or relative number of one cell type is larger than the other , a larger ( smaller ) amount of chemicals would be supplied from the majority ( minority ) type to the minority ( majority ) type , so that the growth rate of the minority type is enhanced compared to that of the other type . This is the case for network 2 , where F ( r ( 1 ) ) < F ( 1−r ( 1 ) ) for r ( 1 ) > 1/2 , and the difference in volume ( or number ) decreases over time , leading to a balanced cell distribution ( Fig 6B ) . Nonetheless , this is not always the case . Fig 6A shows that the growth rate of the majority type is larger than that of the minority type in network 1; that is , F ( r ( 1 ) ) > F ( 1−r ( 1 ) ) for r ( 1 ) > 1/2 . Accordingly , the difference in volume increases over time , and thus the two different cell types cannot stably coexist . This instability of collective growth differs from a scenario of parasitic behavior , because μ ( tot ) ≡r ( 1 ) μ ( 1 ) + r ( 2 ) μ ( 2 ) is higher than μ ( iso ) for almost the entire range of r ( 1 ) . The condition for the stability is analytically expressed as follows . First , the temporal change of r ( 1 ) is represented by dr ( 1 ) /dt = r ( 1 ) ( 1−r ( 1 ) ) [F ( r ( 1 ) ) − F ( 1−r ( 1 ) ) ] , which has a trivial fixed point solution r ( 1 ) = 1/2 . This fixed point , where the two cell types coexist , is unstable if F′ ( 1/2 ) > 0 , and is stable if F′ ( 1/2 ) < 0 . To estimate F′ ( 1/2 ) , recall that d x i ( m ) / d t = 0 is fulfilled in a cell with steady growth , and Dmed V ≫ D . Then , from the definition of the growth rate μ , we get F ′ ( 1 / 2 ) D ≃ - ∂ x 0 ( 1 ) ∂ r ( 1 ) - 1 2 ∑ i = 1 k - 1 σ i ∂ ( x i ( 1 ) - x i ( 2 ) ) ∂ r ( 1 ) r ( 1 ) = 1 2 . ( 3 ) Since the first term is always negative as described in S1 Text , Eq ( 3 ) shows that if the difference in exchanged chemicals between the majority and minority cells [∑ i = 1 k - 1 σ i ( x i ( 1 ) - x i ( 2 ) ) ] increases in proportion to the increase in volume ratio of the majority type , then F′ ( 1/2 ) is negative and thus the collective growth is balanced . Now , let us consider how the difference in volume alters the states of two interacting cells in networks 1 and 2 . When r ( 1 ) > 1/2 , the population ratio of type-1 cells is increased , and the amount of X4 supplied from the minority type-2 cells is not sufficient for the majority type-1 cells to maintain their differentiated chemical composition . In contrast , the minority type-2 cells receive sufficient amounts of X3 from the majority type-1 cells , and maintain their differentiated composition . Consequently , the chemical composition of the type-1 cells approaches that of the isolated case , which contains X1 and X2 equally . This indicates that the majority of type-1 cell produces more X5 than the minority of type-2 cell does , and thus ∂ ( x 5 ( 1 ) - x 5 ( 2 ) ) / ∂ r ( 1 ) > 0 holds around r ( 1 ) = 1/2 . Therefore , the diffusibility of X5 contributes to the stability of network 2 , and the non-diffusibility of X5 contributes to the instability of network 1 . Intuitively , this mechanism can also be explained as follows: with network 1 , the majority cell type can produce a greater fraction of a non-diffusible chemical X5 for itself and a smaller fraction of a diffusible chemical X3 or X4 for the other cell type , and thus the majority cell type grows faster than the minority cell type . The stability and instability of collective growth are also observed in the original N-cell model . With an “unstable” network 1 , the N-cell model repeats the following dynamic behavior , as shown in Fig 6C: the medium is dominated by cells of one type , and cells of the minority type become extinct . Then , their differentiated compositions cannot be maintained with a single cell type , leading to de-differentiation . Thus , the coexistence of differentiated cells is temporally regained . In contrast , in a “stable” network 2 , the two differentiated cell types stably coexist and their growth is balanced ( Fig 6D ) , in which a perturbation to increase the population of one cell type leads to a decrease in the growth rate of that type .
In this paper , we have shown that an aggregate of identical cells achieves metabolic division of labor , with strong cell-cell interactions under limited resources . We have revealed how ( i ) cell differentiation , ( ii ) growth enhancement , and ( iii ) robustness in a cell population can be simultaneously self-organized without assuming the ability of differentiation a priori , in a simple system consisting only of intracellular reaction dynamics and cell-cell interactions through chemical diffusion . First , cells sharing a common genotype ( i . e . , an identical reaction network and identical parameters for reaction and diffusion ) differentiate into several types with different chemical compositions as a result of the instability of a homogeneous cell state induced by cell-cell interactions . This differentiation is facilitated under a condition of limited resources and strong cell-cell interactions , given a high order of catalytic reactions . This dynamical-systems mechanism has also been proposed in previous studies using models of metabolic networks [27] and gene regulation networks [32] . Second , the differentiated cells can achieve cooperative division of labor . The explanation of the division of labor given in the Results section can be simply sketched as follows . Let us consider two reactions in the r2cell model , X0 + αX1 → X3 + αX1 and X0 + αX2 → X4 + αX2 , with x1 + x2 = c . If x1 = x2 = c/2 , the total production rate of X3 and X4 is 2x0 ( c/2 ) α . In contrast , if the concentrations are biased either to X1 or X2 due to differentiation , x1 ∼ c ( or x2 ∼ c ) , the total production rate of X3 and X4 per cell is x0cα , which is 2α−1 times greater than that of the previous isolated , generalist cell . Thus the higher the order of the reaction α , the greater the advantage of division of labor . A more precise argument is given in the Results ( ii ) . This growth enhancement due to division of labor is clearly distinguishable from a scenario of relaxation of the competition for resources through niche differentiation reported previously [27] , in which the growth rate is not increased relative to that of an isolated cell . In our model , the growth rate can be enhanced by concentrating chemicals on one of the modules in the network , while the other module is necessary for catalyzing the reaction . A related mechanism for division of labor was proposed by Michod and colleagues ( see also [21 , 24 , 33 , 34] for discussion on the trade-off for division of labor ) . In the framework of Michod et al . , the convexity of the trade-off function is important for division of labor , and the condition of ( q′/q ) [ ( k − 1 ) / ( k′ − 1 ) ]α > 1 in our model may be related to the convexity of the trade-off function . Although the proposed model describes the division of chemical production among a cell aggregate , the above mechanism can be seen as analogous to the theory for division of labor in economics: the theory of comparative advantage proposed by Ricardo [35] states that the mutual use of surplus from a different country is more advantageous than producing all necessary resources in a single country , unless the transport cost is too high . In this sense , Ricardo’s theory parallels the present mechanism , because two cell types help each other by exchanging the products that are necessary to the other cell type . Indeed , our mechanism works best when cell density is high , so that chemicals are easily exchanged without much loss within the medium . Note , however , that in Ricardo’s theory , trade is assumed to occur between countries that differ in their relative ability of producing multiple goods , in contrast to the current model in which cells share an identical chemical network . With regard to this point , a better comparison would be with Taylorism [36] , which refers to increases in a group’s productivity by each member specializing to each task without assuming individuals with different abilities [26] . Remarkably , this cooperative differentiation is not sufficient to satisfy condition ( iii ) , robustness in the number distribution of differentiated cells . If one cell type begins to dominate the population , production of the chemicals needed by the minority type will increase . Thus , a feedback mechanism to reduce the majority population is expected . However , if the fraction of non-diffusible chemicals is increased for the majority cell type , this storage of chemicals within a cell would suppress the supply of chemicals for the other cell types . Consequently , the majority cell type would further increase its population . This suggests that to achieve a balanced population state , the mutual transport of necessary chemicals must work efficiently beyond any possible increase in internal reserves . From this perspective , it may be interesting to consider a possible economic analogy: reducing internal reserves and sharing a higher degree of wealth will be relevant to the stabilization of groups with division of labor . We here stress that this instability of cooperation can emerge only when cells simultaneously achieve differentiation and division of labor . In theoretical studies for multi-level evolution , a game theory approach has been sometimes adopted to address the evolution and dynamics of conflict between individuals and society . Although our approach differs from game theory , it might be worth discussing our result in light of this perspective . From a game theory perspective , the cellular growth rate is regarded as a measure of fitness or score . Hence , when two cell types stably coexist in network 2 , stable Nash equilibrium is achieved at r ( 1 ) = 1/2 . In contrast , in network 1 , no stable equilibrium exists for 0 < r ( 1 ) < 1 , and only unstable Nash equilibrium exists at r ( 1 ) = 1/2 , and thus one type dominates the population . Interestingly , after extinction of one type , re-differentiation of the remaining cells into two types increases the fitness ( i . e . , growth rate ) for both types , as shown in Fig 6A . This dominance of one cell type and re-differentiation are repeated as a result of symbiotic growth and differentiation due to the instability of a homogeneous cell society . We expect that such dynamic behavior will be observed in an artificial symbiosis experiment with Escherichia coli and diffusible amino acids [37 , 38] . Considering the difference between networks 1 and 2 , the stability and instability of the system can be switched by even a slight change in the diffusibility of a single chemical species . This implies that slight epigenetic changes and transcriptional errors occurring during the lifetime of an organism can lead to instability in the cell distribution , which may relate to the phenomena of metamorphosis during development and carcinogenesis . Our results demonstrate that an aggregate of simple cells consisting only of catalytic reactions and the diffusive transport of chemicals can fulfill differentiation with division of labor , collective growth with symbiotic relationship , and stability . Note that these basic characteristics in our model emerge without a fine-tuned mechanism , and are facilitated by the conditions of strong cell-cell interactions , limited resources , and a high order of catalytic reactions . From this point of view , it is interesting to compare the present results to some characteristics of multicellular aggregates . First , filaments of the cyanobacterium Anabaena are known to differentiate , with each cell metabolically specializing in photosynthesis or nitrogen fixation , enabling more efficient growth [39] . Second , such cell differentiation with metabolic division of labor in some cyanobacteris occurs in response to combined nitrogen limitation [40 , 41] . Third , the biofilm of Bacillus subtilis exhibits metabolic co-dependence between interior and peripheral cells by chemical oscillation [9] , suggesting the relevance of nonlinear dynamics and cell-cell interactions for differentiation . In contrast to the present model of symbiotic growth , however , it has been pointed out that most multicellular aggregates and organisms have achieved division of labor between reproductive and non-reproductive cells throughout evolution [42] . Nevertheless , at some developmental stage of multicellular aggregates , symbiotic growth of different cell types may be expected to exist , by achieving differentiation and functional division of labor for biofilm formation [43 , 44] . Furthermore , in our model , whether or not both types of differentiated cells reproduce strongly depends on the conditions . For example , in network 1 , the growth rate is different between the major and minor cell types . Depending on the parameters , there are also cases in which one cell type would cease growing . In addition , we considered the symmetric differentiation case for clarity , but if the reaction rates are different by different chemicals ( which are natural ) , the growth rates of differentiated cell types could generally be different . Further , if we assume that cellular growth is determined by a certain chemical ( e . g . , X1 in networks 1-3 ) rather than by the total amount of chemicals , after differentiation , one cell type will be reproductive , and the other non-reproductive , while maintaining functional division of labor . Interestingly , characteristics ( i ) - ( iii ) can be part of the requirements for multicellularity . Thus , such characteristics may provide a primitive step to the evolution of multicellular organisms , which has been a topic of much attention from both theorists and experimentalists over the last few decades [15 , 27 , 45–50] . In this context , our results are also related to the experimental emergence of multicellularity from unicellular organisms [13 , 14] . However , the three characteristics may not be sufficient for the emergence of multicellular organisms . For example , besides the metabolic division of labor , multicellular organisms ubiquitously show germ-soma differentiation and apoptosis . Therefore , determining how the cell aggregates with metabolic division of labor considered here might achieve this universal property of multicellularity remains an important issue to be addressed .
|
Unicellular organisms , when aggregated under limited resources , often exhibit behaviors akin to multicellular organisms , possibly without advanced regulation mechanisms , as observed in biofilms and bacterial colonies . Cells in an aggregate have to differentiate into several types that are specialized for different tasks , so that the growth rate should be enhanced by the division of labor among these cell types . To consider how a cell aggregate can acquire these properties , most theoretical studies have thus far assumed the fitness of an aggregate of cells and the ability of cell differentiation a priori . In contrast , we developed a dynamical-systems model consisting of cells without assuming predefined fitness . The model consists of catalytic-reaction networks for cellular growth . By extensive simulations and theoretical analysis of the model , we showed that cells growing under the condition of nutrient limitation and strong cell-cell interactions can differentiate with distinct chemical compositions . They achieve cooperative division of labor by exchanging the produced chemicals to attain a higher growth rate . The conditions for spontaneous cell differentiation and collective growth of cells are presented . The uncovered symbiotic differentiation and collective growth are akin to economic theory on division of labor and comparative advantage .
|
[
"Abstract",
"Introduction",
"Models",
"Results",
"Discussion"
] |
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"cell",
"physiology",
"organismal",
"evolution",
"symbiosis",
"cell",
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"metabolism",
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2016
|
Symbiotic Cell Differentiation and Cooperative Growth in Multicellular Aggregates
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Intestinal parasitic nematodes such as hookworms , Ascaris lumbricoides , and Trichuris trichiura are amongst most prevalent tropical parasites in the world today . Although these parasites cause a tremendous disease burden , we have very few anthelmintic drugs with which to treat them . In the past three decades only one new anthelmintic , tribendimidine , has been developed and taken into human clinical trials . Studies show that tribendimidine is safe and has good clinical activity against Ascaris and hookworms . However , little is known about its mechanism of action and potential resistance pathway ( s ) . Such information is important for preventing , detecting , and managing resistance , for safety considerations , and for knowing how to combine tribendimidine with other anthelmintics . To investigate how tribendimidine works and how resistance to it might develop , we turned to the genetically tractable nematode , Caenorhabditis elegans . When exposed to tribendimidine , C . elegans hermaphrodites undergo a near immediate loss of motility; longer exposure results in extensive body damage , developmental arrest , reductions in fecundity , and/or death . We performed a forward genetic screen for tribendimidine-resistant mutants and obtained ten resistant alleles that fall into four complementation groups . Intoxication assays , complementation tests , genetic mapping experiments , and sequencing of nucleic acids indicate tribendimidine-resistant mutants are resistant also to levamisole and pyrantel and alter the same genes that mutate to levamisole resistance . Furthermore , we demonstrate that eleven C . elegans mutants isolated based on their ability to resist levamisole are also resistant to tribendimidine . Our results demonstrate that the mechanism of action of tribendimidine against nematodes is the same as levamisole and pyrantel , namely , tribendimidine is an L-subtype nAChR agonist . Thus , tribendimidine may not be a viable anthelmintic where resistance to levamisole or pyrantel already exists but could productively be used where resistance to benzimidazoles exists or could be combined with this class of anthelmintics .
Thirteen neglected tropical diseases have tremendous impact on the lives of billions of the poorest peoples in the world with an estimated total disease burden of 56 . 6 million disability-adjusted life years , exceeding that of malaria ( 46 . 5 million ) and tuberculosis ( 34 . 7 million ) [1] , [2] . These diseases play a major role in keeping infected peoples mired in poverty and in a low socioeconomic state [1] , [2] . The top three of these poverty-promoting tropical diseases are caused by intestinal nematodes: ascariasis ( caused by Ascaris lumbricoides ) , trichuriasis ( caused by Trichuris trichiura or whipworm ) , and hookworm disease ( caused by Necator americanus and Acylostoma duodenale ) . These parasites ( hookworms , Ascaris , and Trichuris or HAT ) are amongst the most common human parasitic infections , with an estimated 576–740 million people infected with hookworms , 807–1221 million infected with Ascaris , and 604–795 million infected with Trichuris [3] . Extensive and detrimental impacts of HAT infections have been reported on human growth , nutrition , fitness , stature , metabolism , cognition , immunity , school attendance/performance , earnings , and pregnancy [3] , [4] , [5] , [6] . A recent and thorough meta-analysis of deworming studies in children demonstrated that deworming children in areas for which HAT parasites are prevalent results in statistically significant improvements in almost all primary outcome measures ( weight , height , mid-upper arm circumference , and triceps skin fold ) and in all secondary outcome measures ( e . g . , weight-for-age , height-for-age , … ) [5] . Although HAT infections are one of the most prevalent and important infectious diseases in the world , few treatment options exist . The World Health Organization ( WHO ) has approved two classes of compounds ( anthelmintics ) for treatment of intestinal nematode parasites: the benzimidazoles ( i . e . , mebendazole and albendazole ) and the nicotinic acetylcholine receptor ( nAChR ) agonists ( i . e . , levamisole and pyrantel ) [7] . For practical reasons ( e . g . , efficacy against hookworm , single dose application , weight-independent dosing ) , only one drug , albendazole , is the drug of choice for Mass Drug Administration [7] , [8] . Given the limited number of drugs available , the enormous numbers of people to be treated , and the necessity for repeated treatment due to high reinfection rates and population dynamics of the parasites , the emergence of resistance to existing anthelmintics ( already an enormous problem for veterinary anthelmintics [9] ) poses a serious threat to large-scale deworming efforts . Thus there have been urgent and repeated calls for the development of new human anthelmintics [6] , [7] , [10] . In the past 30 years , only one new anthelmintic to treat human HAT infections has reached the clinic , tribendimidine . Tribendimidine , a symmetrical diamidine derivative of amidantel , is a broad-spectrum anthelmintic drug developed by the Chinese National Institute of Parasitic Diseases during the 1980s [11] . It was approved for human use by the China State Food and Drug Administration in 2004 and is currently undergoing clinical testing in China [11] , [12] . Laboratory and clinical investigations demonstrate that this drug is safe and has a broad spectrum of single-dose activity against parasitic nematode infections in humans , including against Ascaris , hookworms and Strongyloides stercoralis with reported cure rates of 92–96% , 52–90% , and 55% respectively [11] , [12] . A phase IV clinical trial of tribendimidine recently has been conducted in China [13] . In addition to intestinal nematode infections , tribendimidine has also shown in vivo efficacy against trematodes and tapeworms [12] , [14] . Tribendimidine is an important new drug with broad anti-parasite activity . Although tribendimidine is a promising new anthelmintic , virtually nothing is known about its mechanism of action , about whether or not nematodes can develop resistance to it , and , if so , about the molecular mechanism ( s ) associated with resistance . Such information is vital for understanding whether tribendimidine represents a new type of anthelmintic , for predicting how resistance might develop , for monitoring resistance in the field , and for determining how to rotate/combine it with other anthelmintics . Although the required mechanistic and resistance studies are difficult to conduct with parasitic nematodes , they can readily be carried out using the laboratory nematode , Caenorhabditis elegans . C . elegans has a rapid life cycle , is susceptible to most known anthelmintics , and is amenable to mutagenesis , large-scale forward genetic screens , genetics , and relatively quick gene mapping and cloning . As such , C . elegans has been used to discover and/or clarify the mechanisms of action and resistance of almost all known anthelmintics [15] . Here we demonstrate that C . elegans is susceptible to tribendimidine and that C . elegans mutants resistant to tribendimidine can readily be isolated . Detailed studies of tribendimidine-resistant and other anthelmintic resistant mutants demonstrate that tribendimidine unambiguously is a member of the nAChR class of anthelmintics of the same subtype as levamisole and pyrantel .
C . elegans strains were cultured using standard techniques including the use of Escherichia coli strain OP50 as standard food source [16] . The following strains were used for tribendimidine resistant mutants ( trb ) outcrossing , chromosome mapping , and complementation testing: Bristol N2 , dpy-5 ( e61 ) , dpy-11 ( e224 ) , and Hawaiian mapping strain CB4856 . The following levamisole-resistant mutant alleles were used: lev-1 ( e221 ) , unc-29 ( e293 ) , unc-38 ( e264 ) , unc-74 ( e883 ) , unc-63 ( x13 ) , lev-8 ( x15 ) , lev-9 ( x16 ) , unc-50 ( e306 ) , unc-22 ( e66 ) , unc-22 ( s12 ) , lev-10 ( x17 ) , lev-11 ( x12 ) . In addition , the aldicarb resistant mutant unc-10 ( e102 ) and the levamisole-insensitive nicotinic acetylcholine receptor mutant acr-16 ( ok789 ) were also used . The strain PD4793 is a strain of C . elegans with various green fluorescent protein ( GFP ) markers integrated on chromosome V . Tribendimidine was provided by the National Institute of Parasitic Diseases and Chinese Center for Disease Control and Prevention ( Shanghai , China ) . Levamisole and pyrantel were prepared from powder from Acros ( cat . no . 187870100 ) and Sigma ( P7674 ) , respectively . A stock solution of tribendimidine at 4 mg/mL was prepared in 1% DMSO in sterile distilled water for all assays . For all plate and well assays , the final concentration of DMSO was ≤0 . 1% , which both others and we have found has no detectable effect on C . elegans , health , movement or development ( [17] , Y . H . and R . V . A . , unpublished data ) . Levamisole and pyrantel were freshly dissolved in sterile distilled water . The chemical structures of all three drugs , tribendimidine , levamisole , and pyrantel , are shown in Figure S1 . The recipe for NG and ENG plates can be found in [18] . Special S medium ( sS medium ) is a modification of standard S medium used for C . elegans liquid culturing [19] in which the pH has been raised to 7 . 3 and CaCl2 has been omitted ( we found that tribendimidine is mostly inactivated at pH 6 . 0 , the pH of regular S medium; furthermore CaCl2 precipitates at pH 7 . 3 , hence the requirement that it be omitted ) . We have quantitatively confirmed that C . elegans health , development , movement , and brood sized are not affected by using sS medium in place of S medium . A large population of synchronized 4th larval stage ( L4 ) worms was mutagenized in a 30 mM ethyl methanesulfonate ( EMS ) as per standard protocol [19] . The mutagenized P0 animals were grown on OP50-seeded ENG plates at 20° overnight until gravid adults . F1 embryos were isolated from these adults using standard bleaching protocols [18] . After hatching overnight at 25° in M9 medium [19] , the F1 L1 larvae were plated and grown on OP50-seeded ENG plates at 20° for 3 days until gravid adults . These adults were bleached to produce F2 embryos and then hatched overnight in M9 to produce F2 L1 larvae . These F2 L1 larvae were plated onto ENG plates and grown until the L4 stage at 20° , at which point they were washed off the plates , rinsed in sS medium , and then pipetted into 48-well plates at a density of 20–30 worms/well along with 60 µg/mL tribendimidine , 20 µL OP50 ( OD600 = 3 . 0 in sS medium ) , and sS medium up to 200 µL final volume . Tribendimidine-exposed worms were then incubated at 15° overnight . Any nematodes that were motile ( i . e . , resistant to tribendimidine-induced paralysis ) were then transferred out of the wells and grown on NG plates ( minus drug ) to produce progeny . Progeny from these putative candidates were then placed onto NG plates in which tribendimidine ( from the 4 mg/mL stock; see above ) was added to a final concentration of 100 µg/mL just prior to pouring of the plates . Of 15 putative candidates identified initially , ten were reconfirmed on these tribendimidine plates . To ensure independence of mutants isolated , we screened only 7 , 600 F2 animals out of a total F2 population of 152 , 000 ( which came from a population of mutagenized 25 , 300 F1 ) for an estimated 7 , 600 mutagenized F1 genomes screened . The tribendimidine resistant mutants were outcrossed as follows: trb-1 ( ye492 ) was outcrossed six times using a combination of wild-type N2 , dpy-5 ( e61 ) , and dpy-11 ( e224 ) ; trb-2 ( ye493 ) was outcrossed six times using a combination of N2 and dpy-5 ( e61 ) ; trb-3 ( ye494 ) was outcrossed six times using a combination of N2 and dpy-11 ( e224 ) ; and trb-4 ( ye494 ) was outcrossed three times using N2 . In addition , the unlinked double mutants trb-1 ( ye492 ) ;dpy-11 ( e224 ) , dpy-5 ( e61 ) ;trb-2 ( ye493 ) , and trb-3 ( e494 ) ;dpy-11 ( e224 ) were obtained . To do complementation tests among trb mutants , homozygous or heterozygous males from outcrossed strains were obtained and these were mated into trb;dpy double mutant animals or trb-4 ( ye495 ) animals that on their own are uncoordinated ( Unc ) . More than 10 cross-progeny ( non-Dpy or non-Unc animals ) from each cross were placed onto 100 µg/mL tribendimidine toxin plates at 25° for 24 hrs and scored for either 100% or 50% resistance , depending upon whether homozygous or heterozygous males were used . To test for complementation between trb mutants and levamisole resistant mutants , we crossed homozygous PD4793 GFP males into each of the following levamisole resistance mutants: lev-1 ( e211 ) , lev-8 ( x15 ) , lev-9 ( x16 ) , lev-10 ( x17 ) , lev-11 ( x12 ) , unc-29 ( e293 ) , unc-38 ( e264 ) , unc-50 ( e306 ) , unc-63 ( x13 ) , unc-74 ( e883 ) , unc-22 ( e66 ) , and unc-22 ( s12 ) . Heterozygous males were then crossed into trb-1 ( ye492 ) , trb-2 ( ye493 ) , trb-3 ( ye494 ) , or trb-4 ( ye495 ) animals . For each of these crosses , 20 GFP cross-progeny were each plated onto either 1 mM levamisole or 100 µg/mL tribendimidine plates ( levamisole plates were prepared using a 100 mM stock of levamisole in sterile distilled water ) . The matching of levamisole and trb genes was determined by resistance of half of the cross-progeny on both tribendimidine and levamisole plates . Unambiguous results were obtained as described in the text . For gene mapping , each trb mutant was mapped to specific chromosomes and subregions using CB4856 and single-nucleotide polymorphisms [20] . trb-1 was mapped near the middle arm of chromosome I , trb-2 was mapped to the middle region of chromosome X , trb-3 was mapped to the left arm of chromosome III , and trb-4 was mapped to the middle region of chromosome IV . For detecting molecular changes of trb alleles in specific levamisole resistance genes , we used the polymerase chain reaction ( PCR ) to amplify DNA or cDNA isolated from various trb mutant animals with the coding region of specific levamisole resistance genes ( Table 1 ) . Pfu Ultra HS HF DNA Polymerase from Stratagene ( USA ) was used for these amplifications . All the sequence results were confirmed with three independent PCR reactions and double-stranded sequencing . Since the unc-22 gene is very large , we did not sequence in this case . Instead , we did the complementation tests between two different unc-22 alleles ( e66 and s12 ) and all three trb-4 alleles ( ye495 , ye496 and ye497 ) . To examine gut morphology , individual L4 hermaphrodites were individually picked using an eyelash into wells as described above for resistance screening except tribendimidine was used at 100 µg/mL . The animals were incubated for 24 hours at 25° , pipetted onto an agarose pad with 3 mM sodium azide as an anesthetic , visualized with 600× Nomarski optics on an Olympus IX70 microscope with a 60× PlanApo lens ( 1 . 4 NA ) , and photographed with a cool SNAP HQ2 camera ( PhotoMetrics , Inc , USA ) . For measuring dose-dependent developmental inhibition , we pipetted into the wells of a 48-well plate approximately 20 L1 nematodes , 20 µL OP50 ( OD600 = 3 . 0 ) , 20 µL drug , and a total volume of 200 µL ( sS medium is used as the dilutant for all reagents ) . Each well contained a specific dose of drug and that dose was repeated for a total of three times per experiment . The microtiter plate was then wrapped in damp paper towels , placed inside a covered plastic box , and incubated at 20° for 60 h . The number of nematodes that did/did not reach gravid adulthood ( harboring one or more eggs in their uterus ) were tallied for each well . The experiment was independently repeated three times . A mortality assay was used to determine dose-dependent mortality of nematodes exposed to drugs for 6 days at 25° . From these data the LC50 , the concentration at which 50% of the nematodes are dead , was calculated . Death was defined as worms that failed to respond to touch , were very pale , and had lost most internal structures . The LC50 assay with ∼20 L4 animals per well in sS medium was set up as previously described [18] , with the exception that different strains were allowed to grow for different amounts of time at 20° from the L1 to L4 stage prior to testing on drugs in order to reflect slight differences in their growth rates relative to N2 wild type: trb-4 ( ye495 ) , lev-1 ( e211 ) , lev-11 ( x12 ) , and unc-22 ( e66 ) mutant nematodes were allowed to develop for 48 hr and trb-1 ( ye492 ) , trb-2 ( ye493 ) , trb-3 ( ye494 ) , lev-8 ( x15 ) , lev-9 ( x16 ) , lev-10 ( x17 ) , unc-29 ( e293 ) , unc-38 ( e264 ) , unc-50 ( e306 ) , unc-63 ( x13 ) , and unc-74 ( e883 ) were allowed to develop for 45 hours ( N2 wild-type animals were used at 44 hours as previously described ) . To calculate 64 h brood sizes , individual L4 worms were picked up with an eyelash and placed in sS medium in a 48-well plate containing 40 µL OP50 ( OD600 = 3 . 0 ) and a specific dose of tribendimidine . The total volume in each well was 200 µL . Each drug concentration was repeated in five wells per experiment . The plates were incubated for 64 h at 25° . The progeny were then transferred out of the well with a pipette onto an empty NG agar plate for counting . For complete brood sizes of various strains in the absence of drug , individual L4 wild-type or trb hermaphrodites were picked onto individual OP50-seeded NG plates . Every two days , each adult hermaphrodite was shifted to a new NG plate until it stopped producing offspring . The progeny from the old plates were counted the next day . LC50 values and associated 95% confidence intervals were calculated using the PROBIT algorithm ( from XLSTAT add-on to EXCEL ) . Dose-response curves were plotted using Prism 5 ( GraphPad Software Inc . , La Jolla , CA ) . For brood size data , statistical analyses were carried out using Prism 5 , as were pair-wise comparisons between groups via one-way analysis of variance ( ANOVA ) and Tukey's HSD test .
Since there were no previous reports of the effects of tribendimidine on C . elegans , we incorporated the drug into standard nematode growth plates at 100 µg/mL and exposed the nematode to the drug at 25° for 24 hour . Under these conditions , the nematodes become paralyzed , although they are all still alive based on their coloration and the fact that they continue to lay eggs . The vast majority of these animals are coiled up and contracted ( Figure 1A ) ; a few are contracted but not coiled up . When placed in liquid media at the same concentration , wild-type C . elegans rapidly become straightened; only the extreme ends of the animal are able to move . After 24 h exposure to drug , most of the animals become coiled and immobile as on plates , although they are still alive since they lay eggs and will respond to direct touch or vigorous shaking of the microtiter plate . When these animals are mounted for observation at higher magnification , their internal morphology has degenerated , and damage to multiple tissues is evident , including shrinkage of the intestine away from the body wall ( Figure 1B , C ) . The neuromuscular system is probably also damaged based on the motility defects described above . To quantify the effects of tribendimidine on C . elegans , we performed a number of quantitative assays . First , we examined the response of C . elegans to tribendimidine based on what percentage of L1 larvae are able to develop to the gravid adult stage at varying doses of the drug ( Figure 2A ) . We find that C . elegans demonstrates a well-behaved , dose-dependent response to tribendimidine with regards to inhibition of larval development ( Figure 2A ) , with an IC50 ( inhibitory concentration at which 50% of the larvae are unable to complete development at these conditions ) of 18 . 4 µg/mL ( 95% confidence interval 16 . 2–22 . 3 µg/mL ) . Next , we placed C . elegans L4 animals in wells at varying concentrations of the drug and assayed for mortality after 6 days at 25° . We find that C . elegans demonstrates a well-behaved , dose-dependent response to tribendimidine with respect to mortality ( Figure 2B ) . The LC50 value ( concentration at which half the animals are dead ) is 54 . 4 µg/mL ( Table 2 ) . As discussed below , we also found that tribendimidine is able to produce a dose-dependent decrease in C . elegans progeny production . A forward genetic screen was carried out to find C . elegans mutants resistant to tribendimidine ( see Materials and Methods for details ) . After screening 7 , 600 mutagenized F2 animals , a total of ten resistant animals were identified that bred true in subsequent generations . Initial identification and confirmation of resistance were based on the fact that all were motile and healthy at concentrations of tribendimidine that paralyze and intoxicate wild type . Complementation testing among these ten different alleles revealed they fell into four groups that we called trb-1 ( five alleles ) , trb-2 ( 1 allele ) , trb-3 ( 1 allele ) , and trb-4 ( 3 alleles ) ( trb for tribendimidine resistant ) . All trb mutants are clearly resistant to tribendimidine intoxication . In contrast to wild-type animals , trb animals exposed to tribendimidine display a healthy body morphology ( Figure 1C ) similar to that of wild-type animals unexposed to the anthelmintic ( Figure 1B ) . To quantitatively demonstrate resistance , we measured the ability of wild-type ( N2 ) animals and animals from one representative allele of each complementation group— namely trb-1 ( ye492 ) , trb-2 ( ye493 ) , trb-3 ( ye494 ) , and trb4 ( ye495 ) —to survive over a wide dose range of tribendimidine ( Figure 3 ) . At tribendimidine concentrations where most or all of the wild-type nematodes are dead ( e . g . , ≥200 µg/mL ) , the trb-mutant nematodes are mostly or all alive . As opposed to wild-type animals , we did not calculate an LC50 value for any of the trb mutants since there was no concentration in this experiment at which ≥50% of any trb mutant nematodes died . Larvae from all four trb mutants are also resistant to intoxication since they mature to adults at doses that inhibit wild-type larval development ( unpublished observation ) . Resistance to tribendimidine was also confirmed using a quantitative brood size assay [21] , [22] for all four trb mutants . Wild-type C . elegans hermaphrodites show a dose-dependent decrease in brood size production upon exposure to tribendimidine ( Figure 4 ) . In contrast , all trb mutant hermaphrodites exposed to even high doses of tribendimidine show healthy brood sizes that are statistically the same as brood sizes in the absence of the anthelmintic , confirming their resistance ( Figure 4 ) . In the course of our studies , we noticed that tribendimidine stimulated egg-laying in wild-type animals , a behavior that had been previously reported for wild-type C . elegans exposed to the nAChR agonist anthelmintic levamisole [23] . We therefore speculated that tribendimidine might have a similar mechanism of action as levamisole . If so , then one might hypothesize that trb resistant animals might have altered responses to levamisole . To test this hypothesis , we place trb mutant animals on levamisole-containing plates . Whereas wild-type animals become paralyzed and aggregate when exposed to levamisole for 24 h , trb mutant animals are motile and mostly fail to aggregate on levamisole ( Figure 5 ) . Identical results were obtained with pyrantel , another nAChR agonist anthelmintic of the same subtype and mechanism of action as levamisole ( Figure 5; pyrantel and levamisole are collectively known as the L-subtype nAChR agonists [24] ) . These data indicate that tribendimidine-resistant C . elegans are also resistant to L-subtype nAChR agonists . To quantitatively confirm this result , we performed dose-dependent mortality assays of trb-1 ( ye492 ) , trb-2 ( ye493 ) , trb-3 ( ye494 ) and trb-4 ( ye495 ) hermaphrodites on levamisole ( Figure 6 ) . Resistance can be readily discerned at specific concentrations of levamisole; for example at 100 µg/mL only 20% of wild-type animals are alive whereas 99 . 5% , 95% , 99% and 81 . 5% of trb-1 ( ye492 ) , trb-2 ( ye493 ) , trb-3 ( ye494 ) and trb-4 ( ye495 ) animals are alive ( P = 0 . 001 , ANOVA Tukey's test ) . Based on LC50 values ( Table 2 ) , these mutants are 8–16 fold more resistant than wild-type animals to levamisole . We also performed dose-dependent mortality assays of wild-type , trb-2 ( ye493 ) , and trb-3 ( ye494 ) animals on pyrantel ( Figure S3 ) . Although pyrantel is not as effective as levamisole at killing C . elegans ( [16]; this study ) , animals from both trb mutants are resistant to pyrantel relative to wild-type animals . Extensive screens for C . elegans resistant to levamisole have been carried out and have identified a number of genes that mutate to levamisole resistance [25] . Since mutations in trb-1 , -2 , -3 , and -4 resist levamisole , we hypothesized that these mutations might exist in genes known to mutate to levamisole resistance . We mapped the trb-1 , -2 , -3 , and -4 genes to various segments of chromosomes I , X , III , and IV , respectively ( see Materials and Methods for details ) . Each trb mutant was then subjected to genetic complementation tests against known levamisole-resistant mutants located on the same chromosome , to wit trb-1 was tested against unc-29 , unc-38 , unc-74 , and unc-63 , ( but not lev-11 or lev-10 mutants since these were far away on the right arm of chromosome I ) ; trb-2 was tested against lev-8 and lev-9 mutants; trb-3 was tested against the unc-50 mutant; and trb-4 was tested against two alleles of the unc-22 mutant and the lev-1 mutant ( alleles given in Materials and Methods ) . We found that trb-1 ( ye492 ) , trb-2 ( ye493 ) , trb-3 ( ye494 ) , and trb-4 ( ye495 ) each unambiguously failed to complement just one mutant , namely unc-63 ( x13 ) , lev-8 ( x15 ) , unc-50 ( e306 ) and unc-22 ( e66 or s12 ) respectively . To confirm these identities , we sequenced genomic DNA or cDNA isolated from trb-1 ( ye492 ) , trb-2 ( ye493 ) , and trb-3 ( ye494 ) animals ( trb-4/unc-22 is an extremely large locus covering more than 37 kb of DNA and hence was left out of sequencing analyses ) . For trb-2 ( ye493 ) and trb-3 ( ye494 ) , we found that these alleles are associated with point mutations in lev-8 ( tryptophan 164 to a stop codon ) and unc-50 ( serine 261 to leucine ) respectively . The mutation in trb-2 ( ye493 ) is predicted to result in truncation of the C-terminal 70% of the LEV-8 protein , consistent with a null mutant . trb-3 ( ye494 ) is associated with a non-conservative change in an amino acid that is also conserved in unc-50 homologues of other nematodes such as Caenorhabditis briggsae and Brugia malayi , consistent with the fact it might reduce or eliminate function . For trb-1 ( ye492 ) , we found three alterations in nucleotides located in intron 9 of the unc-63 gene ( Figure 7 ) . These alterations occur in conserved intron sequences and can be required for normal splicing [26] , [27] . Thus , the resistance , mapping , complementation , and sequence data indicate that the four complementation groups identified for tribendimidine resistance all occur in genes previously found in screens for levamisole resistance . To determine how much overlap there is between genes that mutant to levamisole resistance and tribendimidine resistance , we took C . elegans strains mutated for eleven levamisole-resistance genes and performed dose-dependent tribendimidine mortality assays ( Figure 8 ) . Taking into account of that some of these mutants ( i . e . , unc-22 and lev-11 ) have compromised health even in the absence of drug , these data clearly show that all eleven mutants are resistant to tribendimidine as demonstrated by their robust survival at doses of the drug that are highly lethal to wild-type ( ≥200 µg/mL; Figures 8 and S4 ) . Thus , for eleven out of eleven levamisole resistant mutants tested , they are also resistant to tribendimidine .
The free living nematode C . elegans has been extensively used in the study of anthelmintics [15] , [25] , [28] . C . elegans is considered an excellent model for anthelmintic mode of action and resistance and has proven invaluable in finding the mechanism of action of almost all anthelmintics in use today . There are many excellent examples of forward genetic screens to discover mutants that allow C . elegans to resist anthelmintics , thereby leading to an understanding of their mechanism of action and mechanisms whereby resistance can develop , including screens for resistance to levamisole [29] , benzimidazoles [17] , aldicarb [30] , ivermectin [31] , [32] , and most recently amino-acetylnitriles [33] . Using the same approach , we have demonstrated that new anthelmintic tribendimidine is an L-subtype nAChR agonist of the same family as levamisole and pyrantel . Tribendimidine causes changes in the egg-laying behavior of C . elegans grossly similar to levamisole . More importantly , a forward genetic screen for C . elegans animals resistant to tribendimidine resulted in the isolation and identification of four mutants that are also resistant to both levamisole and pyrantel and that in fact mutate the same genes that give rise to levamisole resistance . Furthermore , a retrospective study of eleven mutant strains isolated based on their resistance to levamisole demonstrated that all of these mutants are also resistant to tribendimidine . In contrast to these levamisole-resistant mutants , we find that two mutants that affect signaling at the neuromuscular junction independent of levamisole , namely acr-16 ( ok789 ) animals , which lack a levamisole-insensitive nACh receptor [34] , and unc-10 ( e102 ) animals , which are resistant to the cholinesterase inhibitor aldicarb [30] , are qualitatively sensitive to tribendimidine ( Figure S5 ) . Consistent with the fact that tribendimidine does not behave like an cholinesterase inhibitor we find that tribendimidine at 0 . 5 mM , like levamisole at 1 mM , paralyzes animals in seconds , most noticeably at the tip of head , versus cholinesterase inhibitors that take many minutes to affect wild type and contract the body before the head [29] . Thus , although not necessarily intuitive based on its chemical structure ( Figure S1 ) , tribendimidine intoxicates C . elegans using the same pathway as levamisole and thus shares the same mechanism of action as the L-subtype nAChR agonists levamisole and pyrantel . Given the extensive and complete correspondence in the nematode C . elegans between levamisole resistance and tribendimidine resistance , we are certain that tribendimidine will have the same mechanisms of action and resistance as levamisole/pyrantel in parasitic nematodes as well . There are several practical applications of these results . For treating hookworm infections , the intestinal parasitic nematode with the highest disease burden , the benzimidazole albendazole is currently the treatment of choice since it has much better cure rates than levamisole and pyrantel as a single dose and can be given as a fixed dose , unlike the nAChR agonists that are given as dose/weight [7] , [35] . Recent work with tribendimidine suggests that it is superior to levamisole or pyrantel at a single dose and comparable to single-dose albendazole in treating Ascaris or hookworms [11] , [12] . Our data indicate that in places where resistance to benzimidazoles exists or is suspected ( e . g . , in Mali , Zanzibar , Vietnam [10] , [36] , [37] ) , tribendimidine would be a good alternative since its mechanism of action is different from that of the benzimidazoles . However , tribendimidine would be a poor choice in places where nAChR agonist resistance exists or is suspected ( e . g . , in Australia [37] ) . Furthermore our data indicate that tribendimidine would be useful in combinatorial anthelmintic strategies , such as with benzimidazoles [36] , but not in others , such as with levamisole or pyrantel since it shares the same mechanism of action . Our data also highlight the importance of determining the molecular changes associated with L-subtype nAChR agonist resistance in human parasitic nematodes since these changes would allow us to simultaneously track resistance to tribendimidine , levamisole , and pyrantel . Our study highlights the utility of using C . elegans in studying the mechanism of action of anthelmintics used for clinical and veterinary use . This laboratory nematode allows one to rapidly uncover important aspects of new anthelmintic mechanism of action and resistance and can inform how to design strategies for maximizing anthelmintic therapy and minimizing the development of anthelmintic resistance .
|
Intestinal parasitic nematodes or roundworms infect over 1 billion people in tropical countries . Overall , they are a huge source of morbidity in infected people , including children and pregnant women , and are increasingly being recognized as key poverty-promoting parasites . Despite their importance , few drugs for dealing with them exist . Furthermore , none has optimal efficacy , all can be resisted by the parasites , and , for practical reasons , only one is used for single-dose Mass Drug Administrations ( MDAs ) . There is a dire need for better roundworm drugs ( anthelmintics ) . In the past 30 years , only one anthelmintic , tribendimidine , developed by the Chinese CDC , has entered human clinical trials . Tribendimidine has good single-dose efficacy against some roundworm parasites . However , how tribendimidine works was unknown . Here , using the roundworm Caenorhabditis elegans to evolve resistance to tribendimidine in the lab , followed by genetic and molecular testing and cross-resistance drug studies , we demonstrate that tribendimidine is unequivocally in the same drug family as two known anthelmintics , levamisole and pyrantel . These results have important implications for how tribendimidine might be used in MDAs where resistance to current drugs is known or suspected and for how tribendimidine might be combined with other drugs to maximize therapy while minimizing resistance threats .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases/helminth",
"infections",
"infectious",
"diseases/antimicrobials",
"and",
"drug",
"resistance"
] |
2009
|
The New Anthelmintic Tribendimidine is an L-type (Levamisole and Pyrantel) Nicotinic Acetylcholine Receptor Agonist
|
Mycobacterium ulcerans is the causative agent of the necrotizing skin disease Buruli ulcer ( BU ) , which has been reported from over 30 countries worldwide . The majority of notified patients come from West African countries , such as Côte d’Ivoire , Ghana , Benin and Cameroon . All clinical isolates of M . ulcerans from these countries are closely related and their genomes differ only in a limited number of single nucleotide polymorphisms ( SNPs ) . We performed a molecular epidemiological study with clinical isolates from patients from two distinct BU endemic regions of Cameroon , the Nyong and the Mapé river basins . Whole genome sequencing of the M . ulcerans strains from these two BU endemic areas revealed the presence of two phylogenetically distinct clonal complexes . The strains from the Nyong river basin were genetically more diverse and less closely related to the M . ulcerans strain circulating in Ghana and Benin than the strains causing BU in the Mapé river basin . Our comparative genomic analysis revealed that M . ulcerans clones diversify locally by the accumulation of SNPs . Case isolates coming from more recently emerging BU endemic areas , such as the Mapé river basin , may be less diverse than populations from longer standing disease foci , such as the Nyong river basin . Exchange of strains between distinct endemic areas seems to be rare and local clonal complexes can be easily distinguished by whole genome sequencing .
Buruli ulcer ( BU ) , the third most common mycobacterial disease affecting humans after tuberculosis and leprosy , is caused by Mycobacterium ulcerans [1] . The disease is characterized by progressive necrosis of the skin and subcutaneous tissue , leading to sometimes extensive ulcerations . Even though standard antibiotic treatment for eight weeks with rifampicin and streptomycin , as it is currently recommended by the World Health Organization ( WHO ) , is highly effective in killing the bacterium , quite a number of patients still require surgery for wound debridement and/or skin grafting and can remain with scarring and disabilities [2] . BU has been reported from over 30 countries worldwide but has its highest incidence in West Africa , where it occurs very focally in rural areas , which are associated with wetlands , marshes and riverine zones [3] . The distinct pathology of M . ulcerans infections is mainly attributed to mycolactone , a macrolide exotoxin produced by the mycobacteria [4] . Mycolactone is highly toxic to mammalian cells and is also believed to have immunomodulatory functions [5] . The polyketide synthases required to produce this potent toxin are encoded as three large genes on a giant virulence plasmid , pMUM001 , whose acquisition represents a crucial step in the divergence of M . ulcerans from its progenitor M . marinum [6 , 7] . Further hallmarks of M . ulcerans evolution include the proliferation of two distinct insertion sequence ( IS ) elements ( IS2404 and IS2606 ) , the accumulation of a large number of pseudogenes and considerable genome downsizing . These findings indicate that M . ulcerans has recently passed through an evolutionary bottleneck and is adapting to a new and more stable environment [7] . This new niche is suspected to be aerobic , osmotically stable , dark and possibly extracellular . Production of the immunosuppressive toxin mycolactone and the loss of a set of highly immunogenic proteins [8] may represent an adaptation to an environment that is screened by an immune system . Possums , an Australian marsupial species , seem to be especially susceptible to the disease and may function as an animal reservoir in BU endemic foci of Victoria , Australia . However , attempts to identify an animal reservoir in Africa have not been successful to date [9 , 10] . Therefore it is assumed that there may be other environmental reservoirs of M . ulcerans in association with stagnant water bodies . Contact with such wetlands is a known risk factor for contracting BU , and patients may become infected through microtrauma of the skin or inoculation by an unknown insect vector [3] . M . ulcerans DNA has been detected by IS2404 specific PCR in environmental samples , but the cultivation of the slow growing mycobacteria from such samples is exceptionally difficult and has only succeeded once so far [11 , 12] . Therefore it remains unclear what relevance the presence of DNA in the environment has and how M . ulcerans is transmitted . For a long time the highly clonal population structure of M . ulcerans represented a major obstacle for molecular epidemiological studies . Conventional typing methods such as restriction fragment length polymorphism , multilocus sequence typing and variable number tandem repeat analysis provide insufficient resolution [13] . To date the best typing resolution was attained with single nucleotide polymorphism ( SNP ) typing assays [14] . With this method Röltgen et al . were able to demonstrate focal transmission in the Densu river basin of Ghana [14] . Furthermore , with an extended set of SNPs sufficient phylogenetic signal could be obtained to reconstruct recent evolutionary events in M . ulcerans on a continental scale [15] . A drawback of this method is , however , that it requires prior knowledge of the relevant SNPs . With its decreasing costs , whole genome sequencing ( WGS ) is now replacing SNP typing for M . ulcerans . Here we report on a genomic epidemiological study aimed at inferring evolutionary patterns of M . ulcerans in two BU endemic regions of Cameroon ( the Mapé and the Nyong river basins ) by using WGS combined with fine-scale geographic information on the origin of the patients from which the M . ulcerans strains were isolated .
Samples for this study were collected from patients recruited between August 2010 and July 2012 in the Mapé river basin of Cameroon [16] and at the district hospital in Ayos in southern Cameroon [17] . Ethical clearance for the collection and processing of samples was obtained from the Cameroon National Ethics Committee ( N°041/CNE/DNM/09 , N°006/CNE/SE/2010 , and N°172/CNE/SE/2011 ) , the Ethics Committee of the Heidelberg University Hospital , Germany ( N°ISRCTN72102977 ) and the Ethics Committee of Basel ( EKBB , reference n . 53/11 ) . Participation was voluntary and all patients who participated in the study or their legal guardian provided written informed consent . Prior to the start of medical treatment , cotton swabs were collected from each patient for diagnosis of M . ulcerans disease by quantitative polymerase chain reaction ( qPCR ) targeting the M . ulcerans specific IS2404 [18] and for cultivation of the bacteria . Wound exudates in phosphate buffered saline ( PBS ) , that were produced from cotton swabs for DNA extraction as described by Lavender and Fyfe [19] , were decontaminated as described by Bratschi et al . [20] and cultures initiated on Löwenstein-Jensen ( LJ ) medium slants ( with glycerol; Becton Dickinson and Company ) and/or LJ medium slants supplemented with 2% PANTA . Inoculated cultures were incubated at 30°C until growth could be observed . Detected growth was confirmed to be M . ulcerans by colony PCR using primers MU154 ( 5’-ggcagttacttcactgcaca-3’ ) and MU155 ( 5’-cggtgatcaagcgttcacga-3’ ) and amplification for 32 cycles of 30 seconds at 94°C , 30 seconds at 60°C and 1 minute at 72°C . PCR products were resolved in a 1 . 5% agarose gel . Confirmed M . ulcerans cultures were expanded on 7H10 agar plates ( Becton Dickinson and Company ) until enough bacteria could be harvested for DNA extraction as described below . M . ulcerans DNA for WGS was extracted as described by Käser et al . [21] . Briefly , M . ulcerans bacteria were transferred from 7H10 agar plates into a 1 . 5ml screw-cap tube and suspended in lysis buffer ( 15% sucrose , 50 mM Tris ( pH8 . 5 ) and 50 mM EDTA ) . After Incubation with lysozyme for 1h at 37°C , sodium dodecyl sulfate ( SDS ) and proteinase K ( PK ) were added and the bacteria lysed with a tissue homogenizer ( Precellys24 , Bertin Technologies ) in tough micro-organism lysis tubes containing beads ( Bertin Technologies ) . DNA from lysate supernatant was extracted by the Phenol-chlorophorm / Ethyl alcohol ( EtOH ) method [21] . Amount and purity of the extracted mycobacterial DNA was assessed with a Qubit 2 . 0 Fluorometer according to the manufacturer’s protocol ( Qubit dsDNA HS Assay Kit , Invitrogen ) . All processing and sequencing of genomic DNA was performed by the core sequencing teams at the Wellcome Trust Sanger Institute . All samples were sequenced as multiplexed libraries using Illumina HiSeq 2000 analyzers on 75-bp paired-end runs as described by Harris et al . [22] . Variation , in the form of SNPs , was detected using a mapping approach . The paired-end Illumina reads were mapped against the M . ulcerans reference genome of the strain Agy99 ( accession number CP000325 ) and against the M . ulcerans Agy99 pMUM001 plasmid , with an insert size ranging from 50 to 400 bp using SMALT version 0 . 7 . 4 ( http://www . sanger . ac . uk/resources/software/smalt/ ) with a word length of 13 and skip size of 1 . The maximum insert size was 1000 , the minimum insert size was 50 , resulting , on average , in a 25x depth coverage for more than 92 . 1% of the reference genome . The default mapping parameters recommended for reads were employed , except for the minimum score required for mapping , which was increased to 30 to make the mapping more conservative . Candidate SNPs were identified using SAMtools [23] mpileup as previously described [24] . Base calls for all isolates were filtered to remove those at sites with a SNP quality score below 30 , where the called based was in less than 75% of mapped reads on each strand , or where fewer than two reads mapped to each strand . SNPs called in repetitive regions of the M . ulcerans reference genome ( 737 , 280 bp ) were excluded from the analysis and only the SNPs mapped in the core genome ( 4 , 894 , 326 bp ) were used to construct the phylogenetic tree . Repetitive regions were defined as exact repetitive sequences of ≥20 bp in length , identified using rep repeat-match [25] . If 10% of the genomes under study had an ambiguous base in a called SNP , these positions were removed from the analysis . The information on the potentially problematic regions is provided in S1 Table . A maximum-likelihood phylogenetic tree was generated from the whole genome sequences based on the SNPs called by SMALT . Published M . ulcerans genomes from strains isolated in Ghana ( NM14_01 , NM49_02 , NM54_02 , NM43_02 , Agy99 ) , Benin ( Mu_06–3845 , Mu_06–3846 , Mu_07–1082 ) and the M . marinum genome ( Mu_06–3844 ) of an isolate from Belgium were included in the analysis [13] . The M . marinum genome was used to root the tree . In total 91 strains were included in this study ( S2 Table ) . Separate maximum-likelihood trees for the plasmid and the chromosome were reconstructed based on one M . marinum genome and 53 M . ulcerans genomes . The M . ulcerans genomes include 45 Cameroonian isolates with each patient being represented by one strain . Maximum-likelihood phylogenetic trees were constructed using RAxML v7 . 0 . 4 und a general time-reversible ( GTR ) substitution model with γ correction for among-site rate variation . Support for nodes on the trees was assessed using 100 bootstrap replicates . To compute the pairwise distance based on the genome wide SNP count between the isolates we used MEGA6 [26] . The average pairwise SNP distances per genome within the lineages were plotted using software package R ( http://www . r-project . org/ ) and statistical significance was assessed by applying the Wilcoxon rank sum test . Genetic diversity was assessed by calculating the average pair-wise nucleotide differences per site ( Pi ) for both main lineages of M . ulcerans in Cameroon , using the program VariScan [27] . We calculated the nucleotide diversity for 1 . 5 kb non-overlapping windows . We produced two median joining networks using Network 4 . 6 . 1 . 2 [28] , based on 107 variable nucleotide positions from 34 isolates of the Mapé river basin and 117 variable nucleotide positions from 11 isolates of the Nyong river basin . For the analysis of the association between geographic and genetic distances of the sampled populations we performed a Mantel test using the function “mantel” asking for the Pearson’s product –moment correlation with 999 permutations in the R package “vegan” [29] . We did this for two sets of matrices , a smaller subset of 11 samples corresponding to the Nyong isolates , and the entire set of 33 samples corresponding to the Mapé isolates .
We sequenced the genomes of 82 M . ulcerans strains isolated from 45 IS2404 qPCR confirmed Cameroonian BU patients . Patients were identified between 2010 and 2012 and came from two geographically separated BU endemic regions of Cameroon , the Mapé and the Nyong river basins . Prior to treatment , ulcerative lesions were sampled with a cotton swab for laboratory confirmation of the clinical diagnosis , primary isolation of the disease causing organism and WGS of the isolated M . ulcerans strains . The mapping of the obtained Illumina sequencing reads against the reference strain resulted in an average coverage of 380 reads per position per genome . We reconstructed the phylogenetic relationship among 45 Cameroonian ( one strain per patient ) , five Ghanaian and three Beninese M . ulcerans isolates based on 26 , 740 variable nucleotide positions , rooting the tree using a published M . marinum genome ( Fig 1 ) . The phylogenetic tree showed a very strong geographical structure . The chromosome tree of the Cameroonian M . ulcerans isolates showed two distinct lineages , the first one containing all the Nyong river basin isolates ( Nyong lineage ) and the second one all the isolates from the Mapé region ( Mapé lineage ) . The Mapé river basin isolates were more closely related to a set of published genomes [13] of Ghanaian and Beninese isolates that we included in the analysis , than to the Nyong lineage . The two Cameroonian clonal complexes differed in altogether 828 SNPs shared by all members of the respective lineages ( Fig 1 ) . The plasmid phylogeny reflected the topology of the M . ulcerans chromosome phylogenetic tree ( Fig 1 ) , supporting the hypothesis of a unique acquisition of the plasmid during the emergence of M . ulcerans [30] followed by parallel evolution between the chromosome and the plasmid . We analysed the genetic diversity within the two Cameroonian geographical lineages separately ( Fig 2 ) . The genetic diversity observed for the Nyong river basin isolates ( median pairwise SNP difference = 26 . 2 SNPs ) was significantly higher ( p-value < 0 . 0001 ) than for the Mapé basin isolates ( median pairwise SNP difference = 7 . 6 SNPs ) . Furthermore , an analysis of the pairwise geographic distance of seven isolates from the Eastern Nyong river basin ( approximately 1090 km2 ) and of four isolates from Western Nyong ( approximately 625km2 ) still yielded values that were higher ( median 24 and 36 pairwise SNP difference ) than for the Mapé isolates ( Fig 2 ) . The higher genetic diversity thus does not seem to be related to the broader geographical distribution for the Nyong river basin isolates ( approximately 8600 km2 ) compared to the Mapé river basin isolates ( approximately 6400 km2 ) ( Fig 3A2 and 3B2 ) . These results were also reflected in the phylogenetic tree , where branch lengths were longer for the Nyong river basin strains than for the Mapé river basin isolates ( Fig 1 ) . The gene encoding for rpoB , which is known to harbour drug resistance mutations against rifampicin in M . tuberculosis [31] , was not affected by SNPs in any of the M . ulcerans strains analysed here . When analysing the nucleotide diversity distribution along the chromosome by calculating the average nucleotide pairwise diversity per site ( Pi ) for both lineages , 99 . 9% of the genome was found to be highly conserved ( S1 Fig ) . However , the average nucleotide diversity per site for the Nyong river basin lineage was 3 . 2 times higher than for the Mapé river basin lineage ( 4 . 10e-6 versus 1 . 3e-6 ) . The regions of the genome with higher nucleotide diversity ( 0 . 375e-4 and 1 . 25e-4 , respectively ) seemed randomly distributed across the chromosome for both lineages ( S2 Fig ) and the gene content of these regions varied between the two Cameroonian lineages , comprising affected genes of diverse functionalities ( S3 Table ) . In order to analyse the distribution of genetic variants within the endemic areas , we reconstructed median joining networks for the sequenced strains and mapped the places of residence of the patients from which the strains originated ( Fig 3 ) . The network of the Mapé river basin isolates had a star structure with two isolates ( BP130 and BP140 ) at the centre . All the other isolates were connected to this centre and separated by three to nine SNPs . While the SNP distance between the two central strains was zero , the geographical distance between the corresponding residence places of the two corresponding patients was 19 . 5 km . A total of four clusters were distinguished in the network: blue formed by two isolates , green and yellow formed by three isolates each and the red cluster as a complex structure formed by nine strains . The strains belonging to the red cluster shared two SNPs , the green ones also two SNPs , the yellow ones four and the blue ones shared three SNPs . All the grey strains were not forming clusters and differed by 1 to 11 SNPs from the central strains . In the network of the Nyong river basin isolates we observed only three clusters formed by three ( green ) , seven ( red ) and one strain ( blue ) . The isolates from the red cluster shared six SNPs , while the isolates from the green one shared only two SNPs . Overall , for both BU endemic areas in Cameroon we did not find a clear correlation between the genetic networks and the geographic distribution of the houses where the patients lived in the year prior to the onset of BU disease ( Fig 3 ) . Statistical analysis with the Mantel test for the smaller subset of Nyong samples resulted in a positive and marginally significant correlation between the geographic and genetic distances ( r = 0 . 2785 , p-value = 0 . 054 ) , whereas the test performed for the Mapé set of isolates resulted in a small non-significant negative correlation ( r = -0 . 04774 , p-value = 0 . 676 ) . In the course of this genomic epidemiological study we obtained from three patients isolates from two or three different time points during the course of their disease ( Table 1 ) . When comparing the SNP diversity between the isolates from the same patient , only one SNP difference was observed between two sequential isolates ( Table 1 ) . The affected gene ( MUL_1383 ) encoded for a hypothetical protein and the detected mutation was synonymous . No SNP difference was observed between two strains isolated from two distant ulcers of one patient ( Table 1 ) .
Due to the limitations of conventional typing methods for the differentiation of strains belonging to the highly monomorphic African M . ulcerans population , use of WGS was suggested to reach sufficient analytical depth for molecular epidemiology studies [32] . Here our comparative genomic analysis of strains from two geographically separated BU endemic areas of Cameroon , the Mapé and the Nyong river basins , identified two phylogenetically distinct lineages of M . ulcerans . These data support previous findings that the spread of local clonal lineages between endemic areas only rarely occurs [13 , 14] . In a previous IS element—SNP based typing study most strains from the central region of Cameroon had the same SNP types as strains from neighbouring Gabon . The IS element—SNP type found in a strain from the Mapé river basin was also present across entire Central and West-Africa leading to the hypothesis that this lineage represents the founder of the other observed IS element—SNP types [15] . Our WGS analysis showed that the strains from the Mapé river basin are in fact more closely related to the M . ulcerans strain circulating in Ghana and Benin than the strains belonging to the Nyong river basin lineage . Additional WGS data with strains from all BU endemic African countries are required to shed more light on the spread and evolution of M . ulcerans in Africa and the origin of the locally observed two distinct Cameroonian lineages . Analysis of the pairwise SNPs distance and nucleotide diversity distribution revealed a lower genetic diversity among the Mapé river basin strains than among the Nyong river basin strains . Epidemiological data suggest that M . ulcerans has expanded in the Mapé river area more recently than in the Nyong river basin . Descriptions of BU cases in the Nyong river basin exist since 1969 [17] . In contrast , clinically suspected cases of BU in the Mapé river area have been reported first only in 2004 [33] . While the disease may have preexisted there , epidemiological data strongly indicate that BU incidence has recently increased in the Mapé river basin [16 , 33] . Recent expansion of a clone may thus explain the more limited genetic diversity of the M . ulcerans lineage present in the Mapé river basin . It was speculated , that this expansion was associated with environmental changes caused by the damming of the Mapé river in 1989 [16 , 33] . Although it has been shown that cases associate more with the Mbam river as opposed to the Mapé dam directly [11 , 16] , damming may have had an indirect effect on groundwater level and stagnant water bodies in the area . We have compared the genome sequences of strains isolated at different time points over the course of the BU infection of three patients . In only one case , we detected a single SNP in one of the isolates compared to the strain isolated earlier from the same patient . It is not possible to conclude whether this observed single polymorphism is related to a re-infection by a variant strain or to a point mutation that occurred either in the patient or during the in vitro cultivation . However , these data support the expectation of a low mutation rate in M . ulcerans . Our analysis shows that WGS is an important tool for studying the local diversity and population structure of M . ulcerans in endemic areas and for resolving the evolutionary history of the pathogen . A combination of phylogenetic analysis with geographical information on the patient’s home at the time of disease onset did not reveal a clear distribution pattern of the genetic variants . This may in part be related to the limited resolution of the comparative genomic analysis performed here . Resolution of the WGS typing could be further increased by inclusion of repetitive regions of the genome and the virulence plasmid that we so far excluded from the analysis , such as the IS2404 and PE/PPE regions . On the other hand , our sero-epidemiological analyses have provided evidence that exposure to M . ulcerans does not primarily occur at the homes of patients [34] , but may rather be associated with more peripheral environmental water contact sites . Furthermore , for patients from the Mapé river basin it has been found that many of them move over long distances ( in some cases >15 km ) from their homes towards the Mbam river for their farming activities [11 , 16] . For genomic epidemiology studies it may therefore be necessary to establish detailed individual movement and environmental water contact patterns to follow the spatial-temporal spread of genetic variants .
|
Buruli ulcer ( BU ) is a progressively necrotizing disease of the skin , caused by infection with Mycobacterium ulcerans . BU occurs very focally with highest incidence in West Africa . The mode of transmission and the nature and role of potential environmental reservoirs are currently not entirely understood . In this study we sequenced whole genomes of sets of M . ulcerans case isolates from two BU endemic regions in Cameroon . We identified two distinct phylogenetic lineages , which directly correlated with the two endemic regions . Furthermore , we showed that the genetic diversity of M . ulcerans is higher in the older endemic region of Cameroon ( Nyong river basin ) compared to the more recently emerged infection focus in the same country ( Mapé river basin ) . Together , our results demonstrate that M . ulcerans is developing local clonal complexes by the accumulation of single nucleotide polymorphisms ( SNPs ) and that these complexes often remain confined to individual endemic foci . The gene encoding for rpoB , which is known to harbour drug resistance mutations against rifampicin in M . tuberculosis , was not affected by SNPs in any of the analysed M . ulcerans strains .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
|
Locally Confined Clonal Complexes of Mycobacterium ulcerans in Two Buruli Ulcer Endemic Regions of Cameroon
|
In an effort to identify the functional alleles associated with hepatocellular carcinoma ( HCC ) , we investigated 152 genes found in the 4q21-25 region that exhibited loss of heterozygosity ( LOH ) . A total of 2 , 293 pairs of primers were designed for 1 , 449 exonic and upstream promoter regions to amplify and sequence 76 . 8–114 Mb on human chromosome 4 . Based on the results from analyzing 12 HCC patients and 12 healthy human controls , we discovered 1 , 574 sequence variations . Among the 99 variants associated with HCC ( p < 0 . 05 ) , four are from the Dickkopf 2 ( DKK2 ) gene: three in the promoter region ( g . -967A>T , g . -923C>A , and g . -441T>G ) and one in the 5’UTR ( c . 550T>C ) . To verify the results , we expanded the subject cohort to 47 HCC cases and 88 healthy controls for conducting haplotype analysis . Eight haplotypes were detected in the non-tumor liver tissue samples , but one major haplotype ( TAGC ) was found in the tumor tissue samples . Using a reporter assay , this HCC-associated allele registered the lowest level of promoter activity among all the tested haplotype sequences . Retention of this allele in LOH was associated with reduced DKK2 transcription in the HCC tumor tissues . In HuH-7 cells , DKK2 functioned in the Wnt/β-catenin signaling pathway , as an antagonist of Wnt3a , in a dose-dependent manner that inhibited Wnt3a-induced cell proliferation . Taken together , the genotyping and functional findings are consistent with the hypothesis that DKK2 is a tumor suppressor; by selectively retaining a transcriptionally inactive DKK2 allele , the reduction of DKK2 function results in unchecked Wnt/β-catenin signaling , contributing to HCC oncogenesis . Thus our study reveals a new mechanism through which a tumor suppressor gene in a LOH region loses its function by allelic selection .
Hepatocellular carcinoma ( HCC ) is the fifth most common cancer worldwide , and the third leading cause of cancer-related mortality , contributing to over 660 , 000 annual deaths worldwide [1 , 2] . HCC exhibits a distinct geographic distribution of over 80% of HCC cases occurring in Southeast Asia and sub-Saharan Africa . It should also be noted that the incidence of HCC has recently increased significantly in the United States of America [3] . Late-stage HCC cases typically display more genetic alterations than hyperplasia or dysplasia lesions; these alterations include chromosomal instability , DNA rearrangements , DNA methylation , and DNA hypomethylation [4] . Several studies have identified recurrent chromosomal instability regions associated with HCC by comparative genomic hybridization ( CGH ) or loss of heterozygosity ( LOH ) mapping [5–10] . The chromosomal gain regions involve 1q , 5q , 6p , 8q , 10q , 11q , 17q , and 20q , while the chromosomal loss regions involve 1p , 4q , 6q , 8p , 10q , 13q , 16q , and 17p [11] . Several cancer genes have been identified and validated in these chromosomal instability regions . However , the mechanisms by which these genomic alterations at multiple chromosomal segments of potential oncogenes and tumor suppressor genes lead to hepatocarcinogenesis remain undetermined . The Wnt/β-catenin pathway is involved in homeostasis , cell proliferation , differentiation , motility , and apoptosis [12] . Activation of the Wnt/β-catenin pathway frequently occurs in HCC [13 , 14] . β-catenin overexpression and mutations related to this have been described during early-stage HCC development and HCC progression [15–17] . More β-catenin mutations are manifested in hepatitis C virus-associated HCC than in hepatitis B virus-related HCC [17–19] . It is interesting that β-catenin mutations are typically seen in HCC with a low-level genomic instability [20] , indicating that the Wnt/β-catenin pathway could represent an alternative route to hepatocarcinogenesis . Accumulation of β-catenin in the nucleus has been observed in 40% to 70% of HCC cases [10 , 21] . Several secreted proteins are known to negatively regulate the Wnt/β-catenin pathway . These Wnt antagonists can be divided into two functional classes [22] . One involves the Wise , sclerostin and Dickkopf ( DKK ) families that bind directly to LRP5/6 . The other consists of Wnt inhibitory factors and secreted frizzled-related proteins that bind directly to soluble Wnt ligands . The DKK family consists of secreted proteins that contain two cysteine-rich domains [23] and of four members ( DKK1 to DKK4 ) that are able to inhibit the Wnt co-receptors LRP5/6 and Kreman 1/2 [24 , 25] . Down-regulation of the DKK family , when observed in HCC , usually involves epigenetic inactivation either by methylation or via silencing by miRNA [22 , 26] .
On the basis of CGH and LOH studies , approximately 30% to 70% of HCC patients showed genetic alterations in bands 21–25 of chromosome 4q [27–29] . Chromosome 4q21-25 loss is involved in early HCC development [29] . To delineate the LOH pattern in chromosome 4q22-25 , we used ten STR markers from 92 . 5 Mb to 117 . 5 Mb on human chromosome 4 to determine the minimal critical region of LOH for 47 HCC cases . As shown in Fig 1 , 28 cases ( 59 . 6% ) were determined to have LOH within chromosome 4q22-25 region , while the other cases were either non-informative or heterozygous . The result is consistent with the overall LOH frequencies for chromosome 4q22-25 obtained from other studies . According to the Knudsen’s two-hit theory [30] , cancer develops when a tumor suppressor gene mutation occurs in one allele , followed by the loss of the other allele , reflecting as LOH in the genetic analysis . Thus , detection of variant sequences specifically associated with LOH in the tumor tissue is one method of identifying candidate tumor suppressor genes . We have taken a re-sequencing approach in an attempt to discover significant sequence variations in the genes on chromosome 4q21-25 . A total of 2 , 293 pairs of primers were designed for PCR to amplify target sequences; these include 1 , 449 exonic and upstream promoter regions of 152 known and predicted genes that reside in the interval from 76 . 8 Mb to 114 Mb on human chromosome 4 ( NCBI , build 33 ) . In the pilot study using a sample panel consisting of 12 HCC patients and 12 healthy human controls , we identified a total of 1 , 574 sequence variations , consisting of 1 , 462 substitutions , 43 insertions , and 69 deletions . Among these variations , 99 sequence variations of 62 genes were found to be significantly associated with HCC ( p < 0 . 05 ) ( S1 Table ) . Using allelic retention status in the HCC tumor as a criterion , three genes ( UNC5C , DKK2 , and ZGRF1 ) from the LOH region were evaluated for further investigation ( Fig 2 ) . UNC5C , which encodes ntetrin-1receptor , has been reported to function as tumor suppressor gene in human colon cancer [31 , 32] . The DKK family is able to inhibit the Wnt signaling pathway in several cell types and is usually down-regulated in several different cancers [33] . ZGRF1 , whose identity and function were not yet known at the time that we conducted the genotype analysis , is now grouped as a zinc finger gene in the database . Interestingly , 6 of the 12 cases showed LOH in the ZGFR1 sequence and the tumors invariably retained the G-A-C-G haplotype for the four SNPs . Of these variations that were associated with HCC , four that belong to the human DKK2 gene were of particular interest due to their location within the regulatory region of the gene; these consisted of three in the promoter region ( g . -967A>T , g . -923C>A , and g . -441T>G ) and one in the 5’UTR ( c . 550T>C ) . To further investigate the association with HCC , we increased the subject number to 47 HCC cases and 88 healthy controls to analyze these four variations . The results are summarized in Table 1 . The association remained significant for DKK2_-967 , DKK2_-923 , and DKK2_+550 ( p < 0 . 05 ) . Note that the two SNPs at the promoter region ( nucleotides positions -967 and -923 ) are in linkage disequilibrium , therefore , the allele frequency is the same between the two sites . Genetic studies based on haplotypes have provided greater statistical power than those based on the underlying SNPs [34] . To investigate whether or not there were specific DKK2 haplotypes that are associated with HCC , we determined the haplotypes of 88 healthy controls using GENECOUNTING 2 . 2 . A total of four haplotypes that had a probability higher than 0 . 02% were predicted ( Table 2 ) . Among them , two major haplotypes–haplotype 2 ( ACTT ) and haplotype 3 ( TATT ) –had a combined frequency of nearly 76% in the studied subjects; haplotype 2 was the dominant haplotype ( 44 . 9% ) . We also performed direct sequencing to determine the haplotypes of individually cloned genomic DNA fragments from the blood , tumor adjacent tissue , and tumor tissue of 16 HCC patients who were heterozygous for DKK2 . Of 13 HCC cases , eight haplotypes were identified , including four recombinant haplotypes: haplotype 5 ( TAGT ) , haplotype 6 ( ACTC ) , haplotype 7 ( TATC ) and haplotype 8 ( ACGT ) . Interestingly , these additional haplotypes were only detected in the non-neoplastic tissues but were absent from both the blood samples and tumor tissue samples ( Table 3 ) . Notably , haplotype 1 ( TAGC ) was the most frequently observed haplotype in the tumor tissue samples from these HCC cases , observed in 13 out of 16 samples . When we compared the haplotypes of blood , tumor adjacent tissue and tumor tissue from the same patients , we unexpectedly found that there were more than two haplotypes in the tumor adjacent tissues . However , there was only one major allele , haplotype 1 , retained in the tumor tissue . These results indicated that there had been frequent recombination events affecting DKK2 during HCC tumorigenesis and that the DKK2 haplotype 1 had been selectively retained in the tumors . Three of the four identified DKK2 SNPs were located in the promoter region of this gene , while the fourth was in the 5’UTR . We speculated that the various DKK2 haplotypes might show differences in transcriptional activity . To address this issue , we measured the reporter activity of a luciferase gene that was driven by the promoter sequences of the DKK2 haplotype alleles . A promoterless construct was used as a negative control , and the transcriptional activity was normalized against the transfection efficiency determined by β-galactosidase activity . Haplotype 2 ( ACTT ) , which was found most frequently in the healthy controls , drove the expression of luciferase at a rate that was 10 fold higher than that of the promoterless construct ( Fig 3A ) . Similarly , haplotype 3 ( TATT ) , which is referred to as wild type in the NCBI public database , drove the expression of luciferase at a rate 8 fold higher than the reference level . In contrast , haplotype 1 ( TAGC ) was found most frequently in the tumor samples and showed significantly lower transcriptional activity when compared to the other seven haplotypes ( p < 0 . 001 ) . To demonstrate that DKK2 haplotypes effect DKK2 expression and to confirm observations from in vitro studies , relative DKK2 expression levels between the tumor tissues and the non-tumor counterparts from 30 pairs HCC samples were analyzed by reverse transcription quantitative PCR ( RT-qPCR ) . The relative expression levels were classified into three categories , determined by the presence of chromosome 4q24-25 LOH and/or DKK2 TAGC haplotype ( Fig 3B ) . The difference between the two groups , non-LOH and LOH without TAGC , was not significant ( p = 0 . 229 ) . However , the cohort with both chromosome 4q24-25 LOH and DKK2 TAGC haplotype showed significantly lower DKK2 expression levels than the other two cohorts: without chromosome 4q24-25 LOH ( p < 0 . 001 ) and with chromosome 4q24-25 LOH but no DKK2 TAGC haplotype ( p < 0 . 001 ) . Taken together with the genotyping data , our results indicate that this transcriptionally inactive DKK2 allele was being selectively retained in the tumor when heterozygous HCC patients exhibited a LOH during tumorigenesis . To investigate the function of DKK2 as part of the Wnt/β-catenin signaling pathway in hepatocytes , we incubated HuH-7 cells that had been transiently transfected with the TCF reporter plasmid with variable amounts of recombinant Wnt3a and DKK2 . The plasmid contains multiple TCF binding sites upstream of the promoter , and the luciferase activity within the cells reflected the β-catenin concentration in the nucleus [35] . As shown in Fig 4A , luciferase gene expression was correlated with Wnt3a concentration in a dose-dependent manner ( p < 0 . 05 ) . With Wnt3a stimulation , there was significant association between the luciferase activity and DKK2 concentrations above 200 ng/ml ( p < 0 . 05 ) and DKK2 down-regulated Wnt3a-enhanced luciferase gene expression in a dose-dependent manner ( p < 0 . 05 ) . This effect was paralleled between the luciferase assay and the cell proliferation assay ( Fig 4A and 4B ) . Consistently , by abrogating the Wnt and receptor interaction at the cell surface , DKK2 inhibited β-catenin translocation from the cytosol to the nucleus ( Fig 4C ) . The data confirmed that signaling molecules of the Wnt/β-catenin pathway are involved in oncogenesis by controling cell proliferation [36] . Thus , the results of our DKK2 functional studies are consistent with previous reports whereby members of the DKK family are able to play a role in development and disease by modulating the Wnt/β-catenin pathway [22] . To address possible mechanisms of LOH for the DKK2 gene , we analyzed the cytogenetic changes in eight HCC cases that were heterozygous for DKK2 haplotype 1 in their tumor adjacent tissue . All eight HCC cases had chromosomal deletions of band 4q2l , and six cases showed LOH for 4q22-25 ( S2 Table ) . Interestingly , three of the cases were polysomic and one was disomic for chromosome 4 with loss of 4q21 , as determined by dual-color FISH . An example is shown in Fig 5 . Sequencing of the DKK2 gene in the tumor tissue of these cases indicated that only haplotype 1 was retained in the tumor tissue , regardless of the copy number of 4q21 signals . These results support the idea that , during HCC tumorigenesis , chromosome amplification occurs at the DKK2 locus prior to LOH ( Fig 6 ) .
In this study , we have taken a genetic approach to investigate the LOH region of human chromosome 4 and its role in HCC oncogenesis . By scrutinizing the genetic variants in a 37 . 2 Mb region of common chromosomal loss that affects nearly 60% of the HCC cases , we have uncovered the tumor suppressor function of DKK2 in the liver . Additionally , our study provides new insights regarding LOH in HCC . First , we have shown that DKK2 function was compromised in HCC by the removal of active DKK2 alleles . The Wnt signaling pathway plays an important role in liver cancer , and extensive studies have revealed that Wnt antagonists can be inactivated by epigenetic modification of the DKK coding genes [22 , 26] . By way of contrast , our finding provides a new mechanism whereby DKK2 loses its function through selective retention of an inactive allele ( Fig 6 ) . Thus , our data supports that this principle is also applicable to hepatocarcinogenesis . Secondly , re-sequencing the LOH region allowed us to discover functional variants associated with hepatocarcinogenesis . By detecting the differential distribution of haplotypes between blood , non-tumor tissue , and tumor tissue ( Table 3 ) , we were able to identify significant genetic changes in the chromosomal regions showing genomic instability . Selective retention of a functional allele , in theory , could also give rise to overexpression of an oncogene . Allelic imbalance in combination with DNA amplification has been detected in the HCC genome . Given the frequent and extensive genomic changes associated with HCC , other tumor suppressor genes might also be inactivated through a similar mechanism . For example , UNC5C is a known tumor suppressor gene [31 , 32] . Within the 4q21-25 region , UNC5C displayed a nonrandom distribution of alleles in the HCC tumors when LOH has occurred . The functional significance of the ZGFR1 gene showing LOH is currently unknown . Thirdly , by taking a comprehensive approach on a focused region , our analysis revealed that there was hyper-recombination in the promoter region of the DKK2 sequence ( Table 3 ) . We identified more than two haplotypes in the adjacent non-tumor liver tissues , yet most HCC cases retained haplotype 1 in the tumor tissues . Myers et al . ( 2008 ) reported that two DNA motifs are associated with recombination hot spots: the 7-mer CCTCCCT and the 13-mer CCNCCNTNNCCNC; these are clustered in breakpoint regions and act as a driver of genome instability [37] . We scanned the DKK2–1 . 5 kb to +1 kb region and found two CCTCCCT motifs in the DKK2 exon 1 sequence at +640 to +646 and +644 to +650 ( S1 Fig ) . Furthermore , we searched ReDB ( http://www . bioinf . seu . edu . cn/ReDatabase/ ) , a recombination rate database to investigate the DKK2 locus . Interestingly , the recombination rate of the DKK2–437 to -4 , 276 promoter region was dramatically increased from average of 0 . 02% to 14 . 73% ( S3 Table ) . Thus , the results of the sequence analysis support the scheme shown in Fig 6 . As the cell proliferation rate is elevated in the pre-cancerous tissues , DNA breakage is likely to occur near the recombination hotspots in the DKK2 promoter region and this will lead to loss of DKK2 alleles . At the same time , those cells with low transcriptional activity of the DKK2 haplotype 1 allele are selected for clonal amplification during tumorigenesis . Finally , our genetic and functional data confirms that DKK2 functions as a tumor suppressor in the liver . The results from the functional analysis using cultured liver cancer cell support the hypothesis that DKK2 acts through the canonical Wnt pathway and antagonize the cell proliferation elicited by the Wnt3a ligand ( Fig 4 ) . While this study was in progress , others studying different cancer types have reported that DKK2 functions as a tumor suppressor gene [38–40] . Of particular relevance to liver cancer , Maass et al . ( 2015 ) recently published that a Dkk2 deletion in mice is associated with liver carcinogenesis and enrichment of stem cell properties [41] . Thus , DKK2 might work through both Wnt-dependent and independent mechanisms during hepatocarcinogenesis . Considering the role of DKK2 in HCC oncogenesis , genes affected by DKK2 modulation could possibly serve as biomarkers in epidemiological studies . Additional work is warranted to address the implications of these findings with respect to disease classification and clinical management .
The study was approved by the Research Ethics Committee of National Health Research Institutes ( Permit Number: EC1030201-E ) and informed consent was obtained from each participant . Human subjects were recruited from the Koo Foundation Sun Yat-Sen Cancer Center and Chang Gung Memorial Hospital . These human specimens were collected under informed consent in accordance with the recommendations of Research Ethics Committee of National Health Research Institutes . Genomic DNA and total RNA were isolated using the single-step method [42] from tumor tissues of the HCC patients as well as from their adjacent non-tumor tissues that appeared normal . Primers specifically targeting each genomic fragment were designed using Primer3 . Primer sequence information on the 2 , 293 amplicons is available on request . PCR was initiated at 95°C for 10 minutes , followed by 45 cycles of 95°C for 30 seconds , annealing at various temperatures as appropriate to the primer pair for 30 seconds , and extension at 72°C for 45 seconds . The final step was at 72°C for 3 minutes . The optimal annealing temperature for each pair of primer was pre-tested . The PCR products were treated with exonuclease I in order to remove unreacted primers . DNA sequencing reactions were performed using Dye-terminator ( Applied Biosystems Inc . , Foster , CA ) and the same primers were used for the PCR amplification . The products were separated by electrophoresis on an automated ABI 3700 PRISM DNA sequencer to determine the sequence of amplified fragments . The results were analyzed using Phrap-Phred and PolyPhred ( ver . 10 ) software [43] . Heterozygous variations were identified by the presence of double peaks at single nucleotide positions . The forward primer 5'-TTTGCTTGGAAAGTCTCGC-3' and the reverse primer 5'-AGGGGTGGGAATGCAAAG-3' were used for PCR amplification of the -1 , 135 to +667 genomic region of the DKK2 gene . The PCR products were subjected to TA cloning using the pGEM-T vector ( Promega ) . After transformation , 96 colonies were individually selected for direct sequencing . A DNA fragment , -1 , 135 to +667 of the DKK2 gene , was amplified using genomic DNA from each of the HCC cases with different haplotypes . Sequence of the PCR product was verified before cloning into the pGL3 vector . In total , 4 μg of pGL3-DKK2 promoter plasmid DNA and 0 . 8 μg of pcDNA3 . 1-His-LacZ plasmid DNA were co-transfected into HuH-7 cells . After 48 hours , the cells were lyzed and the luciferase activity was detected by LucLite Kit ( Packard BioScience ) following the manufacture’s instruction . To report the relative activity , the measured luciferase activity was normalized against the activity of β-galactosidase activity , which served as a transfection control . Relative DKK2 expression levels between the tumor ( T ) tissues and the non-tumor ( N ) counterpart were determined using RT-qPCR . Total RNA from 30 pairs of HCC samples were reverse-transcribed to cDNA using SuperScriptII ( Invitrogen ) according to the manufacturer's instructions . Subsequent qPCR reactions for DKK2 and β-actin were performed in triplicates on ABI StepOne real-time PCR system , using KAPA SYBR FAST ABI Prism 2X qPCR Master Mix ( Kapa Biosystems ) . The sequences of the primers used for RT-qPCR were as follows: for DKK2 , 5’- GCAATAATGGCATCTGTATC ( forward ) and 5’- GTCTGATGATCGTAGGCAG ( reverse ) and for β-actin , 5’- ATCCGCAAAGACCTGTAC ( forward ) and 5’- GGAGGAGCAATGATCTTG ( reverse ) . All samples were analyzed and normalized with expression level of the internal control gene , β-actin . Relative quantification of fold-change was performed , comparing △CT of tumor tissues and △CT of tumor adjacent tissues . For the TOPflash assay [35] , 2 μg of TCF reporter plasmid DNA and 0 . 5 μg of pcDNA3 . 1-His-LacZ plasmid DNA were co-transfected into HuH-7 cells . After 24 hours , the cells were starved with DMEM medium containing 0 . 1% FBS for another 24 hours . Then , the cells were cultured for 48 hours with medium that contained Wnt3a and/or DKK2 recombinant protein ( ng/ml ) ( Peprotech ) . The TOPflash activity was measured by luciferase activity using the Dual-Luciferase Reporter Assay Kit ( Promega ) . The data was normalized against β-galactosidase activity . HuH-7 cells were plated in the 24 well plates ( 2x104 cells per well ) for 24 hours before the cells underwent serum starvation . After 24 hours , the cells were cultured with DMEM medium containing Wnt3a and/or DKK2 recombinant protein ( ng/ml ) for 48 hours . The cell proliferation assay was performed using alamarBlue cell viability reagent ( Thermo Scientific ) according to the user manual . HuH-7 cells were serum-starved and stimulated with Wnt3a and/or DKK2 recombinant protein , as described above . But , after 6 hours , the nucleus and cytoplasm were separated using the ProteoJET Cytoplasmic and Nuclear Protein Extraction Kit ( Fermentas ) . Protein samples , loaded with 20 μg per lane , were separated by electrophoresis on a 10% SDS-PAGE gel and transferred onto a membrane . Then , the membrane was probed with primary antibodies at optimal dilutions , followed by secondary antibody detection . The primary antibodies used for the current study were anti-β-catenin ( Cell Signaling ) and anti-GAPDH ( Novus Biologicals ) and anti-Histone H3 ( Cell Signaling ) . Touch slide preparations , probe preparations and fluorescence in situ hybridization were performed according to published protocols [44] . In brief , a biotin-labeled 964 a_2 YAC probe specific to chromosome band 4q2l was cohybridized with a digoxigenin-labeled centromeric probe for chromosome 4 . Signal detection was accomplished using avidin-FITC and rhodamine antidigoxigenin . Nuclear counterstaining was carried out using 0 . 1 μg/ml DAPI in antifade solution . To confirm significance of the data obtained from in vivo studies , Kruskal-Wallis H test was implemented to determine if the clusters were significantly different . After significance was established , Mann-Whitney U tests were used to identify which cluster exhibited the greatest significance . For in vitro data , variance pre-test was analyzed using the F test of equality of variances . Once the data sets were determined to show homoscedasticity , Student's t test was performed to test the significance of the differences between the sample conditions . To verify dose-dependence of cell proliferation rate , ANOVA for regression analysis was used .
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Liver cancer is one of the most lethal human cancers . Identifying functional alleles associated with liver cancer can provide new insights into the disease’s pathogenesis and help to advance the development of new therapeutic approaches . We conducted re-sequencing of the 4q21-25 region that frequently showed loss of heterozygosity ( LOH ) in liver cancer . Among the 99 variants associated with liver cancer , four are found within the Dickkopf 2 ( DKK2 ) gene . We conducted haplotype analysis of the DKK2 promoter sequence and found that a transcriptionally inactive DKK2 allele was selectively retained in the tumor tissues . Additionally , by sequencing individual molecular clones , we detected 7-mer CCTCCCT sites within the DKK2 promoter region that are involved in PRDM9 binding , pinpointing hotspots for recombination and genome instability . Furthermore , we demonstrated that DKK2 functioned as an antagonist within the Wnt/β-catenin signaling pathway . Our findings have led to the discovery of a new mechanism whereby a tumor suppressor gene in a LOH region loses its function by allelic selection .
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2016
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Selective Retention of an Inactive Allele of the DKK2 Tumor Suppressor Gene in Hepatocellular Carcinoma
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To achieve robust replication , bacteria must integrate cellular metabolism and cell wall growth . While these two processes have been well characterized , the nature and extent of cross-regulation between them is not well understood . Here , using classical genetics , CRISPRi , metabolomics , transcriptomics and chemical complementation approaches , we show that a loss of the master regulator Hfq in Caulobacter crescentus alters central metabolism and results in cell shape defects in a nutrient-dependent manner . We demonstrate that the cell morphology phenotype in the hfq deletion mutant is attributable to a disruption of α-ketoglutarate ( KG ) homeostasis . In addition to serving as a key intermediate of the tricarboxylic acid ( TCA ) cycle , KG is a by-product of an enzymatic reaction required for the synthesis of peptidoglycan , a major component of the bacterial cell wall . Accumulation of KG in the hfq deletion mutant interferes with peptidoglycan synthesis , resulting in cell morphology defects and increased susceptibility to peptidoglycan-targeting antibiotics . This work thus reveals a direct crosstalk between the TCA cycle and cell wall morphogenesis . This crosstalk highlights the importance of metabolic homeostasis in not only ensuring adequate availability of biosynthetic precursors , but also in preventing interference with cellular processes in which these intermediates arise as by-products .
Central metabolism is crucial for generating energy and biosynthetic precursors during cell growth and proliferation . In bacteria , cellular replication also requires the synthesis of peptidoglycan ( PG ) , a major component of the bacterial cell wall that determines the shape and size of the cell . Both central metabolism and cell morphogenesis have been extensively studied , but often independently of each other . A connection between these two processes is supported by the observation that many mutations that affect cell shape and size are found in metabolic genes [1–11] . One mechanism that connects central metabolism to cell morphogenesis in general and cell division in particular is through the action of ‘moonlighting’ enzymes . In addition to performing their normal metabolic functions , moonlighting enzymes indirectly promote or inhibit septal PG synthesis in a metabolite-dependent manner through their interaction with a component of the cell division apparatus [2 , 4 , 6–8] . A second mechanism that links central metabolism to cell morphogenesis has been suggested by genetic studies [1 , 3 , 12] . In this case , the proposed link is more direct as it relies on the availability of shared metabolic substrates . For instance , glycolysis and PG synthesis use common metabolites , such as fructose-6-phosphate ( F6P ) and phosphoenolpyruvate ( PEP ) , as substrates [13] . Therefore , mutations in metabolic genes may alter metabolic fluxes that generate F6P and PEP , causing a depletion of these substrates and ultimately affecting PG synthesis . Hfq is an RNA chaperone and a global regulator of gene expression that is involved in many aspects of bacterial physiology and stress response [14–17] . Deletion of hfq can affect the expression of up to 20% of the genes in the genome , including metabolic genes [18 , 19] . Interestingly , the loss of Hfq in various bacteria results in varying degrees of cell morphological defects [20–25] . In this study , we show a critical role for Hfq in maintaining metabolic homeostasis in Caulobacter crescentus that reveals a previously unrecognized mechanistic link between metabolic dysregulation , PG synthesis and cell morphogenesis .
A recent genome-wide Tn-Seq study in C . crescentus annotated hfq ( CCNA_01819 ) as essential for viability in PYE medium at 30°C [26] , which is a common laboratory growth condition for C . crescentus . When we attempted to generate an hfq deletion by allelic gene replacement with an oxytetracycline resistance cassette ( S1A Fig ) , we were able to obtain Δhfq colonies . However , the Δhfq colonies were much smaller than expected for normal growth on PYE plates at 30°C . The Δhfq strain also grew considerably slower than wild-type CB15N ( WT ) in liquid culture ( doubling time of ~250 min vs ~90 min ) , consistent with a severe loss of fitness . Whole-genome sequencing verified the hfq deletion and the absence of suppressive mutations ( data deposited in the Sequence Read Archive database as SRP105792 ) . The discrepancy with the Tn-Seq study regarding the essentiality of hfq is addressed in a later section of the manuscript . Phase-contrast microscopy of Δhfq cells revealed the presence of storage granules in some cells ( Fig 1A , arrows ) , a common indicator of stressful conditions [27–30] . More interesting to us , however , was the association of the hfq deletion with a cell morphology phenotype ( Fig 1A ) . While the parental CB15N strain ( WT ) maintained a narrow distribution of cell lengths ( l = 2 . 83 ± 0 . 68 μm , mean ± standard deviation ) and widths ( w = 0 . 63 ± 0 . 02 μm ) , the Δhfq strain displayed large variability in cellular dimensions ( l = 3 . 99 ± 2 . 26 μm , w = 0 . 72 ± 0 . 11 μm; S1 Table ) due to an abnormally high frequency of wide and elongated cells in the population ( Fig 1B ) . In the C . crescentus genome , hfq is the first gene in an operon that also contains hflX , a gene predicted to encode a ribosome-associated GTPase ( S1A Fig ) . A ΔhflX strain showed neither growth nor cell shape defects ( S1B–S1D Fig ) , indicating that the Δhfq phenotypes were caused by the loss of Hfq , and not by a polar effect on hflX expression . To investigate the origin of the Δhfq phenotypes , we first undertook a genetic approach . We took advantage of the Δhfq growth defect to isolate ‘suppressor’ mutants following Tn5 mutagenesis ( Fig 2A ) . From ~74 , 000 Tn5 mutant colonies inspected on PYE agar plates , 143 of them ( ~0 . 2% ) appeared to form noticeably bigger colonies compared to the parental Δhfq strain , indicating faster growth . The majority of these suppressors also showed markedly improved growth rates in liquid culture ( Fig 2B ) . To identify the potential mechanism of suppression , we mapped the transposon insertion sites for the top 30 fastest-growing suppressors ( Fig 2C , S2 Table ) . This set represented suppressors with growth rates within 16% of the wild-type rate ( Fig 2B , red shaded region ) . Two thirds ( 20/30 ) of the transposon hits mapped to a single uncharacterized gene: CCNA_03280 , which we renamed vor because of its putative role in branched-chain amino acid utilization , as explained below . Another two hits were found in the adjacent gene , CCNA_03281 , which encodes an Lrp-like transcription factor that typically senses amino acids [31] . The remainder of the Tn5 hits were scattered around the genome ( S2 Table ) . We focused our attention on vor given the prevalence of its inactivation among suppressor mutants . In addition to rescuing the growth defect in PYE medium , the vor::Tn5 mutation partially suppressed the abnormal cell shape/size distribution caused by the hfq deletion ( l = 2 . 87 ± 1 . 2 μm and w = 0 . 66 ± 0 . 05 μm; Fig 2D and 2E , S1 Table ) . Partial suppression of the growth and cell shape phenotypes was also observed for a Δhfq strain , in which we replaced the vor gene by an antibiotic resistant cassette ( S2A–S2D Fig , S1 Table ) . Inactivation of vor alone ( either with the Tn5 insertion or by gene deletion ) in a hfq+ background did not show any apparent growth or morphological phenotypes ( S2B , S2E and S2F Fig , S1 Table ) . Based on sequence homology , vor is predicted to encode a 1147-residue enzyme ( VOR ) belonging to the 2-oxoacid:ferredoxin oxidoreductase superfamily , known to be involved in the metabolism of keto acids [32] . Recent work in Phaeobacter inhibens , a marine bacterium in the same α-proteobacterial class as C . crescentus , proposes that a homolog to CCNA_03280 ( renamed VOR ) may be involved in branched-chain amino acid ( BCAA ) utilization in place of the typical branched-chain keto acid dehydrogenase complex ( BCKDC ) , which catalyzes the decarboxylation of branched-chain keto acid into acyl-CoA [33] . The C . crescentus genome appears to encode all of the necessary enzymes for BCAA degradation , except BCKDC ( S3A Fig ) [34] . We therefore hypothesized that VOR is the enzyme responsible for metabolizing branched-chain keto acids in C . crescentus . To test this idea , we monitored the ability of WT and vor::Tn5 strains to utilize BCAAs ( leucine , isoleucine and valine ) as carbon sources in a defined minimal medium ( M2BCAA ) . In agreement with our hypothesis , the vor::Tn5 mutant , unlike WT , was unable to utilize the supplemented BCAAs for growth ( S3B Fig ) , though it showed similar growth to WT in the presence of glucose as the sole carbon source ( S3C Fig ) . Note that the small amount of growth observed for the vor::Tn5 strain in M2BCAA could be attributed to the vitamin mixture included in this growth medium ( S3D Fig ) . This vitamin mixture contains small amount of myo-inositol , a known carbon source for C . crescentus [35] . Previous microarray experiments in C . crescentus have shown that vor expression is induced in the amino acid-containing PYE medium relative to amino acid-free media [36] . Together , these data support a role for VOR in the BCAA degradation pathway in C . crescentus , most likely in metabolizing branched-chain keto acids , as previously suggested for P . inhibens . VOR is the name used to define the subfamily of 2-oxoacid:ferredoxin oxidoreductases involved in BCAA utilization [32 , 37] . The fact that deletion of vor suppressed , at least partially , the Δhfq phenotypes ( Fig 2 ) indicated that vor expression is deleterious in the Δhfq strain . To test whether the enzymatic activity of VOR was required for its observed toxicity in the Δhfq background , we expressed wild-type ( WT ) or catalytically inactive ( E84A ) VOR proteins from a plasmid in the Δhfq Δvor double mutant background , and found that the growth and morphology defects of the Δhfq strain depend on the enzymatic activity of VOR ( S4 Fig , S1 Table ) . Since VOR is a metabolic enzyme , we considered the possibility that the Δhfq defects might be caused by metabolic perturbations . We therefore conducted metabolite profiling experiments using liquid chromatography-mass spectrometry ( LC-MS ) to quantify the abundance of intracellular metabolites in Δhfq and control strains grown in PYE . We expected that if one or more metabolites were involved in the Δhfq defects , their levels would be abnormal in the hfq mutant and be at least partially rescued in the suppressor Δhfq vor::Tn5 . For these metabolomics experiments , we adapted a filter culture-based method [38 , 39] for C . crescentus growth ( S5A Fig ) , and verified that the defects of the Δhfq strain observed in PYE liquid cultures were reproduced under these conditions ( S5B Fig ) . The metabolic profile of the Δhfq strain revealed major alterations in steady-state levels of various central metabolites compared to the wild-type strain ( Fig 3A , S3A Table ) . Many of the metabolites were closely associated with the TCA cycle ( Fig 3B ) . Perturbations in the TCA cycle provides a potential explanation for the growth defect of the Δhfq strain , as the TCA cycle is expected to play a crucial role in energy production when amino acids are the main carbon sources . However , it was less intuitive how this metabolic dysregulation may account for the observed cell morphogenesis defects . To address this question , we focused our attention on α-ketoglutarate ( KG ) , as this metabolite showed the most drastic change , with ~35-fold increase in the Δhfq strain relative to WT based on LC-MS analysis . The abundance of KG was partially decreased in the Δhfq vor::Tn5 strain ( Fig 3A ) , consistent with the partial suppression of the Δhfq morphological defects in this strain ( Fig 2D and 2E ) . An enzymatic assay on metabolite extracts from liquid cultures independently confirmed the correlation between the intracellular level of KG and the severity of the cell shape defects ( Fig 3C ) , though with different fold changes that may result from differences in growth conditions ( solid versus liquid media ) , metabolite extraction procedures [40] and quantification methods . We reasoned that if KG accumulation was the cause of the Δhfq morphological phenotypes , an increase in KG levels independently of an hfq mutation ( i . e . , in an hfq+ background ) would phenocopy the cell shape defects . To test this prediction , we sought to increase the intracellular KG concentration by reducing the activity of the ketoglutarate dehydrogenase enzyme ( KGDH ) , which converts KG into succinyl-CoA ( SucCoA; Fig 3B ) . KGDH is a multisubunit enzyme complex . The E1 and E2 subunits are encoded by the operon containing sucA ( CCNA_00342 ) and sucB ( CCNA_00343 ) genes . Both genes are essential for viability [26] . Attempts to deplete E1 and E2 by placing the sucAB operon under a vanillic acid-controllable promoter ( Pvan ) failed , as the resulting strain did not show any growth defects when cultured in the absence of vanillic acid , presumably due to an incomplete repression of Pvan . To achieve better repression , we adapted the CRISPRi system [41] to C . crescentus ( S1 Text ) . We validated the CRISPRi system by depleting the essential cell division protein FtsZ , which led to the expected cell filamentation phenotype ( S6 Fig ) . To control KGDH levels , we created a CRISPRi construct targeting sucA . Since sucA and sucB are located in an operon , our CRISPRi construct is expected to deplete both E1 and E2 proteins ( Fig 4A ) . Using CRISPRi , we found that KGDH depletion led to a growth defect ( Fig 4B ) and an increase in average cell size ( Fig 4C and 4D , S1 Table ) , though we noted a subpopulation of cells that did not show considerable change in length and width ( Fig 4D ) , possibly due to incomplete depletion of KGDH in these cells . In a separate project , we isolated two independent strains carrying different point mutations in sucA that are temperature-sensitive ( ts ) for viability . These mutants exhibited normal cell shape and size at 28°C , but the cells became wider and longer when the cultures were switched to the non-permissive temperature ( 38°C ) ( S7 Fig , S1 Table ) . These results support the notion that KG accumulation , independently of an hfq mutation , is sufficient to cause cell morphological defects . How can KG accumulation lead to a cell shape defect ? A cell widening phenotype is often characteristic of a PG defect . For instance , mutations in the cell envelope biosynthesis pathway that lead to a limitation in the lipid II PG precursor cause morphological phenotypes reminiscent of those observed in our study [42 , 43] . Depletion of proteins involved in the synthesis of PG precursors has similarly been shown to increase cell width [9] . In addition , treatment with a sublethal concentration of fosfomycin , an antibiotic that inhibits an early step of PG precursor biosynthesis ( Fig 5A ) , resulted in wider and elongated cells ( S8 Fig ) [44] . This phenotypic connection raised the possibility that KG accumulation in Δhfq cells might affect cell morphology through inhibition of the PG precursor synthesis pathway . Interestingly , KG is a by-product in the synthesis of meso-diaminopimelate ( m-DAP ) ( Fig 5A ) , which is incorporated into PG precursors at the third position of the peptide side chain of PG in C . crescentus and other bacteria [45] . The enzymatic reaction producing KG is catalyzed by a succinyldiaminopimelate aminotransferase , also known as DAP-AT ( Fig 5A ) . Over 50 years ago , biochemical work with purified DAP-AT from E . coli showed that a high level of KG inhibits the activity of this enzyme [46] . This in vitro observation led us to hypothesize that accumulation of KG in Δhfq cells may be sufficient to inhibit DAP-AT activity and therefore m-DAP production in vivo . A reduction in the m-DAP pool could , in turn , limit the synthesis of PG precursors and cause cell shape defects ( Fig 5B ) . Our hypothesis led to two key predictions . First , inhibition of DAP-AT activity should lead to accumulation of UDP-N-acetylmuramoyl-l-alanyl-d-glutamate ( UDP-MurNAc-dipeptide ) , the intermediate in the PG biosynthesis pathway immediately before m-DAP addition ( Fig 5A and 5B ) . Therefore , we examined the Δhfq metabolome for an ion matching the calculated mass of UDP-MurNAc-dipeptide ( C28H43N5O23P2 , m/z = 878 . 1751 ) . As expected , we found a large peak with a retention time of ~12 . 22 min in the Δhfq metabolome , while the corresponding peak was barely detectable in the WT sample ( Fig 5C and 5D ) . The abundance of this ion was lower in the suppressor Δhfq vor::Tn5 strain ( Fig 5C and 5D ) , consistent with the partial reduction in KG level in this strain ( Fig 3A and 3C ) . To verify that this peak actually corresponded to UDP-MurNAc-dipeptide , we analyzed the chromatographic profiles of the same ion from a strain in which DapE was depleted . The dapE gene ( CCNA_00277 ) encodes a succinyl-diaminopimelate desuccinylase , which is necessary for m-DAP synthesis and functions downstream of DAP-AT in the pathway ( Fig 5A ) . Depletion of the DapE counterpart in B . subtilis results in UDP-MurNAc-dipeptide accumulation [47] . Similarly , we observed accumulation at the 12 . 22-min peak identified in the Δhfq chromatogram when we depleted C . crescentus DapE using CRISPRi ( S9A Fig ) , confirming that this peak corresponds to UDP-MurNAc-dipeptide . Notably , DapE depletion also resulted in the appearance of wider and more elongated cells ( S9B Fig , S1 Table ) , consistent with the idea that m-DAP depletion causes a cell shape defect . The second prediction of our hypothesis was that , if inhibition of DAP-AT activity and the resulting limitation of m-DAP synthesis cause the morphological defects of the Δhfq strain , addition of a downstream metabolite that bypasses DAP-AT should increase m-DAP availability for PG synthesis and restore normal cell morphology . To test this prediction , we grew the Δhfq strain in the presence of 2 , 6-L , L-diaminopimelate ( DAP ) , which can be converted into m-DAP in vivo ( Fig 5A ) . Consistent with the prediction , DAP supplementation rescued the morphology phenotypes of the Δhfq strain ( Fig 5E and 5F , S1 Table ) , despite the level of KG remaining high ( Fig 3C; p-value = 0 . 38 by two tailed t-test , in comparison with Δhfq cells grown without DAP ) . We noticed , however , that the Δhfq growth defect was not suppressed by DAP ( S10 Fig ) , suggesting the involvement of additional factors that impair growth ( e . g . , coenzyme A ( CoA ) , see below ) . Disconnection between cell growth and peptidoglycan synthesis has been previously observed [44] . Altogether , these results strongly suggest that KG accumulation can specifically affect cell morphogenesis by reducing PG precursor synthesis . What could cause the increased levels of KG when Hfq is absent ? In various microorganisms , KG has been shown to accumulate under nitrogen starvation conditions [38 , 48–50] . However , it is unlikely that the Δhfq strain was deprived of nitrogen as cells were grown in PYE , a growth medium that contains amino acids as sources of nitrogen . To gain more insight into the physiological changes associated with the hfq deletion , we performed an RNA-Seq experiment to compare the gene expression profiles of WT and Δhfq cells grown in PYE cultures . Transcriptomic analysis revealed that hundreds of genes are differentially expressed by a fold-change ≥ 2 ( p-value ≤ 0 . 01 ) in the absence of Hfq ( S4A Table ) . These genes were associated with a wide range of cellular functions , as shown by the analysis of ‘clusters of orthologous groups’ ( COGs ) ( S11 Fig ) . We specifically looked for differentially expressed genes whose functions could be tied to KG metabolism . Based on this analysis , we hypothesized that the KG accumulation in Δhfq cells primarily stems from misregulation of two metabolic genes , panD ( CCNA_02380 ) and vor . Both genes are involved in CoA metabolism ( Fig 6A ) , which is directly tied to KG metabolism ( Fig 3B ) . panD , which encodes an enzyme involved in β-alanine synthesis , was one of the most downregulated genes in the Δhfq transcriptome with ~4 . 5-fold decrease ( Fig 6B , S4 Table ) . β-alanine is a precursor for pantothenate , which is required for the synthesis of CoA . Expression of vor was , in contrast , elevated 2-fold in the absence of Hfq ( Fig 6B ) . One of the predicted substrates of VOR , ketoisovalerate , is also involved in pantothenate biosynthesis . Hence , overexpression of VOR may draw more ketoisovalerate into the valine degradation pathway , potentially reducing pantothenate synthesis . The combination of higher vor and lower panD expression levels in the Δhfq strain may result in reduction of free CoA inside cells ( Fig 6C ) . This prediction is consistent with the ~2-fold reduction in CoA levels in the absence of Hfq ( Fig 3A , S3A Table ) . Reduced levels of CoA in Δhfq cells could negatively affect the activity of KGDH , which uses CoA as a cofactor to convert KG into SucCoA , resulting in KG accumulation ( Fig 6C ) and cellular defects . This would explain why inactivation of vor , which is commonly found among the Tn5 suppressors ( Fig 2C ) , partially rescues the Δhfq phenotypes; vor inactivation would increase the flux of ketoisovalerate into pantothenate synthesis , thereby making CoA available for KGDH , leading to a decrease in KG accumulation in the suppressor strain ( Fig 6C ) . Further evidence of CoA limitation in Δhfq cells was provided by the observation of an accumulation of pyruvate ( Fig 3A , S3A Table ) , a substrate of the pyruvate dehydrogenase complex , which also requires CoA . Pyruvate levels were conversely lower in the suppressor hfq vor::Tn5 strain compared to Δhfq ( Fig 3A , S3A Table ) . This model predicts that increasing CoA levels would restore KG levels and rescue the Δhfq defects . One way to increase the intracellular concentration of CoA is by supplementing the culture medium with the CoA precursor pantothenate , which can be taken up by many bacteria [51 , 52] . Accordingly , Δhfq cells grown in PYE with pantothenate ( 1 mM ) displayed a decrease in KG abundance ( Fig 3C ) and a restoration of the WT morphology ( Fig 6D and 6E , S1 Table ) . Interestingly , growth of the hfq mutant was also improved with pantothenate supplementation ( Fig 6F ) . This observation suggests that both growth and morphology phenotypes of the Δhfq strain originate from metabolic perturbations in general and the reduction in CoA levels in particular . The two phenotypes are , however , uncoupled downstream of the metabolic dysregulation , as shown by the addition of DAP , which exclusively suppresses the cell shape defects by restoring PG precursor synthesis ( Fig 5E and 5F ) . PG is a common target for many successful antimicrobial agents [53] . Therefore , we reasoned that the metabolism-dependent reduction of PG precursor synthesis in the Δhfq strain may also alter the susceptibility of the cell to PG-targeting antibiotics . To test this hypothesis , we used disk diffusion assays on PYE agar plates to quantify the sensitivity of Δhfq and control strains to three PG-targeting antibiotics . Specifically , we used fosfomycin , cephalexin , and vancomycin ( C . crescentus is sensitive to vancomycin despite having an outer membrane [54] ) , which target different stages of PG synthesis . As mentioned above , fosfomycin inhibits the synthesis of PG precursors in the cytoplasm ( Fig 5A ) [55] . In contrast , cephalexin and vancomycin inhibit later steps in PG assembly that occur in the periplasm [56 , 57] . We found that the Δhfq strain exhibits increased sensitivity to all three PG-targeting antibiotics , as shown by the larger zones of inhibition relative to the wild-type strain ( Fig 7A and 7B ) . Such increase in sensitivity was not observed for protein synthesis inhibitors , such as gentamicin and spectinomycin ( S12 Fig ) . The hypersensitivity to PG-targeting antibiotics was partially suppressed in the Δhfq vor::Tn5 suppressor strain ( Fig 7B ) , consistent with the partial rescue of PG precursor synthesis and the lower levels of KG in this strain ( Figs 3C and 5D ) . In addition , supplementation of DAP in the plate reduced the heightened sensitivity of the Δhfq strain to cephalexin and vancomycin , while exhibiting no effect on the susceptibility of the WT strain ( Fig 6B ) . Interestingly , we did not observe any change in Δhfq sensitivity to fosfomycin when grown in the presence of DAP ( see Discussion ) . Altogether , these results suggest that the reduction of PG precursor synthesis due to metabolic perturbations in the Δhfq strain impairs cell wall biogenesis and increases the cell’s susceptibility to PG-targeting antibiotics . So far , we have examined the hfq deletion phenotypes during growth on amino acids as the main carbon sources . Microbes are often faced with different nutrient sources that can have a significant influence on cellular metabolism . Therefore , we hypothesized that the nutrient composition of the growth medium would affect , positively or negatively , the Δhfq phenotypes . Aside from amino acids , glucose and xylose are the best characterized carbon sources for C . crescentus growth in the laboratory [36] . In glucose-containing medium ( M2G ) , Δhfq mutant cells grew slower than wild-type cells , but their morphology appeared normal ( S13A–S13C Fig , S1 Table ) . Accordingly , metabolomic analysis of M2G-grown Δhfq cells showed perturbations in the levels of central metabolites , but no significant accumulation of KG ( S3B Table ) . Growth in xylose as a carbon source ( M2X ) aggravated the fitness defect associated with the hfq deletion , as Δhfq cells could barely grow in M2X ( S13D Fig ) and appeared stressed as suggested by the prevalence of storage granules ( S13E Fig , arrow ) . Investigating the effect of xylose metabolism on cell morphology was problematic , as growth is required to observe a dramatic cell shape defect . However , despite the small amount of growth , the few Δhfq cells in the M2X culture already presented a cell widening phenotype relative to WT ( S13E and S13F Fig , S1 Table ) . The detrimental effects of xylose on Δhfq cells provide a possible explanation for the annotation of hfq as an essential gene in the genome-wide Tn-Seq study , as the growth medium used in this study contained xylose [26] . Altogether , the strong phenotypic sensitivity of the Δhfq mutant to the available nutrient source further supports the notion that maintaining metabolic homeostasis is not only important for optimal cell growth but also for proper cell morphogenesis in different nutritional environments .
Our work demonstrates the importance of Hfq in regulating central metabolism in C . crescentus ( Figs 2 , 3 , 6 and 8 , S13 Fig ) . The key finding is the direct connection between the TCA cycle and PG synthesis , which provides an explanation for the cell shape defects in Δhfq cells grown in PYE . Based on RNA-Seq data , Hfq does not regulate the expression of genes known to be involved in cell shape regulation ( S4 Table ) . Instead , our data support a model for a metabolite-dependent regulation of PG synthesis ( Fig 8 ) . In this model , imbalance of a TCA cycle metabolite directly affects a PG biosynthetic step . The precise mechanism by which Hfq affects the TCA cycle is unknown . As an RNA chaperone , Hfq regulates gene expression at the posttranscriptional level , typically in conjunction with small regulatory RNAs ( sRNAs ) [14–16] . We propose that the metabolic perturbations in Δhfq cells are connected to the misregulated expression of metabolic genes ( e . g . , panD , vor ) ( Fig 8 ) , perhaps through the loss of sRNA activity . While the mechanistic details for this regulation remain to be determined , the proper control of metabolic genes is critical for maintaining the correct balance of central metabolites . Disruption of KG homeostasis in the Δhfq mutant is likely due to a block in the TCA cycle caused by a reduction in CoA level ( Fig 8 ) . The resulting accumulation of KG inhibits DAP-AT enzymatic activity , limiting m-DAP synthesis , and thereby reducing the level of PG precursors ( Fig 8 ) . KG has recently emerged as an important molecule in bacteria that affects many cellular processes , including fatty acid metabolism , nitrogen metabolism , and sugar uptake [58] . Our work expands the list of cellular processes influenced by KG to include PG synthesis . It might seem counterintuitive that reduced PG precursor synthesis would cause cells to become wider and more elongated . Recent work suggests that the increase in cell size upon inhibition of PG synthesis by fosfomycin is due to an imbalance between the growth of cell surface area and volume [44] . In this model , volume growth ( due to the synthesis of cytoplasmic components ) is faster than surface growth ( synthesis of PG ) , potentially causing higher turgor pressure that leads to a cell width increase . In addition , the slower PG synthesis is proposed to trigger a delay in cell division , which presumably allows cells to accumulate sufficient PG materials , thereby leading to a cell length increase . A similar model may be applicable to the Δhfq phenotypes since PG synthesis is also impaired in this strain . The reason for the large variation in cell size and shape defects within a clonal population of Δhfq cells ( Fig 1C ) is unknown . This cell size variability can be rescued by the addition of DAP ( Fig 5E and 5F ) , suggesting a potential heterogeneity in PG synthesis among Δhfq cells . A link between the TCA cycle and PG synthesis was previously noted in studies investigating E . coli mutants that require lysine supplementation when grown on glucose as the sole carbon source [59–61] . The lysine auxotrophy was proposed to stem from depletion of SucCoA , a cofactor required for the synthesis of m-DAP and lysine ( Fig 5A ) [60 , 62 , 63] . We found that in Δhfq cells , the abundance of SucCoA is modestly decreased compared to the WT situation ( Fig 3A , S3A Table ) . Therefore , it is possible that the lower SucCoA level may also contribute to the defective PG synthesis in the hfq mutant . Central metabolism provides energy and building blocks for the cell . Therefore , maintaining the proper concentration of central metabolites—i . e . , achieving homeostasis—is important for driving enzymatic reactions and achieving optimal growth under various conditions . What is less appreciated is that central metabolites , of which KG is a prime example , can be involved in a wide range of enzymatic reactions inside cells . In addition to serving as substrates in their primary pathways , these metabolites can be by-products of reactions in other pathways . An excess of these common metabolites will have unanticipated inhibitory effects on such pathways , as is the case for KG on m-DAP synthesis ( Figs 5A and 8 ) . Our work illustrates the importance of homeostatic control of central metabolites in preventing a “ripple effect” through interference with other cellular processes . The observation that KG accumulation causes not only cell shape defects , but also increased sensitivity to PG-specific antibiotics exemplifies a detrimental consequence of this ripple effect ( Fig 7 ) . DAP supplementation to Δhfq cultures , which restores PG synthesis , reversed the hypersensitive phenotypes toward cephalexin and vancomycin . Surprisingly , it did not suppress the fosfomycin hypersensitivity . The basis for this discrepancy is unclear . Since fosfomycin treatment downregulates the expression of genes encoding various transporters for amino acids , sugars , and cations in Staphylococcus aureus [64] , it is possible that fosfomycin may interfere with the uptake of DAP in C . crescentus . Restoring PG precursor synthesis through a genetic mutation , such as in the Δhfq vor::Tn5 strain , was sufficient to reduce fosfomycin sensitivity ( Fig 7 ) . Thus , the hypersensitivity to fosfomycin is linked to the impaired PG synthesis , similar to what we observed for cephalexin and vancomycin . Importantly , the fact that the antibiotic susceptibilities of Δhfq cells are linked to TCA cycle perturbations further supports the growing notion that the state of cellular metabolism is an important determinant for antibiotic efficacy [65–67] . Since the TCA cycle and the PG synthesis pathway described here are broadly conserved in bacteria , our results suggest a potential new strategy for combination drug therapy that exploits an accumulation in KG to potentiate the action of antibiotics targeting PG-related processes .
The strains and plasmids used in this study are listed in S5 Table , and the details of their construction are described in S6 Table . The list of oligonucleotides used in this study is in S7 Table . Unless otherwise indicated , C . crescentus strains were cultured at 30°C in PYE broth ( 2 g/L bacto peptone , 1 g/L yeast extract , 1 mM MgSO4 , 0 . 5 mM CaCl2 ) . For experiments performed in defined media , C . crescentus was grown in M2 medium ( 0 . 87 g/L Na2HPO4 , 0 . 54 g/L KH2PO4 , 0 . 5 g/L NH4Cl , 0 . 5 mM MgSO4 , 0 . 5 mM CaCl2 , 0 . 01 mM FeSO4 ) with either 0 . 2% ( weight/volume , w/v ) glucose ( M2G ) , 0 . 2% ( w/v ) xylose ( M2X ) , or a mixture of 2 mM leucine , isoleucine , and valine ( M2BCAA ) . Unless indicated otherwise , M2BCAA is supplemented with 1x Kao and Michayluk vitamin mix ( K3129 , Sigma-Aldrich ) . When appropriate , vanillic acid ( 0 . 05 or 0 . 5 mM ) was added as indicated . Antibiotics used for C . crescentus growth were as follows: kanamycin 5 μg/mL , oxytetracycline 1 μg/mL , spectinomycin 25 μg/mL , gentamicin 2 μg/mL . For growth curve measurements , overnight cultures were diluted to an OD660 ~0 . 01 in the appropriate growth medium . The diluted cultures ( 200 μL ) were then transferred into 96-well plates and grown at 30°C in a Synergy2 microplate reader ( BioTek ) . Growth curves were generated by reading OD660 every 10 min for 40–48 h . Growth rates were determined by fitting an exponential function to the early phase of the growth curves ( up to OD660 = 0 . 2 ) . When appropriate , vanillic acid and other metabolites were added at the beginning of the growth curve measurements . Prior to imaging , C . crescentus cells were cultured to an OD660 ~0 . 3 ( corresponding to exponential phase ) at 30°C and spotted on 1% agarose pads containing the same growth medium . Microscopy was performed on an Eclipse Ti-E microscope ( Nikon , Tokyo , Japan ) , equipped with Perfect Focus System ( Nikon ) , a phase-contrast objective Plan Apochromat 100X/1 . 40 NA ( Nikon ) , and an ORCA-Flash4 . 0 V2 Digital CMOS camera ( Hamamatsu Photonics , Hamamatsu City , Japan ) . All images were acquired using MetaMorph software ( Molecular Devices , Sunnyvale , CA , USA ) , and analyzed with Metamorph , Oufti [68] and MATLAB software ( MathWorks ) . Genomic DNA ( gDNA ) was extracted using ChargeSwitch kit ( Thermo Fisher Scientific ) from overnight cultures of the Δhfq strain ( CJW5477 ) grown in PYE at 30°C following the manufacturer’s recommendation . Prior to library preparation , the quality of the gDNA was assessed by measuring A260/A280 and A260/A230 ratio with a NanoDrop device ( Thermo Fisher Scientific ) and by running the sample on Bioanalyzer ( Agilent ) . Library preparation and sequencing were done by the Yale Center for Genome Analysis ( YCGA ) on a HiSeq2500 with 75 bp paired-end reads . Data analysis to identify potential mutations was performed using breseq [69] against the C . crescentus CB15N reference genome ( NC_011916 . 1 ) . Whole genome sequencing result is available in the Sequence Read Archive database with ID# SRP105792 . EZ-Tn5 transposome ( R6Kγori/Kan-2 , Epicentre ) was used to mutagenize Δhfq ( CJW5477 ) cells . To introduce the transposon , 0 . 2 μL of EZ-Tn5 was electroporated into 50 μL of competent CJW5477 cells . PYE medium ( 1 mL ) was added to the cells and the culture was incubated at 30°C for 1 . 5 h before being plated onto PYE plates supplemented with kanamycin to select for clones carrying the transposon . The plates were incubated at 30°C for 4–5 days prior to visual screening for clones that formed larger colonies . These potential ‘suppressors’ were also tested for oxytetracycline resistance to verify the presence of Δhfq::tet allele . To map Tn5 insertion sites , gDNA was extracted from 0 . 5 mL of overnight cultures growing in PYE medium using Puregene kit ( Qiagen ) following the recommended protocol . Two to four micrograms of gDNA were digested with either NcoI or SacII restriction enzymes ( both enzymes cut gDNA outside of the transposon; New England Biolabs ) , circularized using T4 DNA ligase ( New England Biolabs ) , electroporated into competent E . coli S17-1 λpir or EC100D pir-116 ( Epicentre ) cells and plated on LB agar plates supplemented with kanamycin to select for clones carrying circularized EZ-Tn5 transposon . Plasmids were then extracted from the kanamycin resistant clones and sequenced using primers specific to the transposon as recommended by the EZ-Tn5 transposome protocol ( Epicentre ) . The full details of the CRISPRi system are described in S1 Text . For metabolite extraction from filter cultures , C . crescentus strains were grown at 30°C in the appropriate growth medium until reaching OD660 ~0 . 2–0 . 4 . Approximately 1 . 5x109 cells were transferred onto 0 . 22 μm nitrocellulose or polyethersulfone ( PES ) membrane filter ( Millipore ) by vacuum filtration . The filters were then deposited on the surface of agar made with the same growth medium , and the cells were allowed to continue growing at 30°C for 2 doublings . Metabolism was then quenched by dropping the filters directly into precooled acetonitrile/methanol/ H2O ( 40:40:20 , kept at around -40°C ) . Cells were washed off the membrane filters and the entire solution was then subjected to mechanical lysis using 0 . 1 mm Zirconia beads in a Precellys tissue homogenizer ( 3 cycles of 15” on , 1’ off ) maintained at 4°C . Lysates were clarified by centrifugation at 13 , 000 x g for 5 min and then filtered across a 0 . 22 μm Costar microcentrifuge nylon filter ( Corning ) . Filtered metabolite extracts were kept at -80°C prior to analysis on LC-MS . Bacterial biomass of individual samples was determined for normalization by measuring the protein content of metabolite extracts using Pierce BCA assay kit ( Thermo Fisher Scientific ) . For DapE depletion by CRISPRi , metabolite extracts were prepared directly from liquid cultures grown at 30°C in PYE medium in the presence or absence of 0 . 05 mM vanillic acid . Approximately 3x109 exponentially growing cells were rapidly collected onto a 0 . 45 μm PES membrane filter ( Millipore ) by vacuum filtration , and metabolism was quenched by plunging the membrane filter into precooled acetonitrile/methanol/ H2O ( 40:40:20 , kept at around -40°C ) . Metabolite extraction was then performed as described for the filtered cultures . For LC-MS analysis , metabolite extracts were mixed in a 1:1 ratio with acetonitrile and 0 . 2% formic acid . After a centrifugation step ( 13 , 000 x g for 5 min ) , the extracts were analyzed on an Agilent 1200 liquid chromatography ( LC ) system with a Cogent Diamond Hydride type C column ( MicroSolv Technology , Leland , NC , USA ) coupled to an Agilent Accurate Mass TOF 6220 [70] . The mobile phase consisted of the following: solvent A ( 0 . 2% [v/v] formic acid and 99 . 8% H2O ) and solvent B ( 0 . 2% [v/v] formic acid and 99 . 8% acetonitrile ) . The gradient used was as follows: 0–2 min , 85% solvent B; 3–5 min , 80% solvent B; 6–7 min , 75% solvent B; 8–9 min , 70% solvent B; 10–11 . 1 min , 50% solvent B; 11 . 1–14 min , 20% solvent B; and 14 . 1–24 min , 5% solvent B; followed by a 10 min re-equilibration period at 85% solvent B and a flow rate of 0 . 4 mL/min . Dynamic mass axis calibration was achieved by continuous infusion of a reference mass solution ( mixture of acetic acid D4 and hexakis phosphazine ) . Metabolite identities were searched using a mass tolerance of <0 . 005 Da in Profinder 8 . 0 ( Agilent ) . Metabolite levels were quantified by integrating the area under the peak , followed with normalization for protein concentration in the extract . For this experiment , metabolite extracts were prepared from liquid cultures . C . crescentus strains were grown at 30°C in the appropriate growth media until reaching OD660 ~0 . 2–0 . 4 and ~1 . 5x109 of cells were quickly collected onto a 0 . 45 μm PES membrane filter ( Millipore ) by vacuum filtration . The membrane filter was plunged into 0 . 5 M formic acid solution ( kept at 4°C ) to quench metabolism . Cells were washed off the membrane filters and vortexed briefly . The solution was kept at 4°C for 1 h . Lysates were clarified by centrifugation , frozen at -80°C and lyophilized . Metabolites were resuspended in 10 mM Tris-HCl pH 7 . 6 , and KG was quantified using the KG assay kit ( Sigma-Aldrich ) following the manufacturer’s recommendation . The protein content in the extract was measured for normalization using the Pierce BCA assay kit ( Thermo Fisher Scientific ) . C . crescentus wild-type CB15N and Δhfq ( CJW5477 ) cells were grown , in triplicate , at 30°C in PYE until the cultures reached an OD660 between 0 . 2 and 0 . 3 . At this point , 20–40 mL of culture were harvested by centrifugation at 4°C for 5 min at ~7000 x g . Total RNA was extracted using Trizol ( Thermo Fisher Scientific ) according to the manufacturer’s protocol , except that centrifugation was performed at ~21000 x g . RNA pellets were resuspended in 100 μL of DEPC water and incubated for 5 min at 55°C . Size and integrity of the extracted RNA were assessed by electrophoresis on denaturing agarose gel . Removal of contaminating DNA was done by treating ~10 μg of total RNA with 10 units of DNase I ( Sigma-Aldrich ) at 37°C following the manufacturer’s protocol . The reactions were subjected to phenol:chloroform extraction and ethanol precipitation to purify total RNA . DNA-free total RNA was further evaluated by absorbance ratio 260/280 nm and 260/230 nm using a Nanodrop device ( Thermo Fisher Scientific ) . Samples were considered good if the ratio 260/280 nm was >1 . 9 . rRNA depletion was performed using Ribo-zero rRNA removal kit for Gram-Negative bacteria ( Illumina ) , as recommended by the manufacturer . RNA-Seq library was prepared using ScriptSeq v2 kit ( Illumina ) with multiplexing following the manufacturer’s recommendation . Sequencing was done at the Yale Center for Genome Analysis ( YCGA ) using HiSeq2000 , 1x75 bp to generate ~30–50 million reads per sample . For data analysis , sequencing reads were trimmed using Cutadapt [71] and mapped onto the C . crescentus CB15N reference genome ( NC_011916 . 1 ) using Bowtie2 [72] . The number of reads mapped to each gene was determined using HTSeq [73] and differential expression analysis was performed using DESeq2 [74] . The expression level for each gene was calculated as the number of reads mapped per kilobase of gene ( ‘gene count’ ) normalized by the 75th-percentile of all gene counts in the sample [75] . The raw RNA-Seq data is available from Gene Expression Omnibus ( GEO ) database with accession number GSE98467 . Cells were grown in PYE medium to exponential phase ( OD660 ~0 . 2 ) and 250 μL of culture was mixed with 4 mL of PYE soft agar ( 0 . 75% agarose , kept at 55–60°C ) prior to being poured onto PYE agar plates ( 1 . 5% agarose ) with or without 100 μM 2 , 6-l , l-diaminopimelate ( cat # 89469 , Sigma-Aldrich ) supplementation . The plates were dried in the fume hood for at least 10 min before being used . Antibiotics were added onto sterile 6 mm filter disks ( Sigma-Aldrich ) , dried in the fume hood for 10 min , and deposited on top of the soft agar plates . The plates were then incubated for 70 h at 30°C before measuring the zone of inhibition using a digital caliper . The reported numbers are the diameters of the clearing zone around the antibiotic-loaded filter disks . The total amount of antibiotic used per filter disk was as follows: fosfomycin 50 μg , cephalexin 50 μg , vancomycin 1 mg , gentamicin 100 μg , spectinomycin 100 μg . Total RNA was extracted from 5–10 mL of cultures using Trizol ( Thermo Fisher Scientific ) according to the manufacturer’s protocol . Contaminating genomic DNA was removed by treating ~10 μg of total RNA with 2 units of TURBO DNase ( Thermo Fisher Scientific ) , as recommended by the manufacturer . Quantitative real-time RT-PCR was performed with ~80 ng of total RNA using SYBR FAST One-Step qRT-PCR kit ( Kapa Biosystems ) following the manufacturer’s protocol . The cycling parameters used for these experiments were: 42°C for 5 min , 95°C for 3 min , 40 cycles of 95°C for 3 s and 60°C for 20 s , using BioRad CFX96 Real-Time PCR instrument . The level of ftsZ mRNA from each sample was normalized to the level of pdhA ( CCNA_01799 ) mRNA ( encoding the pyruvate dehydrogenase E1 subunit ) . Fold-change was calculated using the ΔΔCt method [76] . DNA oligonucleotides used for these experiments were irv1814/irv1815 for ftsZ and irv1771/irv1772 for pdhA ( see S7 Table for the oligonucleotide sequences ) .
|
Bacteria are well-known for their remarkable ability to multiply , a property that we often aim to control . To successfully self-replicate , bacterial cells must generate energy and building blocks through central metabolism and synthesize cell wall material to reproduce their shape and size . How cellular metabolism and cell wall growth are integrated during cellular replication remains poorly understood . In this work , we demonstrate the importance of the global regulator Hfq for maintaining the homeostasis of central metabolites in Caulobacter crescentus . Specifically , we show that accumulation of central metabolite α-ketoglutarate caused by the loss of Hfq inhibits an enzymatic reaction needed to produce cell wall building blocks . This metabolism-dependent perturbation of cell wall synthesis results in cell morphological defects and renders the cell more susceptible to cell wall-targeting antibiotics . Given that central metabolism and cell wall biosynthesis are broadly conserved , our findings suggest a new approach for combinatorial drug design .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"antimicrobials",
"cell",
"physiology",
"medicine",
"and",
"health",
"sciences",
"caulobacter",
"drugs",
"enzymology",
"microbiology",
"cell",
"metabolism",
"prokaryotic",
"models",
"metabolites",
"antibiotics",
"membrane",
"metabolism",
"enzyme",
"metabolism",
"experimental",
"organism",
"systems",
"pharmacology",
"cellular",
"structures",
"and",
"organelles",
"bacteria",
"enzyme",
"chemistry",
"drug",
"metabolism",
"research",
"and",
"analysis",
"methods",
"caulobacter",
"crescentus",
"gene",
"expression",
"cell",
"membranes",
"pharmacokinetics",
"biochemistry",
"cell",
"biology",
"genetics",
"microbial",
"control",
"biology",
"and",
"life",
"sciences",
"metabolism",
"organisms"
] |
2017
|
Crosstalk between the tricarboxylic acid cycle and peptidoglycan synthesis in Caulobacter crescentus through the homeostatic control of α-ketoglutarate
|
The RCK-containing MthK channel undergoes two inactivation processes: activation-coupled desensitization and acid-induced inactivation . The acid inactivation is mediated by the C-terminal RCK domain assembly . Here , we report that the desensitization gating is governed by a desensitization domain ( DD ) of the cytoplasmic N-terminal 17 residues . Deletion of DD completely removes the desensitization , and the process can be fully restored by a synthetic DD peptide added in trans . Mutagenesis analyses reveal a sequence-specific determinant for desensitization within the initial hydrophobic segment of DD . Proton nuclear magnetic resonance ( 1H NMR ) spectroscopy analyses with synthetic peptides and isolated RCK show interactions between the two terminal domains . Additionally , we show that deletion of DD does not affect the acid-induced inactivation , indicating that the two inactivation processes are mutually independent . Our results demonstrate that the short N-terminal DD of MthK functions as a complete moveable module responsible for the desensitization . Its interaction with the C-terminal RCK domain may play a role in the gating process .
K+ channels are found in almost every free-living organism , with a universally conserved architecture . Typically , a functional K+ channel is composed of four copies of a pore-forming subunit of two or six transmembrane ( TM ) helices with the amino ( N ) - and carboxyl ( C ) -termini usually residing in the cytoplasm . These cytoplasmic N- or C-terminal domains can control channel assembly and trafficking , as well as function as a gatekeeper to regulate the access of K+ to the ion-conducting pathway [1–5] . A particular form of control by cytoplasmic domain is exemplified by the RCK [6] ( also known as KTN [7] ) domain found in a large number of prokaryotic K+ transport systems , including ion channels and transporters [8] , and also in the animal Slo-type K+ channels [6] . The crystal structure of the RCK-containing MthK channel ( M107I mutant ) , from the archaeon Methanobacterium thermoautotrophicum , provides a relatively simple model , allowing direct structural , biochemical , and functional correlations to understand the regulatory roles of the RCK domain in K+ channels [9] . Each subunit of MthK is composed of a short cytoplasmic N terminus of 18 amino acid residues followed by a 2-TM pore-forming domain . A RCK domain of approximately 220 residues is covalently linked to the C terminus of the second TM through a linker of 18 residues ( Figure 1A , left ) . On the basis of the crystal structure of MthK ( M107I mutant ) , four copies of a separately expressed , soluble RCK domain have been proposed to interact with the four membrane-tethered RCK domains in a pairwise manner to form a “gating ring” complex . Binding of Ca2+ to the domains extends the diameter of the ring and thus accounts for the activation gating [9 , 10] . Later on , it was discovered that besides Ca2+ , MthK is also regulated by the pH of the cytoplasmic side such that the channel becomes completely inactivated at a pH value below 6 . 0 [11 , 12] . This acid inactivation has been proposed to be modulated by disassociation of a high-order RCK oligomer into dimers as demonstrated by both structural and biochemical analyses on the isolated RCK domain [10–13] . More recently , we discovered that the macroscopic current of MthK declines spontaneously after Ca2+ activation on a timescale of seconds , indicating the channel undergoes a process called desensitization [11] . This set of experiments was carried out by directly patch clamping the MthK channels expressed in enlarged E . coli membrane . However , the purified MthK channels studied in black lipid membrane ( BLM ) has not been observed to undergo desensitization [12 , 14 , 15] . This inconsistent observation raised a question as to whether this unique desensitization phenomenon observed in the enlarged E . coli system is an intrinsic molecular property or an experimental artifact . Besides the difference in the desensitization phenomenon , another set of inconsistent results was also observed from MthK studied by these two different recording systems . Single-channel analysis of purified MthK in the BLM system has shown that the open probability of MthK is drastically increased when the pH of the cytoplasmic side is raised to 8 . 0 and above , even without Ca2+ [12] . A gating ring model has been proposed to explain this alkaline-activation phenomenon in which alkaline pH extends the diameter of the octomeric gating ring in the absence of Ca2+ to open the channel gate [12] . However , this alkaline activation was not observed from the MthK studied in the enlarged E . coli system . Instead , the macroscopic currents show that MthK continues to require Ca2+ for activation followed by subsequent desensitization at alkaline pH up to 8 . 5 [11] . Additionally , our biochemical analysis of the isolated RCK domain at pH values higher than 8 . 0 has shown that the domain is predominantly monomeric when Ca2+ is absent [11] . This biochemical observation is also inconsistent with the proposed gating ring model made up by a stable octomeric RCK assembly [12] . In this report , we first demonstrate that the desensitization phenomenon is indeed intrinsic to the MthK channel , and the gating process is controlled by the cytoplasmic N-terminal 17 residues . By mutational and 1H NMR analyses , we further demonstrate that the desensitization gating mechanism may involve interactions between the N-terminal desensitization domain ( DD ) and C-terminal RCK domain . Additionally , we show that the desensitization and acid-inactivation gatings are controlled by distinct parts of the channel , and the processes are mutually independent . Finally , we demonstrate that MthK requires Ca2+ for activation in all ranges of pH as high as pH 9 . 0 , the pH at which the isolated RCK domain undergoes a monomer-to-oligomer conversion by the presence of Ca2+ .
The MthK channels studied in this work were expressed in giant E . coli spheroplasts . Patch clamping with inside-out membrane patches was coupled with a rapid perfusion system to study the time-dependent channel kinetics in a timescale of milliseconds . Patches containing approximately 100–600 active wild-type MthK channels were usually observed when the giant spheroplasts were prepared with IPTG ( isopropyl β-d-1-thiogalactopyranoside ) treatment to promote the expression . This has allowed us to study the macroscopic behavior of the channel [11] . MthK has been shown to be activated by millimolar concentrations of Ca2+ with a half-effective concentration around 8 . 5 mM at pH 7 . 5 and −50 mV [11] . When activated by 20 mM or an excessive amount ( 100 mM ) of Ca2+ , the wild-type MthK current shows spontaneous decay within seconds , indicating that the channels undergo desensitization ( Figure 1A , left and middle traces , respectively ) . Single-channel recording from giant spheroplasts without IPTG treatment reveals that the open probability of wild-type MthK decreases during the extended Ca2+ perfusion ( Figure 1A , right trace ) . In an attempt to test the functionality of MthK after being fused with the Mistic protein of Bacillus subtilis [16] to its N terminus ( see Materials and Methods ) , we found that this Mistic-MthK chimera does not desensitize to Ca2+ at either 20 or 100 mM ( Figure 1B , left and middle traces , respectively ) , though the number of active channels in an excised patch is drastically reduced . To test whether the disappearance of MthK desensitization is specific to the N-terminally fused protein , the Mistic protein was replaced with a 33-residue peptide containing a nona-histidine tag ( Materials and Methods ) . Interestingly , the resulting 9xHis-MthK chimera also does not undergo desensitization ( Figure 1C ) . These observations led us to hypothesize that the N terminus of MthK may be involved in the desensitization process in the wild-type channel . To test this hypothesis , the entire cytoplasmic N terminus of MthK , from Val2 to Lys17 , was deleted for examination . The macroscopic current of this Δ2–17 MthK channel shows rapid Ca2+ activation as the wild-type channel does , but the current does not decline during sustained Ca2+ perfusion at either 20 or 100 mM ( Figure 1D , left and middle traces , respectively ) . The channel open probability also does not decrease during the extended Ca2+ perfusion ( Figure 1D , right trace ) , indicating that the desensitization process is completely abolished in the Δ2–17 MthK . Therefore , we conclude that the short N terminus of MthK is required for desensitization . The deletion experiment described above is reminiscent of the “ball” for the N-type inactivation in the mammalian Shaker K+ channels [17] . To test the idea , an artificial aa1–17 peptide , corresponding to the first 17 residues of MthK , was synthesized and added to the perfusate to test its effect on the Δ2–17 MthK . To this end , the Δ2–17 MthK channels in an excised inside-out patch were first activated by 20 mM Ca2+ ( Ca20 solution ) for 20 s ( Figure 2A ) . The synthetic peptide was then added to the cytoplasmic side by stepping the perfusate to the same Ca2+ solution with an additional 10 μM aa1–17 peptide ( Figure 2A , red bar ) . Surprisingly , the addition of the aa1–17 peptide drastically reduces the open probability of Δ2–17 MthK at the single-channel level ( Figure 2A , upper trace ) , and causes the macroscopic current to decay down to zero in about 30 s ( Figure 2A , bottom trace ) . These results show that the synthetic aa1–17 peptide is able to inhibit the activity of Δ2–17 MthK in a way similar to the desensitization process of the wild-type channel . Note that to distinguish this inhibitory process by the synthetic aa1–17 peptide versus the desensitization process by the natural N terminal DD in wild-type channel , we will refer to the inhibitory process as peptide desensitization . The dose response of the peptide desensitization was examined between 3 to 1 , 000 μM . The time courses of the peptide desensitization can be fitted with single exponentials ( Figure 2B ) . We found that at 10 μM , the rate of peptide desensitization ( τ = 4 . 23 ± 1 . 34 s; Figure 2B , brown trace ) is close to that of the wild-type MthK desensitization ( 5 . 7 ± 0 . 6 s; Figure 1A ) , which can be regarded as the virtually local concentration of the native DD in wild-type MthK . At concentrations above 100 μM , the time constant reaches its limit of around 0 . 3–0 . 5 s ( Figure 2B , green and blue traces ) . The inhibition of the channel activity by the synthetic peptide is not permanent . The peptide-desensitized channels can be slowly , but readily , returned back to the open state after removing the peptide from the perfusate with a time constant of approximately 135 s ( Figure 2C , red lines ) . The rate of recovery back to the closed state was determined by simultaneously removing the Ca2+ and peptide using a two-activation protocol . As shown in Figure 2D , the Δ2–17 MthK channels in an excised patch were first activated by 20 mM Ca2+ for 20 s to determine the maximal activatable current before adding 10 μM aa1–17 peptide to fully desensitize the channels . The peptide and Ca2+ were then withdrawn simultaneously for various lengths of time ( Figure 2D , red bar ) to allow the peptide-desensitized channels to recover back to the closed state . The number of channels that have recovered to the closed state was determined by a second Ca2+ activation ( Figure 2D , arrow ) . By comparing the amplitudes of the two Ca2+-activated peaks , we found that the time course of the recovery has a time constant of approximately 0 . 59 s ( Figure 2D , bottom panel ) , which is about 200-fold faster than that of the recovery of wild-type MthK from the desensitized state back to the closed state ( τ ∼110 s [11] ) . Since the DD in wild-type MthK is membrane tethered at the N terminus , the much faster recovery of the peptide-desensitized Δ2–17 MthK to the closed state can be partly explained by an entropic gain of the freely diffusing aa1–17 peptide . The results thus far demonstrate that the cytoplasmic N-terminal 17 residues of MthK alone form a complete movable module , which can be deleted and added in trans to abolish or reconstitute the desensitization phenomenon , respectively . We then refer to it as the desensitization domain , DD ( Figure 3A ) . In the original X-ray structure , the N-terminal 18 residues of MthK were not resolved in the structural model ( Protein Data Bank [PDB] code 1LNQ [9] ) . Therefore , to define the residues or regions within DD that are important for the desensitization process , we performed mutational analyses within the DD by deletion and point mutation . In a series of sequential deletion , we found that the deletions within the first 11 residues often result in mutant channels with significantly altered gating properties . For examples , the deletion of two residues , from Val2 to Leu3 ( Δ2–3 ) , increases the rate of desensitization ( τ = 995 ± 82 ms , n = 5; Figure 3B ) , whereas the deletion of four residues , from Val2 to Ile5 ( Δ2–5 ) , results in an activation spike followed by a further slow current decay ( Figure 3C ) . Deletion of six residues , from Val2 to Ile7 ( Δ2–7 ) , results in partial desensitization ( Figure 3D ) , and deletion of ten residues ( Δ2–11 ) completely removes the desensitization process ( Figure 3E ) . The drastic effects of the N-terminal deletions on the gating profile led us to hypothesize that the initial hydrophobic segment , containing Met1 , Val2 , Leu3 , Val4 , and Ile5 , may be important for desensitization . These hydrophobic residues were then replaced individually by an aspartate ( D ) reside to test the idea . Interestingly , introducing the charged residue at the second ( V2D ) or third ( L3D ) position removes the desensitization process almost completely ( Figure 3F and 3G , respectively ) . At the fourth ( V4D ) and fifth ( I5D ) positions , the charged residue also significantly alters the gating property . ( Figure 3H and 3I , respectively ) . The effect of the charged residue at the initial hydrophobic segment was also tested with a synthetic N-terminal peptide , containing a L3D mutation ( aa1–17 ( L3D ) peptide ) . When tested at 100 μM , the mutant aa1–17 ( L3D ) peptide is much less effective to inhibit the Δ2–17 MthK activity ( Figure 3J , left trace ) than the wild-type aa1–17 peptide treated subsequently on the same patch ( Figure 3J , right ) . Since the disruption of the initial hydrophobicity has a profound effect on the desensitization process , we then went on to test whether this hydrophobic segment alone is able to inhibit the Δ2–17 MthK activity . Interestingly , this hydrophobic segment ( aa1–6 peptide ) , when applied at 100 μM , has almost no inhibitory effect on the Δ2–17 MthK channels ( Figure 3K , left trace ) . However , the longer aa1–11 peptide is able to inhibit the current as effectively ( Figure 3K , right ) as the aa1–17 peptide does ( Figure 3J , right ) , but with a bit higher residual activity at the steady state . If the N-terminal DD of MthK is the primary structural determinant of the desensitization process , how does it render the channel into the desensitized state ? As revealed in the crystal structure , the closest distance between Pro19 ( the N terminus of TM1 ) and the membrane-facing side of the RCK domain is about 8 Å ( PDB code 1LNQ ) . The physical proximity of the N- and C-terminal domains suggests that the DD may interact with RCK domain . To probe any possible interactions between these two domains in a sequence-specific manner , we used 1H NMR spectroscopy to analyze the behavior of synthetic DD peptides in response to the presence of isolated RCK domain in pH 7 . 5 solution . To this end , the aa1–17 , aa1–17 ( L3D ) , aa1–11 , and aa1–6 peptides , which have been functionally tested on the Δ2–17 MthK channel above , were analyzed individually by titrating the isolated RCK protein at various molar ratios ( Figure 4 ) . As shown in the bottom 1:0 traces of Figure 4A , 4B , and 4C , the aa1–17 , aa1–11 , and aa1–17 ( L3D ) peptides give three distinct narrow peaks , corresponding to the Cε1H ( 7 . 73 ppm ) and Cδ2H ( 6 . 96…6 . 98 ppm ) ring protons of the His11 , and to the CεH3–protons ( 2 . 10 ppm ) of the Met1 . With the aa1–6 peptide , the peak of CεH3 protons of the Met1 was also observed at the same position ( Figure 4D , bottom-right trace ) together with two other peaks from the protons of the C-terminal amide group ( Figure 4D , asterisks ) . These well-resolved , narrow peaks indicate that the peptides are in a rapidly tumbling state , corresponding to an unbounded form in the solution . If strong physical interactions between the peptide and the slowly rotating RCK protein occur , disappearance or displacement of these characteristic peaks will be observed . This is due to significant line broadening or shift , caused by a much shorter t2 relaxation time of the RCK protein or changes in these protons' local environment , respectively [18] . During the titration of RCK protein with peptides , significant differences in the behaviors of the four peptides were observed . For the aa1–17 peptide , the three characteristic peaks disappear at [aa1–17]:[RCK] ratios of 1:5 and 1:2 ( Figure 4A , second and third rows , respectively ) . At 1:1 ratio , broadened and lowered intensity peaks of His11 ring protons were detected ( Figure 4A , fourth row , left trace ) , but not the peak from Met1 ( right trace ) . The similar disappearance of the peaks was also observed from the aa1–11 peptide ( Figure 4B ) . Conversely , for the aa1–17 ( L3D ) peptide , all three characteristic peaks were clearly detectable at the same positions , starting from 1:5 to 1:1 ratios ( Figure 4B , arrow heads ) . For the aa1–6 peptide , the peak from Met1 was also detectable at the three titration ratios ( Figure 4D , arrow heads ) . The disappearance of the characteristic peaks of the aa1–17 and aa1–11 peptides , but not those of the aa1–17 ( L3D ) and aa1–6 ones , indicates that the aa1–17 and aa1–11 peptides have stronger interaction with the isolated RCK protein than the aa1–17 ( L3D ) and aa1–6 peptides do in a residue-specific manner . In Figure 2 , we showed that applying the DD peptide while the Δ2–17 MthK channels are in the open state can inhibit the channel activity . The decrease in the macroscopic current may be due to either open pore blockage , similar to the “ball-and-chain” model of the Shaker K+ channels , or an allosteric blockage , possibly through an interaction between RCK and DD . To delineate between these two possible mechanisms , we tested the effect of the DD peptide on the closed channels by adding the peptide to the EGTA solution . To this end , the Δ2–17 MthK channels in an excised patch were first activated twice with a 5-s interval in-between to be certain that the 5-s EGTA perfusion , after the first Ca2+ activation , is able to completely return the channels to the closed state ( Figure 5 , left trace ) . Ten or 100 μM synthetic DD peptide in EGTA solution was then added to the perfusate , after the 5-s EGTA perfusion , for 0 . 5 s prior to the second Ca2+ activation ( Figure 5 , left and right arrows , respectively ) . Since the channels have fully returned to the closed state during the 5-s EGTA perfusion , applying DD peptide afterward allows examination of the peptide effect on channels that are in the closed state . Interestingly , by comparing the amplitude of the two Ca2+-activation peaks , we found that the perfusion of 100 μM DD peptide is able to inhibit about 40% ( 43 . 3 ± 13 . 9% , n = 5 ) of the closed channels ( Figure 5 , right trace ) , whereas 10 μM DD peptide has little effect on the closed channel ( 5 . 1 ± 3 . 6% inhibited , n = 5; Figure 5 , middle trace ) . Since no channel opening was observed during the 0 . 5 s of peptide perfusion , these results demonstrate that the synthetic aa1–17 peptide , when applied at high concentration , is able to shift the equilibrium of the Δ2–17 MthK channel directly from the closed to the peptide-desensitized state . Our previous analysis has shown that the rate of macroscopic decay of the wild-type MthK current depends on the pH of the Ca2+ solution ( Figure 6A [11] ) . Since the desensitization process can now be completely removed by deleting the DD , we also examined the pH response of the Δ2–17 MthK to see whether there is a correlation between the desensitization and the acid-inactivation processes . As shown in Figure 6B , at pH above 7 . 5 , the macroscopic currents of Δ2–17 MthK show little decline ( Figure 6B , left two traces ) , whereas dramatic decay in the macroscopic currents starts when the pH is shifted to 7 . 0 or lower ( Figure 6B , right 3 traces ) , indicating that the pH inactivation is insignificant at pH above 8 . 5 and maximized at pH below 6 . 5 . This result is similar to that determined from wild-type MthK [11] , suggesting that the deletion of DD has little effect on the acid-inactivation gating process , and the two inactivation processes have no synergy effect . Single-channel analysis on purified MthK in BLM has shown that the channel open probability is drastically increased when the pH of the cytoplasmic side is above 8 . 0 , even in the absence of Ca2+ [12] . We also examined this alkaline pH effect on both wild-type and Δ2–17 MthK in E . coli membrane . Contrary to the BLM data , our analyses show that the activation of either wild-type or Δ2–17 MthK at pH values as high as 9 . 0 still requires the presence of at least submillimolar concentration of Ca2+ ( Figure 7A , upper and lower traces , respectively ) . When examined at pH 9 . 0 , the oligomeric state of the isolated RCK domain is predominately monomeric in EGTA solution and multimeric in Ca2+ solutions ( Figure 7B ) , much the same as what we previously observed at pH 8 . 5 [11] . The gating behavior of MthK at extreme alkaline pH values examined in E . coli membrane correlates well with the solution behavior of the isolated RCK domain , which supports the idea that the activation gating of MthK is controlled by oligomeric RCK conversion [11] .
Previous studies of MthK were carried out by purifying the channel proteins and reconstituting them in BLM [12 , 14 , 15] . Although this method ensures that the activities observed originate from the pure proteins , the BLM system cannot readily resolve the rapid ligand-gating kinetics because of the time required for chamber perfusion . Therefore , the gating properties previously studied in BLM may not reflect those of activation gating [19] . Biochemical analyses of the isolated RCK domain of MthK have shown that the domain is able to form various oligomeric states , including 1-mer , 2-mer , 4-mer , 6-mer , and 8-mer , depending on the pH and Ca2+ [10 , 11 , 13] . In this study , we propose that the synthetic N-terminal DD induces desensitization by interacting with the RCK domain ( below ) . Based on these observations , it is possible that the separately expressed free RCK protein , which has been shown to be copurified with the full-length MthK [9 , 14] , can bind to either the N-terminal DD or the membrane-tethered C-terminal RCK domain . Although the native form of the DD-RCK or RCK-RCK interaction is unclear , these interactions could possibly be altered during the purification and reconstitution processes . For instance , the crystal structure of the MthK ( M107I mutant ) shows that the two channels interact with each other through the member-tethered RCK domains after being extracted and purified [9] . Therefore , whether the conformation of the reconstituted MthK in the BLM system remains the same as those in the cell membrane needs to be established . To avoid possible alternations in the natural conformation , we have expressed MthK in the membrane of giant E . coli spheroplasts for direct patch clamp [11] . The desensitization property was discovered when the excised patches were bathed in a stream of perfusate that can be switched within tens of milliseconds [11] . The E . coli patch-clamp method forgoes the simplicity of BLM reconstitution but provide a natural setting to examine the channel without altering the native RCK assembly . However , a logical possibility exists that a protein native to E . coli may interact with the MthK protein from the cytoplasm , causing it to desensitize . In this study , we addressed this question by showing that the desensitization phenomenon of MthK can be removed by deleting its N-terminal DD and can be re-established by a synthetic DD peptide , and its shorter variants , added in trans . These results are consistent with the conclusion that the desensitization process is an intrinsic molecular property . To further characterize how the DD and RCK domains modulate the gating of MthK , reconstituting purified channels into an artificial liposome for patch clamp may provide a more advanced system to circumvent possible heterologous interactions in giant E . coli system , as well as to resolve rapid kinetics [20–22] . Our initial observations had reminisced the works of the N-type ball-and-chain inactivation model in the voltage-gated Shaker K+ ( Kv ) channels [17] . In Shaker-type Kv channels , the N-type inactivation was restored to inactivation peptide ( IP ) -truncated channels by a synthetic IP segment of approximately 20 amino acids located at the N-terminal end of the Shaker channel subunits [23 , 24] . The current model for Shaker inactivation is that the IP ball , as an unfolded chain , reaches the intracellular cavity of the pore through the lateral opening between T1 and the TM domain of the channel . In this model , a single IP acts like a quaternary ammonium channel blocker and can access and physically occlude the central ion pathway of an open channel [25–33] . In MthK , the DD , which is about the size of the IP ball , is directly attached to the MthK channel body without a “chain” as seen in the Shaker channels . Therefore , does the DD desensitize MthK by reaching the intracellular cavity to block the pore , or by interacting with other parts of the channel to cause allosteric blockage ? Our analyses using the synthetic DD peptide showed that the rate of peptide desensitization varies between subseconds to tens of seconds , depending on the peptide concentration . And the recovery of the peptide-desensitized channel depends on Ca2+ , such that the recovery is much faster when Ca2+ is removed than when present . This kinetic behavior of DD peptide blockage is quite different from that of the TBA ( tetrabutylammonium ) pore blockage . When examined between 0 . 01 and 10 mM , the rates of TBA blockage and recovery on open Δ2–17 MthK channels are both around tens of milliseconds ( unpublished data ) . Since the quaternary ammonium compound is known to block K+ channels by directly plugging into the intracellular cavity of the pore [31] , the much slower rates of DD peptide blockage suggest that the blocking mechanism may not simply be the same as the TBA pore blocker . Consistent with this conclusion is the observations that the shorter aa1–6 peptide has almost no blocking effect , and the aa1–11 peptide is not able to fully block the Δ2–17 MthK channels as the aa1–17 peptide does at steady state . These results indicate that the initial hydrophobic segment of the DD alone is not able to act like the quaternary ammonium pore blocker . Instead , the subsequent residues , from Ile7 to Lys17 , also play a role to complete the gating modulation . Furthermore , in Figure 5 , we showed that perfusion of DD peptide at substantially high concentration to the cytoplasmic side of Δ2–17 MthK can desensitize the channels without opening . This result indicates that the binding site for the DD peptide is accessible in the closed channel , and the DD blockage is rather allosteric . In the 1H NMR analyses , we demonstrated that the aa1–17 and aa1–11 peptides , but not the mutant aa1–17 ( L3D ) or aa1–6 peptide , directly interact with the isolated RCK domain . These binding results correlate well with the functional analyses in which the aa1–17 and aa1–11 peptides , but not the mutant aa1–17 ( L3D ) or aa1–6 peptide , are able to inhibit the K+ current . This direct correlation between protein binding and channel blockage suggests that the physical interaction of the synthetic DD peptide to the RCK domain is required to cause the peptide-desensitization . Although the results of peptide desensitization should be interpreted with caution to surmise the native desensitization mechanism , it is plausible that the N-terminal DD in the wild-type MthK interacts with the C-terminal RCK domain in the similar way to render the channel into the desensitized state after Ca2+ activation . Further functional characterizations of the natural and peptide desensitization are required to compare the differences and similarities of these two mechanisms . Note that this set of experiments highlights that an interaction between the N-terminal DD and C-terminal RCK domain may be responsible for the desensitization gating process; however , it does not exclude additional interaction of DD with other parts of the channel , including the transmembrane pore region . Given that the structure of DD is not resolved in the crystal structure of MthK ( M107I ) , further elucidating the detailed DD-RCK interaction of the entire MthK structure at atomic scale may help understanding how the interactions participate in the gating process . Results in this report together with previous studies of others provide a converging view that the gating mechanisms of activation , of activation-coupled desensitization and of the acid-induced inactivation are largely mediated by conformational changes in the RCK domain , namely the desensitization by DD and the acid inactivation by RCK disassembly into dimers at acidic pH [11–13] . For activation gating , however , it remains to be better understood how Ca2+-triggered conformational change of the RCK domains results in channel opening . In conclusion , these dynamic Ca2+- , pH- , and DD-dependent conformational changes of RCK domain underlie the mechanistic basis of MthK gating .
The gene of wild-type MthK was cloned into the pB11d vector between the NcoI and XhoI sites behind a LacUV5 promoter . The N-terminal mutants were created by designing the 5′ PCR mutant primer with a restriction cutting site compatible with NcoI for ligating into the vector . All the mutations were confirmed by DNA sequencing . The Mistic-MthK and 9xHis-MthK chimeras were made by cloning the MthK ORF into a Gateway-adapted Mistic-containing pMIS4 and a Gateway-adapted pHis9 vector [11] , respectively . The amino acid sequences before the Methionine1 of MthK in the 9xHis-MthK chimera is MKHHHHHHHHHGGLESTSLYKKAGSLVPRGSGS ( 33 residues ) . Mistic is a “membrane-integrating” protein from B . subtilis . It was originally discovered for its ability to increase the expression of eukaryotic membrane proteins in E . coli when it is fused to the N terminus [16 , 34] . We originally made the Mistic-MthK chimera to study potential effects of Mistic on the functionality of its MthK cargo . The preparation of giant E . coli spheroplasts and the patch-clamp recordings were performed following the protocol previously described [11 , 35] with minor modifications . In brief , the plasmids containing wild-type or mutant MthK were transformed in FRAG1 ( Δkch ) strain [36] for expression . The Gateway-adapted plasmids , containing the chimeras , were transformed in the BL21Star ( DE3 ) strain ( Invitrogen ) . A fresh single colony was inoculated in 5 ml of modified LB medium ( 10 g/l tryptone , 5 g/l yeast extract , and 5 g/l NaCl ) , supplemented with an antibiotic to maintain the plasmid . The culture was incubated at 250 rpm , 37 °C , until the optical density at 600 nm ( OD600 ) reaches approximately 0 . 3 , and then diluted 10-fold into a prewarmed modified LB medium , supplemented with the antibiotic and 60 μg/ml cephalexin to block cell fission . For macroscopic recordings , 0 . 5 mM IPTG was added to the culture after 2 h of incubation to promote the gene expression for 1 . 5 h . For single-channel recordings , the culture was incubated for 4 h at 250 rpm , 37 °C without adding IPTG . The expression from the basal leakage of the LacUV5 and T7 promoters allows us to obtain patches containing fewer than ten channels ( Figure 1 , right traces ) . The filamentous cells were harvested in a 1 . 5-ml Eppendorf tube by centrifugation and then resuspended with 500 μl of 0 . 8 M sucrose . Thirty microliters of 1 M Tris-HCl ( pH 8 . 0 ) , 24 μl of 0 . 5 mg/ml lysozyme , 6 μl of 5 mg/ml DNase , and 6 μl of 125 mM EDTA-NaOH ( pH 8 . 0 ) were added in sequence and mixed immediately in-between by inverting the tube a few times . After approximately 8 min of incubation at room temperature , 100 μl of Stop Solution ( 10 mM Tris-HCl [pH 8 . 0] , 0 . 7 M sucrose , 20 mM MgCl2 ) was added to terminate the digestion . The spheroplasts were directly used for patch clamp or frozen at −80 °C for later use . For all the patch-clamp recordings , the pipettes were filled with the Ca20 solution , containing ( in mM ) 10 Hepes-Tris ( pH7 . 5 ) , 500 sucrose , 150 KCl , and 20 CaCl2 . The EGTA solution contains 10 Hepes-Tris ( pH7 . 5 ) , 500 sucrose , 150 KCl , 20 MgCl2 , and 5 EGTA . The bath was filled with either the Ca20 or EGTA solution together with the giant spheroplasts for gigohm seal formation . ( The 500 mM sucrose provides osmotic protection and prevents the giant spheroplast from bursting during the seal formation . For unknown reasons , the formation of the gigohm seal and stabilization of the gigohm seal during the prolonged experimental perfusion require the presence of millimolar concentration of either Ca2+ or Mg2+ at both sides of the membrane patch . Thus , 20 mM Ca2+ or Mg2+ was added in the pipette and the EGTA solutions , respectively . ) A seal resistance of 3–5 GΩ was often reached . After being excised , the pipette tip was positioned in front of the opening of a single-walled , three-barrel glass tube ( 0 . 7-mm ID ) of the SF-77B perfusion system ( Warner Instruments ) . The perfusates were gravity fed , and the flow speed at the opening of the tubing was estimated to be approximately 0 . 5 cm/s . The speed of perfusate exchange ( from the beginning of the stepping signal to the activation of channels ) is approximately 61 ± 14 msec ( n = 75 ) . The excised membranes were held at −50 mV for all the recordings . Signals were amplified by an EPC7 Patch Clamp Amplifier ( HEKA Instruments ) . The macroscopic currents were digitized at 1 kHz by a Digidata1322A digitizer ( Axon Instruments ) and filtered at 500 Hz by an in-line eight-pole Bessel filter ( Frequency Devices ) unless otherwise stated . Single-channel currents were digitized at 25 kHz and filtered at 5 kHz ( further filtered at 1 kHz with a Clampfit 9 software for presentation ) . All statistics are shown as mean ± the standard deviation ( SD ) . The synthetic peptides , aa1–17 ( MVLVIEIIRKHLPRVLK-[NH2] ) , aa1–17 ( L3D ) ( MVDVIEIIRKHLPRVLK-[NH2] ) , and aa1–6 ( MVLVIE-[NH2] ) , were from Sigma-Genosys , and aa1–11 ( MVLVIEIIRKH-[NH2] ) was from Celtek Peptides . Stock solutions were prepared at 1–30 mM in water , and their actual concentrations were determined with a NMR using DSS ( 2 , 2-dimethyl-2-silapentane-5-sulfonic acid ) as an internal standard . Before usage , the stocks were diluted into the perfusion solutions and the pH was retitrated to 7 . 5 . Isolated RCK domain ( M107-A336 ) for NMR study were purified from the soluble fraction of the cell lysate in which the wild-type MthK was expressed from the pQE70-MthK plasmid ( a gift from R . MacKinnon ) [9 , 13] . The purified RCK protein , after thrombin cleavage of the C-terminal His-tag , was dialyzed into 20 mM Tris-HCl ( pH 7 . 5 ) , 150 mM KCl , and 1 mM DTT and concentrated to approximately 200 μM for NMR analysis . NMR spectra were recorded using Bruker Avance700 spectrometer equipped with CryoProbe . Spectra were recorded at 25 °C , with a 4-s relaxation delay . The initial peptide concentrations were verified using 0 . 1 mM DSS as a concentration standard . Chemical shifts were calibrated using DSS-Si- ( CH3 ) 3 signal as the internal standard at 0 . 0 ppm . For all peptide titration sets , NMR spectra were recorded for the following [peptide]:[RCK] ratios: 0:1 , 1:5 , 1:2 , 1:1 , and 1:0 . Peptide solutions for titration were prepared using RCK dialysis buffer ( 20 mM Tris-HCl [pH 7 . 5] , 150 mM KCl , and 1 mM DTT ) . Isolated RCK domain ( M107-A336 ) were expressed and purified as described previously [11] . The purified protein , after thrombin cleavage of the N-terminal 9xHis tag , was N-terminal sequenced , and its molecular weight ( 25 . 7 kDa ) was confirmed by mass spectrum analysis . The protein was dialyzed into 20 Tris-KOH ( pH 9 . 0 ) , 150 K+ ( Cl− ) , 5 EGTA , and 1 DTT , and concentrated to approximately 5 mg/ml for injection . The protein was further dialyzed into 20 Tris-KCl ( pH 9 . 0 ) , 150 KCl , and 1 DTT to remove EGTA . Concentrated CaCl2 solution was added to the protein sample to the desired final concentration before injection into a Superdex 200 10/300 GL column . In-line static light scattering was performed as described previously [11] .
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Nerve cells use ion channels , pores in the cell membrane , to send messages in the form of electrical signals between cells . Most ion channels have evolved several elaborate mechanisms that allow the channels to close quickly after opening to prevent wasteful leakage of the electrochemical potential—the currency of neuron communication—across the cell membrane . The process is known as inactivation or desensitization . Previous study on the model RCK-containing MthK K+ channel in the enlarged Escherichia coli membrane has shown that this archaeon channel also undergoes desensitization . Using the same method , we demonstrate that the desensitization is indeed an intrinsic molecular property of the MthK protein . We show that a specific region of MthK , the short N terminus of the protein , functions as a structurally independent domain and is entirely responsible for the desensitization gating process . Moreover , we show that this N-terminal domain interacts with the C-terminal RCK domain as part of the desensitization mechanism . This unique desensitization mechanism , by interaction between the two cytoplasmic termini , is distinct from those traditional mechanisms known as N- and C-type inactivation found in many voltage-gated Na+ and K+ channels or as the desensitization observed in the glutamate receptors . Since the KTN/RCK domain is found in a large number of prokaryotic K+ channels and transporters , this unique mechanism may be common to these transport systems for regulating the K+ flux through the cell membrane .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biochemistry",
"microbiology",
"biophysics",
"neuroscience"
] |
2008
|
The Desensitization Gating of the MthK K+ Channel Is Governed by Its Cytoplasmic Amino Terminus
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Transposable elements are driving forces for establishing genetic innovations such as transcriptional regulatory networks in eukaryotic genomes . Here , we describe a silencer situated in the last 300 bp of the Mos1 transposase open reading frame ( ORF ) which functions in vertebrate and arthropod cells . Functional silencers are also found at similar locations within three other animal mariner elements , i . e . IS630-Tc1-mariner ( ITm ) DD34D elements , Himar1 , Hsmar1 and Mcmar1 . These silencers are able to impact eukaryotic promoters monitoring strong , moderate or low expression as well as those of mariner elements located upstream of the transposase ORF . We report that the silencing involves at least two transcription factors ( TFs ) that are conserved within animal species , NFAT-5 and Alx1 . These cooperatively act with YY1 to trigger the silencing activity . Four other housekeeping transcription factors ( TFs ) , neuron restrictive silencer factor ( NRSF ) , GAGA factor ( GAF ) and GTGT factor ( GTF ) , were also found to have binding sites within mariner silencers but their impact in modulating the silencer activity remains to be further specified . Interestingly , an NRSF binding site was found to overlap a 30 bp motif coding a highly conserved PHxxYSPDLAPxD peptide in mariner transposases . We also present experimental evidence that silencing is mainly achieved by co-opting the host Polycomb Repressive Complex 2 pathway . However , we observe that when PRC2 is impaired another host silencing pathway potentially takes over to maintain weak silencer activity . Mariner silencers harbour features of Polycomb Response Elements , which are probably a way for mariner elements to self-repress their transcription and mobility in somatic and germinal cells when the required TFs are expressed . At the evolutionary scale , mariner elements , through their exaptation , might have been a source of silencers playing a role in the chromatin configuration in eukaryotic genomes .
Almost all eukaryotic genomes contain transposable elements ( TEs ) . Some of these , known as DNA transposons , move by a simple ‘cut-and-paste’ mechanism removing DNA from one site and inserting it into a new target site . Others , called retrotransposons , move via an RNA intermediate that is copied into DNA and integrated into the genome . The overall fraction of TEs that make up currently described genomes remains difficult to estimate due to the accumulation of several layers of such elements . These layers originate from TE amplification bursts at different periods during the evolution of the element , followed by ageing of the DNA sequence . Recent improvements in sequence analysis methods have showed that the human genome likely consists of at least 66–69% of repeated or repeat-derived sequences [1] , which is much higher than the 45–50% that had been reported when this genome was first sequenced . This suggests that the extent to which genomes have been shaped by TEs has probably been underestimated for many eukaryotic species . Mobility , distribution and exaptation of certain TE sequences have been considered as important sources for expansion and diversification of transcriptional regulatory networks as well as for genetic innovations [2 , 3] . Today , DNA segments derived from TEs that were exapted or inactivated over time by accumulation of mutations appear as remnants of repeated sequences of various ages . While they are rare , active TEs are still present in the genome of extant species in which de novo insertions can generate genetic variations . In multicellular eukaryotes TE insertions must occur within the germinal lineage or during early development in order to be transmitted to the following generations . This leads to the suggestion that transposition into somatic cells had no value for the TEs or their host . However , in the early 1980’s evidence began to accumulate showing that somatic TE activity ( i . e . single excision or excision followed by re-insertion ) occurred at high frequency in animal taxa . This was first shown for a DNA transposon , Tc1 in the worm Caenorhabditis elegans [4] . Recently , somatic activity was also observed for mammalian LINE-1 and dipteran R2 retrotransposons [5 , 6] . Interestingly , all of these somatic transpositions occurred in primordial cells associated with neuron-related lineages during embryonic or metamorphic development . Activation of TE transcription within some cell lineages requires that the factors silencing their expression be specifically switched off in these lineages . The Neuron-Restrictive Silencer Factor ( NRSF ) that corresponds to the Charlatan ( Chn ) protein in arthropods [7] and to the SPR-3/SPR-4 in nematodes [8] , represses transcription of many neuronal genes in non-neuronal cell types and in neuronal stem cells prior to their differentiation . NRSF binds to a 21 to 30 bp long element called the Neuron-Restrictive Silencer Element [9] ( NRSE ) . NRSF has never been shown before to interfere with TE transcription , even though NRSEs were found in human retrotransposons such as LINE2 [9 , 10] and that transcription of Tc1-like DNA transposons was shown to be activated during development of the Xenopus nervous system [11] . We report the existence of a silencer element located in the last 300 bp of the Mos1 transposase ( MOS1 ) ORF that is functional in both vertebrate and arthropod cells . This silencer is able to interfere with the transposon promoter as well as with promoters of genes located downstream of the silencer sequence . We show that the presence and location of this silencer element is conserved in mariner-like elements ( MLEs ) , even though their DNA sequences have significantly diverged . Our data reveal that YY1 , NFAT-5 , NRSF , Alx1 , GAF and GTF proteins have binding sites within these silencer elements . Furthermore , our results are consistent with the hypothesis that these silencers function with the Polycomb Repressive Complexes ( PRC ) . Together , mariner silencers might not only regulate the transcription of active MLEs , but might also modify the expression pattern of genes in which active or remnant MLEs are inserted .
Although it was originally used for another purpose ( negative controls of transposition done in absence of a transposase source ) , a stable expression assay was used to investigate whether Mos1 was able to interfere with the expression of neighbouring genes . This assay consisted in transfecting HeLa cells with plasmids containing a Neomycin Resistance ( NeoR ) marker gene and one or two Mos1 DNA segments cloned upstream or downstream of the NeoR gene ( Fig 1A ) . After two weeks of selection with G418 , resistant colonies were stained and counted . The first evidence that Mos1 could decrease the expression of a marker gene located within its neighbourhood was obtained with the Δ1[NeoR]Δ2 construct which corresponded to a complete Mos1 element containing the NeoR gene inserted in its middle . Colony numbers were at least 20-fold lower with the Δ1[NeoR]Δ2 construct than with those obtained with the [NeoR] reference ( Fig 1B ) . Further constructs were tested in an effort to locate the region responsible for the observed decrease in marker expression within Mos1 , a region we refer to hereafter as the silencer element . Results obtained with the Δ3[NeoR]Δ4 and Δ5[NeoR] constructs were not different from those of [NeoR] ( Fig 1B ) . These observations supported two explanations: i ) the silencer element was not in the non-coding terminal regions of Mos1 , ii ) the optimal activity of the silencer element was position-dependent and had an effect only when located downstream of the marker . The first explanation was supported by observations based on the [NeoR]Δ6 and [NeoR]Δ7 constructs which gave results similar to those obtained with the Δ1[NeoR]Δ2 , indicating that the silencer was located within the 3’ half of the MOS1 open reading frame ( ORF ) corresponding to the Δ7 DNA segment ( Fig 1B through 1D ) . The role of the position and orientation of the Mos1 silencer element was confirmed using four constructs in which the Δ7 DNA segment was cloned upstream or downstream of the NeoR gene , in positive ( i . e . with the piece of MOS1 ORF on the same strand as the NeoR ORF ) or negative orientations ( Fig 1E ) . Only the [NeoR]Δ7+ construct showed a strong silencer effect . Hence , the Δ7 DNA segment had a silencer effect only when located downstream of the marker gene , in the positive orientation with respect to the NeoR marker . In addition , complementary experiments demonstrated that an intragenic Δ7 DNA segment in frame with a marker gene had a silencer effect on its expression since Δ7-GFP and MOS1-GFP fusions expression is similar and significantly lower ( ~5-fold ) than the GFP control ( Fig 1F ) . These data support that Δ7-MOS1 segment has a silencer effect when it is fused in frame within a gene and that the silencer effect could be operating with the pCMV promoter . These results were confirmed by RT-qPCR using total RNA extracted from transiently transfected cells and GFP specific primers . To confirm that the Δ7-MOS1 segment contains a real silencer element , we used a transient luciferase expression assay that was previously validated to characterize silencer elements [12] ( S1 Fig ) . Our first results were confirmed with this alternative approach since the only ratio lower than 1 was obtained with the HS2_P_Luc_Δ7+ plasmid ( Fig 2A ) . In addition , they revealed that the expressions of the marker gene in the [NeoR]Δ7+ and HS2_P_Luc_Δ7+ constructs are of the same order of magnitude with respect to controls , [NeoR] ( 14 . 3x; Fig 1B and 1E ) and HS2_P_Luc ( 11 . 7x; Fig 2A ) , respectively . Therefore , these data confirmed that the Δ7-MOS1 segment contained a silencer element which is more efficient when it is located downstream of the maker gene in a positive orientation . They also confirmed that the results obtained with our stable expression assay did not reflect an ability of the plasmid to be integrated into the genome , but the capacity of the NeoR gene to be expressed post-integration . Given the above observations we decided that rather than continuing with the transient assay we should use our stable expression assay to investigate the impact of the distance separating the marker and the Δ7 DNA segment by cloning a 1 . 2 or 2 . 7 kbp spacer between them . We observed that the 1 . 2 kbp spacer had little or no impact on Δ7 silencing activity while the 2 . 7 kbp spacer decreased its activity approximately 5-fold ( Fig 2B ) . It is interesting to notice that a Δ7 DNA segment in the negative orientation located a few kbps away from the marker gene had silencing activity comparable to the one on the Δ7 DNA segment in the positive orientation . These results were verified using linearized constructs ( S2 Fig ) and were not uniform , suggesting that vector configuration is important in such experiments . An orientation effect similar to that previously observed in the absence of a spacer was found with linearized vectors containing a 3 kbp spacer . The activity of the Mos1 silencer element was tested using [NeoR]Δ7+ and [NeoR]Δ7- , our stable expression assay and two other cellular lineages originating from distantly related species: Speedy cells [13] from Xenopus tropicalis ( Amphibia ) and Sf21 cells from Spodoptera frugiperda ( Insecta ) . Our results showed that the Δ7 DNA segment had a silencer effect in both cellular systems ( Fig 3A and 3B ) , suggesting that the protein factors with which it interferes are conserved in these two species . Interestingly , the orientation effect of the silencer was recovered in Speedy cells but was absent in Sf21 cells . The mariner TE family consists of five sub-families designated cecropia , elegans/briggsae , irritans , mauritiana and mellifera/capitata [14] . Based on the phylogeny of its transposase Mos1 belongs to the mauritiana sub-family . The presence of a silencer element was surveyed within the Δ7 DNA segments of three MLEs , Himar1 , Mcmar1 and Hsmar1 , which respectively belong to the irritans , elegans/briggsae , and cecropia sub-families . Results obtained using the stable expression assay ( Fig 3C ) showed that the Δ7-MCMAR1 segment had a silencing activity with features similar to those of Δ7-MOS1 ( i . e . in terms of intensity and orientation ) . The Δ7-HIMAR1 and Δ7-HSMAR1 segments also had silencing activity that was not significantly different from those of Δ7-MOS1 and Δ7-MCMAR1 , but independent of their orientation . This result is important because it suggests that the presence of a silencer within the Δ7 DNA segment is a characteristic shared by all MLEs . It also suggests that protein factors conserved in most animal species that interfere with the mariner silencer elements might have conserved binding site motifs in their sequences . Taking into account the sequence of the active promoter in Hsmar1 [15] , a variant of transient luciferase expression assay was designed with luciferase expression plasmids containing the Mos1 and the Hsmar1 promoters ( Figs 4A and S3 ) . Our results with HeLa cells ( Fig 4B and 4C ) revealed that both promoters were active . pMos1 was found to be 10-fold less efficient than the early promoter for SV40 ( pSV40 ) under these experimental conditions . pHsmar1 was found to be two-fold more active than the pSV40 contained in the P_Luc control . When their silencer were cloned downstream of the marker gene our results revealed levels of marker expression that were lower than those of the controls ( 3 . 3-fold for pMos1 and 2-fold for pHsmar1 ) . This indicated that mariner silencers were able to negatively interfere with their own promoters . Because the closest transcriptional start site ( TSS ) upstream of the silencer element is that of the transposase ORF this mechanism is probably a way for MLEs to repress their transcriptional activity in their host cells and maintain active copies in a state of latency when host factors required for this repression are available . In the next four sections we present the results of molecular and cellular biology investigations in which the Mos1 silencer was used as the main model to elucidate the mechanism of its activity . The silencer of Hsmar1 , and in a few cases those of Himar1 and Mcmar1 , were used as complements to confirm certain results . In the final section of the results pertaining to silencer activity at the scale of a eukaryotic genome , the Hsmar1 and Hsmar2 silencers were used , as they were the only models for which in silico genomic data are available . NeoR expression was monitored for 24 hours both at the protein and mRNA levels using cellular extracts from cells transiently transfected with our constructs . Western-blot analyses ( Fig 5A ) revealed that the amount of neomycin phosphotransferase 2 ( NeoR protein ) was ~5-fold lower in cells transfected with [NeoR]Δ7- than with [NeoR] . However , few or no NeoR protein was detected in cells transfected with [NeoR]Δ7+ . This was also supported by RT-qPCR experiments ( Fig 5B ) which showed that there were respectively 5 and 20-fold fewer NeoR transcripts in cells transfected with [NeoR]Δ7- and [NeoR]Δ7+ than in those transfected with [NeoR] . Taken together these results confirmed that the Mos1 silencer element interferes with the expression of a gene marker located immediately upstream , that it acts at the level of RNA , and that the strength of the effect depends on its orientation since the amount of NeoR transcripts in cells transfected with [NeoR]Δ7- was ~4-fold higher than in those transfected with [NeoR]Δ7+ . Because the silencer element had to be located within or downstream of the marker gene to be effective , we investigated whether it directly interfered with processes occurring after transcription initiation . Transcript quality and RNA interference were examined . Polyadenylation tails of transcripts from cells transfected with [NeoR] , [NeoR]Δ7- and [NeoR]Δ7+ were investigated [17 , 18] according to their concentration in each sample , using GAPDH transcripts as endogenous controls . No difference was found , indicating that polyadenylation was unlikely to be affected . To test if the miRNA pathway was involved , Co115 human cells depleted in a key protein for miRNA processing and DICER function TARBP2 [19] were used in a transient expression assay . Similar silencing activity of [NeoR]Δ7+ was found in both Co115 ( Fig 5C ) and HeLa cells ( Fig 5D ) , suggesting that there was no link between the silencing activity of Δ7 and the miRNA pathway . In an attempt to locate a smaller fragment that would keep silencing activity in our expression assays the Δ7-MOS1 segment was fragmented ( Fig 6A ) . The Δ8-MOS1 segment , corresponding to the last 317 bp of the MOS1 ORF was found to have the same silencing activity as the Δ7-MOS1 segment ( S4 Fig ) . The size of the Δ8 segment is of interest since it was close to the upper limits for a usable protein electrophoretic mobility shift assays ( EMSA ) for investigating the binding of a TF . Several viruses and transposable elements [20–30] were previously found to contain segments capable of silencing their own transcriptional activity to establish their latency in their eukaryotic hosts . These silencers are bound by the transcription factor Yin Yang 1 ( YY1 in vertebrates , Pho in drosophila ) . In eukaryotes , YY1 and other TFs can bind a chromosomal polycomb response element ( PRE ) to mobilize the PRC1 and PRC2 and finally induce transcriptional silencing of that chromosomal region . The presence of YY1 binding sites and TF binding sites involved in PRC2 in drosophila was examined in the Δ7 and Δ8 silencer segments of Mos1 ( Fig 6B ) , Himar1 , Mcmar1 , Hsmar1 , and Hsmar2 ( S5 Fig ) . A set of binding sites for YY1 or Pho , the GAGA factor ( GAF ) , GTGT factor ( GTF ) , and Zeste [31 , 32] located among the Δ7 segments was found in all natural MLEs , each of which is typically repeated . Together the presence of these sites suggests that PRCs might be able to bind to these silencer elements , at least in dipteran species . EMSAs were carried out to verify the presence of functional YY1 binding sites in Δ8 mariner segments . Our results showed that a shifted complex was present with the Δ8-MOS1 , Δ8-HIMAR1 and Δ8-HSMAR1 probes ( Fig 6C ) . These complexes were super-shifted by anti-YY1 antibodies confirming that they correspond to YY1/Δ8 complexes . The absence of a complex with the Δ8-MCMAR1 probe suggested that the binding site located in Δ8 was not bound under our experimental conditions . Hence , the silencing element in Mcmar1 extended beyond Δ8 and might be located at the 5’ extremity of Δ7 , which contains a YY1 binding site ( S5 Fig ) . Since only one shifted band was observed with the Δ8-MOS1 segment while three binding sites were predicted in its sequence , further EMSA investigations were performed with shorter probes , Δ81 to Δ85 ( Figs 6A and 6D and S4 ) . These results were consistent with the sequence binding site prediction analysis , showing that there was one YY1 binding site within Δ81 and Δ83 , two in Δ84 , and none in Δ82 and Δ85 . This last result suggested that the motif located in 3’ was unable to be bound by YY1 under our experimental conditions . However , this was likely an artefact due to its location at the 5’ end of the Δ85 probe . Indeed , when both YY1 sites are located in the middle of the Δ84 probe , two shifted bands were observed ( Fig 6D , lane 12 ) , suggesting that both sites could be bound . Taken together , these data supported the conclusion that the silencer activity of the Δ8 segments was possibly mediated by one or several YY1 silencing pathways . Since the definition of PREs in vertebrate genomes is an issue that has yet to be fully elucidated [31 , 32] , we searched for motifs conserved for both sequence and location using the MEME software suite with the DNA sequences of 34 mariner Δ7 segments ( S6 Fig ) . A single conserved 30 bp motif was found ( p-values ranging from 3 . 36e-21 to 6 . 11e-14 ) that spanned the region coding one of the two signature motifs of mariner transposases , the PHxxYSPDLAPxD peptide [33] , and located in the region as a putative non-cardinal binding site for NRSF [34 , 35] and charlatan [36] . In mammalian genomes , approximately 80% of the 2 000 characterized NRSEs ( called RE1 ) correspond to a 21 bp motif consisting of two conserved motifs of 9 and 10 bp separated by a 2 bp linker . Approximately 12% consist of multiple rearrangements of this motif [34–36] . The remaining 8% are sites with no conserved motifs . The putative NRSE in the mariner silencer element described above belongs to the second category . EMSAs were carried out to assay these predicted NRSEs . HeLa nuclear extracts containing charlatan , human or fugu NRSF tagged with FLAG or Myc were prepared as described in previous studies [36 , 37] . The activity of the nuclear extracts was validated using an NRSE probe ( RE1 ) in EMSA ( S7 Fig ) carried out with appropriate competitor and/or antibodies [36–38] . The binding of NRSF to Δ8-MOS1 was then further investigated with EMSA using shorter versions of Δ8 , Δ81 to Δ85 segments as probes and HeLa nuclear extracts containing charlatan , human or fugu NRSF tagged with FLAG or Myc . No shifted bands were obtained with Δ83 , Δ84 and Δ85 probes . By contrast , shifted complexes sensitive to the competition by a specific competitor ( unlabelled RE1 fragment ) were obtained with the Δ82 probe for the three NRSF proteins ( Fig 7A ) . Since this probe contained the motif encoding the PHxxYSPDLAPxD peptide , we concluded that it was an NRSE . To verify whether these NRSE intervened in the silencer activity under our experimental conditions , transient luciferase expression assays were performed using constructs with the Δ8 , Δ8-ΔNRSF , or Δ8-mutNRSF of Mos1 or Hsmar1 ( S8A and S8B Fig ) cloned in positive orientation downstream of the marker cassette of HS2_P_Luc plasmids ( Fig 7B and 7C ) . Δ8-MOS1-ΔNRSF and Δ8-HSMAR1-ΔNRSF were specified by the deletion of the NRSE motif and Δ8-MOS1-mutNRSF and Δ8-HSMAR1-mutNRSF by the mutagenesis of the NRSE by randomly shuffling its sequence . Results revealed that the DNA motif encoding the PHxxYSPDLAPxD peptide , i . e . the NRSE , was not essential for the silencer activity of the Δ8 silencers of Mos1 and Hsmar1 . Two shifted complexes were also obtained with the three NRSF proteins and the short Δ81 probe in which there was one YY1 binding site and no overlap with the NRSE encoding the PHxxYSPDLAPxD peptide ( Fig 7D lanes 2 , 5 and 8; S9 Fig , lanes 2 and 5 ) . These two complexes were sensitive to competition by a specific competitor of the YY1 binding ( Fig 7D , lanes 3 and 6; S9 Fig , lane 3 ) , indicating that they involved YY1 . Interestingly , we observed that the bigger complex ( indicated by a red star in Figs 7D and S9 ) disappeared when antibodies directed against the tag of the human or fugu NRSF were added ( Fig 7D , lane 9; S9 Fig , lane 6 ) . Together these results indicated that there was a second NRSF binding site in the Δ8 silencer that required the cooperative binding of YY1 to be efficient . In spite of our efforts we failed to locate an NRSE or a charlatan binding element in this region . Therefore , it remains possible that NRSF only interacts with YY1 when it is bound to Δ8 . In order to verify whether this second site of NRSF binding was required for the silencer activity two Δ8 variants for Mos1 ( Fig 8A ) and Hsmar1 ( S8B Fig ) were generated by PCR , cloned downstream of the marker gene into HS2_P_Luc plasmid constructs in positive orientation , and tested in transient luciferase expression assays in HeLa cells . Results obtained with constructs HS2_P_Luc_Δ8-MOS1-[47–311]-ΔNRSF ( Fig 8B ) and HS2_P_Luc_ Δ8-HSMAR1-[61–311]-ΔNRSF ( S8C Fig ) revealed that the silencer activity was conserved in spite of the fact that both regions bound by NRSF proteins in Mos1 were deleted in Δ8 segments . Finally , these data supported that there were either one or two sites where NRSF was able to interfere with the Δ8 segment . However , the binding of NRSF to the mariner silencers of Mos1 and Hsmar1 was not essential to the silencer activity under our experimental conditions . Therefore , we continued our efforts to find out the regions essential for the silencer activity of the mariner Δ8 segment . In addition to the variant Δ8-MOS1-[47–311]-ΔNRSF , four other variants were made ( Fig 8A ) . The first , Δ8-MOS1-[1–152] , contained the 5’ half of Δ8-MOS1 ( i . e . one YY1 binding site plus the two NRSF binding sites ) . The second , Δ8-MOS1-[86–311] , contained the 3’ half plus the NRSF binding site overlapping the DNA motif encoding the PHxxYSPDLAPxD peptide ( i . e . two YY1 binding sites plus one NRSF binding site ) . The third , Δ8-MOS1-[116–311] was similar to the second with the exception that its NRSF binding site was removed . The fourth , Δ8-MOS1-[66–311]-ΔNRSF , was similar to Δ8-MOS1-[47–311]-ΔNRSF but its 19 residues on the 5’ end were deleted . Transient luciferase expression assays in HeLa cells revealed that all of these Δ8 variants had kept their silencer activity but with variable efficiency ( Fig 8B ) . For Δ8-MOS1-[1–152] , Δ8-MOS1-[86–311] and Δ8-MOS1-[116–311] , the luciferase expression is higher than the P_Luc control but , significantly , it was 2 . 5 to 3 . 5-fold lower than that of HS2_P_Luc ( e . g . in Figs 2A and 5B ) . This indicated that the YY1 binding site motif within the 5’ half of Δ8 and other TF binding sites within the positions 116 to 311 were enough to trigger weak silencer activity in HeLa cells . Interestingly , it also indicated that Δ8-MOS1-[47–311]-ΔNRSF and Δ8-MOS1-[66–311]-ΔNRSF had better silencer activity than Δ8 in HeLa cells . Taken together , these results suggested that several combinations of TFs could bind to Δ8 and could cooperatively act with YY1 to trigger the silencer activity . To verify whether this property could be recovered in another mariner silencer , Δ8 variants were also made from Δ8-HSMAR1 ( S8B Fig ) . Their analysis under similar experimental conditions first revealed that the 3’ half ( positions 130 to 310 ) was enough to trigger weak silencer activity in HeLa cells , but was more efficient when the DNA motif bound by NRSF and overlapping the DNA motif encoding the PHxxYSPDLAPxD peptide was present ( positions 86 to 310 ) . This indicated that this NRSF binding site favoured the silencer activity in Δ8-HSMAR1 . In addition , the two variants Δ8-HSMAR1-[61–310]-ΔNRSF and Δ8-HSMAR1-[81–310]-ΔNRSF that have sequence properties similar to those of Δ8-MOS1-[47–311]-ΔNRSF and Δ8-MOS1-[66–311]-ΔNRSF have kept a strong silencer activity , but this was significantly less strong than that of Δ8 . A search for TF binding sites motifs within the regions 47 to 96 in Δ8-MOS1 and 61 to 104 in Δ8-HSMAR1 was achieved using the MatInspector facilities of the GENOMATIX software suite ( Munich , Germany ) . Our results revealed that there were two NFAT-5 and one Alx1 binding sites in the 50 bp MOS1 segment ( Fig 8A ) , and one NFAT-5 and one Alx1 binding sites in the 44 bp HSMAR1 segment ( S8B Fig ) . Under the hypothesis that the same TFs acted on this region , results obtained with Δ8-MOS1-[47–311]-ΔNRSF and Δ8-MOS1-[66–311]-ΔNRSF on the one hand , and Δ8-HSMAR1-[61–310]-ΔNRSF and Δ8-HSMAR1-[81–310]-ΔNRSF on the other hand , suggested that both these TFs might cooperatively intervene in the silencer activity . In order to further investigate this feature the HeLa , Co115 , H4 and DT40 cells used in our work were phenotyped in order to determine their expression for NRSF , YY1 , NFAT-5 , Alx1 and TARBP2 . We found that HeLa cells were NRSF + , YY1 + , NFAT-5 + , Alx1 + and TARBP2 + . Others cells presented differences since Co115 cells were TARBP2 - , DT40 were NFAT-5 - , and H4 were NRSF—and NFAT-5 - . Taking into account these phenotypes , the effect of the Δ7-MOS1 segments in transient luciferase expression assay was analyzed ( Fig 5C and 5D and Fig 8C and 8D ) . Under these experimental conditions the absence of NFAT-5 significantly weakened the silencer effect of Δ7-MOS1 in H4 and DT40 , but did not suppress it entirely . In conclusion , our results suggested that TFs NFAT-5 , Alx1 and NRSF , might intervene alone or cooperatively with YY1 to bind to the silencer of Mos1 and Hsmar1 and elicit the silencer activity . In addition , the weak silencer activity of the region located downstream of the DNA motif encoding the PHxxYSPDLAPxD peptide of Δ8-MOS1 and Δ8-HSMAR1 might be related to the cooperative binding of YY1 and GAF and/or GTF TFs . The sequence features of the Δ8 mariner silencers described above might match those of the Polycomb Responsive Elements/Trithorax Responsive Elements ( PRE/TRE ) [31 , 39–42] that respectively silence or activate gene transcription by modifying chromatin histone marks . In order to further investigate whether the silencing depended on the polycomb pathway we used a specific inhibitor of PRC2 , the 3-deazaneplanocin A ( DZNep ) , in expression assays using plasmid constructs containing different variants of the mariner silencers of Mos1 ( Fig 9 ) and Hsmar1 ( S10 Fig ) . DZNep is an analogue of 3-deazadenosine that inhibits the activity of S-adenosylhomocysteine hydrolase , leading to the indirect inhibition of various S-adenosylmethionine-dependent methylation reactions , such as those catalysed by EZH2 in animal cells , including HeLa cells [43–45] . DZNep efficiently inhibits EZH2 after 8 h of treatment and can induce strong apoptotic cell death reaction in cancer cells beyond 48 h [43–47] . Cells were treated overnight with 5 μM DZNep prior DNA transfection and treatment was maintained until Firefly and Renilla luciferase activity measurements . Before experimenting with DZNep on mariner silencers , the impact of this chemical was verified on the Firefly luciferase expression of the P_Luc and HS2_P_Luc constructs ( Fig 9A ) . Results revealed that DZNep had no effect on P_Luc . However , this chemical decreased the capacity of the HS2 enhancer to boost the Firefly luciferase expression ( ~3 . 5-folds ) even if the difference between P_Luc and HS2_P_Luc constructs remained significant . Because our silencer DNA segments were cloned into an HS2_P_Luc plasmid backbone , expression results obtained with HS2_P_Luc in the presence or absence of 5 μM DZNep were used as references to calculate the expression rate obtained with the mariner silencer constructs in the presence or absence of 5 μM DZNep ( Fig 9B and S10 Fig ) . Results showed that DZNep significantly increased ( p<0 . 05 ) the Firefly luciferase expression from HS2_P_Luc_Δ8-MOS1 , HS2_P_Luc_Δ8-MOS1-[47–311]-ΔNRSF , HS2_P_Luc_Δ8-HSMAR1 , HS2_P_Luc_Δ8-HSMAR1-ΔNRSF and HS2_P_Luc_Δ8-HSMAR1-[61–310]-ΔNRSF . This supported the hypothesis that the Mos1 and Hsmar1 might contain functioning silencers depending on the PRC2 pathway , since the silencer activity of these constructs is decreased . Interestingly , five constructs responding to a DZNep treatment shared the presence of YY1 , NFAT-5 , Alx1 , GAF and GTF binding sites in their DNA sequences . By contrast , the Firefly luciferase expression from constructs containing the 5’ half of the Mos1 silencer ( HS2_P_Luc_Δ8-MOS1-[1–152] ) or the 3’ half of the Mos1 or Hsmar1 silencers ( HS2_P_Luc_Δ8-MOS1-[116–311] and HS2_P_Luc_Δ8-HSMAR1-[115–310] ) were not affected by the DZNep treatment . This suggested that the weak silencer effect resulting from the presence of these DNA segments might result from another silencing mechanism and is detected only when PRC2 is disrupted . Such a duality between silencing pathways was previously described for ITm TEs contained in the genome of murine ES cells . Indeed , these TEs can switch from heterochromatinization mediated by the HP1 ( Heterochromatic Protein 1 ) dependent pathway to a PRC2-dependent silencing when the Histone-lysine N-methyltransferase Su ( var ) 39/HP1 is disrupted [29] . Here , the duality between silencing pathways might also help explain why weak residual silencer effects were observed in some cases , such as in the H4 and DT40 cells ( Fig 5C and 5D ) . Since there is no available animal model with active MLEs for which high throughput chromatin data are available in public databases , we have investigated the chromatin status of two human MLEs , Hsmar1 and Hsmar2 , that appeared in the human genome approximately 50 and at least 80 million years ago respectively [48 , 49] . Currently these elements have lost their ability to transpose due to the accumulation of nucleotide mutations in the ORF coding for their transposase . The advantage of the human model is that it has the richest set of ChIP-Seq data for TFs and histone modifications . Because the recruitment of TFs bound to DNA at the moment of the establishment of histone modifications is not subsequently required for their maintenance and transmission over cell divisions [50–53] , we have focussed our investigations on histone modifications . This was carried out in order to highlight potential associations between the presence or the absence of a complete mariner silencer within each human MLE , their genomic location , and two important silencing pathways: polycomb/trithorax and Su ( var ) 39/HP1 . These two pathways lead to specific signatures of histone modifications: ( i ) H3K27me3 when the genomic loci is inactivated by PRC , ( ii ) H3K27me3/H3K4me3 when PRC and Trithorax complexes interfere together at level of inactive poised regions , ( iii ) H3K27ac/H3K4me3 when the genomic loci is activated by Trithorax complexes , and ( iv ) H3K9me3 and H4K20me1 when it is silenced and heterochromatinized by the Su ( var ) 39/HP1 pathway [39 , 41 , 54 , 55] . Since it was previously shown that human TEs carry more histone modifications when they are located within or near genes [56] , we have distinguished two categories of MLEs: those located in genes coding for proteins and those in inter genic regions . As a first step in our analysis , we inventoried the sequence features of Hsmar1 and Hsmar2 in the human genome using the hg19 RepeatMasker annotation ( S11 Fig , S1 Table ) . Among the 592 and 1240 loci containing respectively an Hsmar1 or an Hsmar2 segment , 361 and 595 contained a nearly full-length copy and 315 and 644 contained Hsmar1 and Hsmar2 Δ8 silencers . Their chromatin status ( Polycomb ( P ) , Trithorax ( T ) , Su ( var ) 39/HP1 ( H ) or a mix of these statuses ) was then inventoried in 14 human cell lines using CHIP-seq peaks ( S2 Table; S12 Fig ) . In a second step , an analysis of the chromatin status was carried out at the scale of complete populations of Hsmar1 and Hsmar2 using a silencer definition in which the sequence of Δ8 segments was complete , absent , or damaged ( Sil+ , Sil- and U ) and their genic or inter genic location in the human genome ( S1 Table ) . Results indicated that the chromatin status was only statistically defined for 25 to 71% of loci , depending on the cell type and the features of the mariner element ( S2 Table ) . Statistical analyses were carried out to test putative associations between the chromatin status , the presence of a silencer , and their genome location ( S1 Table ) . A Wilcoxon test verified the associations between the percentage of Sil+ and Sil- and the polycomb status in cell lines . A Student t-test was used to search for associations between the quantity of polycomb status in Sil+ and Sil- loci . Only one robust association was found with both tests for Hsmar2 . It revealed that Hsmar2 Sil+ has significantly more often a polycomb status than Hsmar2 Sil- in genic regions ( p value = 0 . 00428 with the Wilcoxon signed-rank test and 0 . 02084 with the t-test , see methods ) . Features of Hsmar1 and Hsmar2 elements were therefore further investigated in order to i ) verify whether genomic Hsmar1 silencer were still active and ii ) verify whether the propensity of at least a part of Hsmar2 Sil+ to have a polycomb status was due to their activity . We verified that at least a part of the Hsmar1 elements still contained an active silencer because remnants of human MLEs had accumulated significant amounts of mutations due to their age ( S11 Fig ) . Eight Hsmar1 Δ7 segments were amplified by PCR from human gDNA , sub-cloned , sequenced , and located in hg19 ( Fig 10A and 10B ) . These Hsmar1 Δ7 segments were then assayed with our stable expression and transient expression assays to verify their silencer activity . All of them were found to be strong silencers ( luciferase/renilla ratio > 0 . 5; p<0 . 05; Fig 10C ) . Their putative co-localizations with CHIP-seq peaks on their Δ8 moiety were investigated and our results showed that the chromatin status was statistically defined for 59% of cases ( S3 Table ) . They suggested that 50–56% of the 8 loci had a Su ( var ) 39/HP1 status , whatever the cell type and the loci , the other 44–50% having polycomb status . In agreement with the literature [42] , this suggested that the impact of these 8 strong silencers on the local chromatin status in somatic cells mainly depended on their genomic environment and the origin of the cells . If Hsmar1 silencers play a role in their host genome , our hypothesis is that they would intervene in chromatin organization during development or cell differentiation but not in adult somatic cells . Because lacking data about the chromatin status ( see loci with an undetermined chromatin status ( ucs ) in S1 Table ) of human mariner silencers prevented the calculation of heat maps , only Sil+ , Sil- and U located in intragenic regions and being annotated in at least seven cell lines were selected ( 187 loci , 95 Sil+ , 67 Sil- and 25 U ) to generate a heat map of the chromatin status Hsmar2 silencers ( S13 Fig ) . Both cladograms on the top and the left of the heat map indicated that there was a suitable segregation of loci which were preferentially associated with a polycomb ( green box ) or a Su ( var ) 39/HP1 status ( yellow box ) , excepted for H1-hESC . This observation about hESC was in agreement with previous works indicating that hESC had a global chromatin status that is less marked than in somatic adult cells [57] . This heat map also allowed locating loci with a bivalent status ( P/T loci in the blue box and P/H loci in the orange boxes ) . In agreement with our previous results , we observed that the density of Hsmar2 Sil+ loci associated with a polycomb status ( 91 . 5% of intragenic Sil+ ) was significantly above that of Sil- ( 73% of intragenic Sil- and U ) . Reciprocally , the density of Sil- associated to a Su ( var ) 39/HP1 status ( 27% of intragenic Sil- and U ) was significantly above that of Sil+ ( 8 . 5% of intragenic Sil+ ) . Results with intragenic Sil- and U therefore suggested that only 20% of the Hsmar2 Sil+ would have a chromatin status depending on the activity of their silencer . To verify whether the propensity of intragenic Hsmar2 Sil+ to be polycomb was due to their activity , we verified whether certain YY1 and NFAT-5 binding sites were significantly associated to the polycomb phenotype , taking into account that at least 1 YY1 and 1 NFAT-5 binding sites are required in an active mariner PRE . Because no result was statistically significant with the YY1 sites of Δ8 regions , sequences were extended in 5’ in order to match with a Δ7 segment . The YY1 and NFAT-5 binding sites were located in all Hsmar2 loci with a segment Δ7 . In agreement with the Hsmar2 consensus sequence ( S5 Fig ) , we found four YY1 binding sites at positions 11 , 382 , 431 and 475 ( Fig 11A ) of Δ7 segment and three NFAT-5 at positions 202 , 293 and 294 ( Fig 11B ) , all well conserved in numerous elements . For each binding site , a Wilcoxon test was used to verify the association between its presence and the propensity to have polycomb status in various cell lines . These tests revealed that the YY1 binding site at position 11 and the two NFAT-5 binding sites at positions 202 and 352 were significantly associated to loci with a polycomb status ( p value = 0 . 014 , 0 . 019 and 0 . 023 with the Wilcoxon signed-rank test , respectively ) . In agreement , the association of the YY1 site and one of the two NFAT5 sites in silencers was found to be significantly associated to loci with a polycomb status ( p value = 0 . 017 with the Wilcoxon signed-rank ) . Together , these results indicated that numerous intragenic Hsmar2 elements displaying the Δ7 region would contain a silencer still active in somatic cells . These results also confirmed that the size of a minimal mariner silencer was variable and depended on the MLE “species” . It corresponded to the Δ7 region in Hsmar2 and Mcmar1 ( S5 Fig ) and only to the Δ8 region in Himar1 , Hsmar1 and Mos1 .
In agreement with our hypothesis that mariner silencers function with TFs conserved among animal species we found that their activity may depend on the binding of at least five TF candidates and YY1 . The binding of NFAT-5 alongside with YY1 ( NFAT in D . melanogaster ) to the Mos1 and Hsmar1 silencers is likely the key for the activity of mariner silencers . Alx1 ( Php13-Hazy in D . melanogaster ) and NRSF are also be able to promote the silencer activity but with lower efficiency . In addition , expression data obtained with HS2_P_Luc_ Δ8-MOS1-[116–311] ( Fig 8A and 8B ) indicate that Δ8-MOS1-[116–311] keeps a weak silencer activity in spite of the absence of the NFAT-5 , Alx1 , and NRSF sites , and the YY1 site located near the 5’ end of the Δ8-MOS1 segment . This supports the conclusion that other TFs , such as GAF and-or GTF , might intervene in the silencer activity by binding to the 3’ half of the Δ8 segments ( Figs 6B and S6 ) . Since none of the Δ8-MOS1 and Δ8-HSMAR1 variants lost their silencer activity completely , it suggests that NFAT-5 , Alx1 , NRSF , GAF and GTF might function alone or more likely cooperatively with YY1 to trigger the silencing activity , depending on the cellular context . It should be noted that the variations in silencing efficiency of the Δ8-MOS1 and Δ8-HSMAR1 variants must be carefully considered . Indeed , they might also be due to the relative concentration of each TF in the various cell lines used in our assays rather than to the DNA affinity of each TF for the silencers . Together , the profiles of TF binding sites in the mariner silencers looks like the numerous PRE/TRE that have previously been described in D . melanogaster and the few well-characterized PRE/TRE in mammal genomes [31 , 32] . Because the closest TSS upstream of these MLE silencers is that of their transposase ORF , these PRE/TRE were probably originally dedicated to the repression of the MLE transposon activity in cells expressing one or several of the identified TF candidates . Because MLEs have co-evolved with their animal hosts it is no surprise to observe that they have co-evolved to use conserved TFs and host housekeeping pathways to control their activity . To our knowledge no functional link between NFAT-5 , Alx1 and NRSF in adult insects or vertebrates have been published . Indeed , NFAT-5 is primarily implicated in the response to osmotic stress [58–60] , Alx1 in osteogenesis during vertebrate development [61] , and NRSF as a negative regulator of neuronal fate by silencing neuronal-specific genes in non-neuronal cells [62 , 63] . Furthermore; NFAT-5 and Alx1 appear to share with NRSF the property of participating at different levels in the development and differentiation of the nervous system [64–67] . NRSF was also reported to be involved in non-neuronal pathways of development or cell differentiation [67–69] . Even if the role of NRSF in the functioning of mariner silencers is not yet fully elucidated it suggests that the activity of these silencers in somatic cells might be dependent on the particular development pathway being used and the cellular environment . The fact that NRSF was involved in a polycomb dependent silencer is of interest . Indeed , this TF was found to have context dependent functions for the PRC1 and PRC2 recruitments [70–72] and is able to act as a recruiter for both complexes or as a limiting factor for the PRC2 recruitment [71] . NRSF is therefore an excellent candidate to positively or negatively regulate the commitment of mariner silencers in the polycomb pathway . Confirmation of a functional interplay between NRSF and MLEs would match well with the host range of these TEs . To our knowledge , MLEs are restricted to animal genomes having a nervous system ( i . e . present in the genomes of cnidarians through arthropods and chordates ) . Nevertheless , it should be noted that in the original manuscript describing the discovery of mariner in D . mauritiana [73] mariner activity was not restricted to a particular cell type . Indeed , although the excision activity of Mos1 from the white peach locus was found to occur in neuron-like primordial cells of eye facets , it also occurs in the primordial cells of the larval Malpighian tubules and adult male testis sheaths . The silencing machineries used by mariner silencers can also explain why neo-integrated Mos1 transposons are so stable and inefficient for remobilisation in transgenic insects [74] . Indeed , when silencing pathways promote and propagate H3K27 trimethylation in the neighbouring regions of their primary binding site [75] , the MLE silencer element could extend the silent state of chromatin beyond the transposon , making it inaccessible for the transposase . Self-regulation using host silencing pathways is therefore potentially a mean to control MLE activity at two levels: transposase expression and transposon mobilization . In somatic cells TEs can either move or rearrange themselves within the genome . Therefore , they need to be finely tuned to avoid deleterious side effects due to their activity . Until now it was the TE host that was most often considered the main actors of this control or defence against TEs , using epigenetic mechanisms including RNA interference ( RNAi ) , DNA methylation and histone modifications to silence TE transcriptional activity . In spite of their widespread presence in animal genomes , master loci coding for small interfering RNA and other host mechanisms have not , so far , been demonstrated to be an important mechanism for repression of MLE transcription in animal genomes [76 , 77] . It is therefore possible that other mechanisms exist that control MLE transcription . Our results support that , just as certain viruses and endogenous retroviruses [20–30] , MLEs control their activity using a self-regulation mechanism that uses the host polycomb machinery and certain host TFs . This self-regulation would not be the only mechanism that is controlled by MLEs . Indeed , cells that temporarily do not express TFs that elicit mariner silencers also show evidence of self-repression . Two other non-exclusive mechanisms were proposed to also mediate MLE self-repression . Beyond a certain threshold of transposase concentration , the first mechanism would lead to a partial or complete transposase aggregation outside the nucleoplasm , the compartment in which MLE transposition occurs . This sequestration would likely depend on certain host proteins [78] . The second mechanism would rely on communication between transposase subunits , their concentration , and the number of transposons that can be mobilized in the environment [79] . Whatever the features of their hosts and the role of these mechanisms , it is striking that MLEs might use certain host housekeeping pathways as the main modulator of their expression . This also applies to MITE derivatives that lack a silencer ( e . g . MADE1 for Hsmar1 [80] ) , but their mobility is controlled via availability in the nuclear environment of transposases encoded by related functional elements . Overall , data accumulated on the self-management of some herpesviruses and retrovirus latency by using host silencing machineries support the suggestion that some endogenous retroviruses and MLEs are themselves the main actors of their “latency” regulation in the germ line and the soma of their hosts . This view is a breakthrough compared to the widely accepted idea that the host restrain the activities of all TEs in its genome . It also suggests that some TEs might be able to master their own invasion dynamic within their host genome , and that this would vary depending on their ability to use the host silencing machineries . This change in the conception of TE activity does not modify our understanding of their involvement in the host genome evolution . It is tempting to propose that insertions of MLEs might have had beneficial effects for their host's evolution by spurring the complexity of silencing regulatory networks [6 , 81] . The presence of human MLEs within genic regions supports this hypothesis . However , their distribution might also reflect their preference for inserting into gene loci . This could , for example , be because they would be more accessible to the MLE insertion complex . Silencers are not only located in gene promoters , several of them are scattered downstream of the TSS and the stop codon [82] . As supported by our data , such locations would not hamper the ability of each of these elements from participating in fine-tuning gene expression based on developmental stage , tissue , and cell type . Further investigations will be necessary to develop efficient experimental approaches to determine whether MLE silencers i ) use one or several host silencing pathways to be effective , ii ) have a silencer activity that is fully ubiquitous in animal bodies or have an activity that can cease at some steps of the life cycle , and iii ) were exapted several times in order to intervene in the silencing regulation networks during evolution of animal taxa [83 , 84] . Although we indicated in the result section that the data of our in silico investigations must be viewed only as a prospective study , they suggest that part of the 109 Hsmar2 silencers located in genic regions have kept their ability to induce the PRC2 silencing pathway . Taking into account the lack of transposition activity of Hsmar2 in the human genome and their sequence degeneracy , the conservation of this ability to silence chromatin might have been exapted during evolution by the host genome . Therefore , it might correspond to a putative network of Hsmar2 PRE-like interspersed in certain genic regions of the human genome . Concerning Hsmar1 , we were disappointed when we did not obtain a correlation similar to that obtained with Hsmar2 silencers . Indeed , our experimental data in HeLa cells supported that at least a part of Hsmar1 silencers efficiently silenced gene expression . However , our in silico approaches failed to reveal an impact on local histones . This could be explained by two hypotheses . The first is that Hsmar1 elements have so far not been exapted for this functionality in the human genome . Therefore , the status of their current chromatin was gradually dictated by their genomic environment throughout their evolutionary sequence inactivation . The second implies that only a small population would currently be exapted in the human genome , which hampers localizing them with our analytical approaches . Previous reports support this second hypothesis since a small part of TEs ( ~5% ) located near genes undergo purifying selection in mammal genomes , and might have regulatory functions at the levels of histone modification or gene expression [56 , 85–87] . Novel approaches will also be necessary to investigate the possible role of Hsmar1 and Hsmar2 silencers and whether they were also exapted during human evolution . As noted above , MLEs have co-evolved with their animal hosts and it is therefore not a surprise to observe that they use certain housekeeping proteins to control their activity . Even if our results do not elucidate the involvement of NRSF in the functioning of the mariner silencers and its possible links with Alx1 and NFAT-5 , we found information in various databases and in the literature indicating that part of the genes containing a mariner silencer might be related to the functioning of neuron and the central nervous system . Unfortunately , these preliminary data were not statistically confirmed using facilities of the GREAT platform [88] . Future investigations will require the development of specific approaches to further scrutinize and confirm the determinants of the mariner silencers . Another important issue will be elucidating what the development , differentiation , or physiological pathways are , how they might intervene , and to confirm that they were exapted during evolution of the human genome , and/or in any other animal genomes in which they are widespread .
Six cells lineages were used . Dr G . Sui ( Harvard Medical School , ME USA ) provided DT40 and DT40yy1- cells , Dr M . Esteller ( CNIO , Madrid , Spain . ) provided Co115 cells and Dr HY . Hwang ( Standford University , USA ) provided Speedy ( known as 91 . 1 . F1 ) cells . Sf21 cells were acquires from Sigma-Aldrich , HeLa-S3 and H4 cells from the ATCC . HeLa cells derived from human cervical cancer cells and H4 cells from malignant human glioma were cultured in DMEM ( Gibco ) supplemented with 10% fetal bovine serum ( Gibco ) . DT40 cells from chicken B lymphoma were cultured in RPMI-Glutamine ( Gibco ) supplemented with 10% fetal bovine serum and 1% chicken serum ( Gibco ) . The lineage of malignant human colorectal cells Co115 was cultured in RPMI-Glutamine supplemented with 10% fetal bovine serum . The Xenopus tropicalis speedy cell line [14] is a secondary lineage derived from a primary lineage established from a X . tropicalis limb . Cells were cultured in 67% ( v/v ) L15 medium adjusted to amphibian osmolarity by dilution with sterile water , supplemented with 10% heat inactivated fetal bovine serum ( Sigma ) and a cocktail of penicillin G ( 50U/mL ) and streptomycin ( 50μg/mL ) ( Invitrogen ) . Sf21 cells from Spodoptera frugiperda ovary were cultured in Grace’s insect medium with L-glutamine ( Gibco ) supplemented with 10% fetal bovine serum . pBlueScript SK+ plasmids were used as a vector backbone to make constructs for the stable expression assays . A [NeoR] marker cassette corresponding to a neomycin resistance gene coding a neomycin phosphotransferase 2 was cloned between the EcoRI and BamHI sites of pBS SK+ . This gene was flanked by an early SV40 promoter ( a moderate promoter ) and an SV40 terminator except for the plasmids used in Sf21 cells , where the SV40 promoter was replaced by the immediate early protein 1 promoter ( IE1; baculovirus AcMNPV ) . Each assayed DNA segment was cloned upstream ( EcoRI site ) or downstream ( NotI site ) of the marker in positive ( + ) or negative ( - ) orientation . DNA spacers of 1 . 2 kbp or 2 . 7 kbp were cloned between the 3’ end of the marker and the 5’end of the assayed DNA segment at the XbaI site as described [89] . Cells were co-transfected with approximately 150 ng of a two plasmids mix using jetPEITM as described by the manufacturer ( Polyplus Transfection ) . Two third of the mix ( 100 ng ) corresponded to the pGL3 plasmid ( Promega ) , used to check for effective transfection . One third ( 50 ng ) consisted of the assayed DNA plasmid . The amount of plasmid was fitted to its size with respect to that of the smallest plasmid used as a control in each experiment , [NeoR] . Two days after transfection , 1/3 of the transfected cells were evaluated for luciferase activity with the Luciferase Assay System Kit ( Promega ) . The remaining 2/3 of the cells were transferred in 100 mm Petri dishes followed by G418 sulfate selection ( 800 μg/mL , PAA France ) for 15 days . Cells were then fixed and stained with 70% EtOH-0 . 5% methylene blue for 3 h . Only colonies with a diameter > 0 . 25 mm were counted . Plasmid constructs are presented in S2 Fig . The fragments pMos1 , pHsmar1 , Δ8-MOS1-ΔNRSF , Δ8-MOS1-mutNRSF , Δ8-HSMAR1-ΔNRSF , and Δ8-HSMAR1-mutNRSF were synthesized by ATG:Biosynthetics . To use the plasmids containing promoter pMos1 or pHsmar1 in transient luciferase expression assay , the NcoI-BamHI DNA fragment containing the luciferase ORF and an SV40 late polyadenylation signal was purified from the P_Luc plasmid , then cloned into each of both plasmids between NcoI and BamHI sites . In pMos- and pHsmar1-Luc plasmids , the BamHI site at the 3’ end of the luciferase cassette was used to clone the DNA fragment to assay . For the transient luciferase expression assay in HeLa and H4 cells , 6 x 104 cells were seeded onto a 24-well plate one day prior to transfection . Transfection was performed using jetPEI , according to the manufacturer’s instructions , using 400 ng of test DNA and 50 ng of pRL-Tk Renilla . For DT40 cells , 5 x 105 cells were seeded onto a 24-well plate one day prior to transfection . jetPEI was also used to transfect about 400 ng of test DNA and 50 ng of pRL-Tk Renilla . For Co115 , 4 x 105 cells were seeded onto a 24-well plate one day prior transfection . For each test plasmid , its amount ( 400 ng ) was fitted to its size with respect to that of the smallest plasmid used as a control in each experiment , P_Luc . Transfection was performed with ICAFectin441 DNA transfection reagent , according to the manufacturer’s instructions ( In Cell Art ) , using 400 ng of test DNA and 400 ng of pRL-Tk Renilla . Luciferase expression was measured in a 96-well plate format with detection of fluorescence using the Dual-Glo Luciferase Assay System ( Promega ) . Measurements were recorded on a Berthold plate-reader luminometer . Similar assays were used to investigate whether PRC2 was involved in the silencing effect observed with our constructs . However , a PRC2 inhibition was achieved by adding DZNEp ( Sigma-Aldrich , USA ) in the cell culture medium from the seeding until measuring Firefly and Renilla luciferase activities . The expression profile of NRSF , YY1 , TARBP2 , Alx1 , and NFAT-5 in HeLa , Co115 and H4 cells was determined by Western-blot analysis using commercial antibodies for NRSF ( ab75785; Abcam ) , YY1 ( ab12132; Abcam ) , TARBP2 ( ab42018; Abcam ) , Alx1 ( ABIN785202; antibodies-online GmbH ) and NFAT-5 ( ABIN183505; antibodies-online GmbH ) . For DT40 and HeLa cells , expression profile was determined by RNA-seq analysis using data available in databases , GEO datasets SRX286375 and SRX083286 , respectively . During the analysis , we observed only one discrepancy between the RNA-seq data and the Western-blot analyses . Indeed , our HeLa cells were found to express NFAT-5 whereas the RNA-seq analyses done on another HeLa cell batch led to the opposite conclusion . 8 x 104 cells were seeded onto a 24-well plate one-day prior transfection and then transfected with jetPEI , according to the manufacturer’s instructions using 0 . 5 μg of plasmid DNA . Cells recovered from the culture 24 h post-transfection were washed three times with 1X PBS . The cell pellet was finally suspended in 400 μL 1X PBS-2% paraformaldehyde ( w/v ) , and stored at 4°C . The analyses were performed using a flow cytometer FACSORT and the Cell Quest program ( Beckton Dickinson ) . A total of 20 000 cells were acquired for each sample . Dead cells and debris were excluded from the analysis based on forward angle and side scatter light gating . Analysis gates were determined from the green fluorescence intensity using transfection controls done with or without plasmids expressing GFP . Annotations from RepeatMasker were used to select positions of Hsmar1 and Hsmar2 in the hg19 human genome version . Those containing a Δ8 segment ( Sil+ ) , a damaged Δ8 segment ( U ) , or no Δ8 segment ( Sil- ) and their location in a genic or an intergenic region were inventoried using home made perl scripts ( S1 Table ) . Here , genic regions corresponded to those for which maximal efficiency of the mariner silencer was observed from our experimental data ( i . e . from the TSS of each gene to 5 kbp downstream of its 3’end ) . Intergenic regions corresponded to any region of the genome that was not genic . ChIP-seq peaks files ( EzH2 , H3K27me3 , H3K27ac , H3K4me3 , and H3K9me3; no data about H4K20me3 were available for all cell lines ) were located within UCSC resources for ENCODE data and downloaded at https://genome . ucsc . edu/ENCODE/dataMatrix/encodeDataMatrixHuman . html [86] . Intersections between the location of ChIP-seq peaks and Sil+ , Sil- or U elements were performed using bioconductor . The chromatin status of each silencer was then inventoried and classified in four main categories: ( i ) undetermined chromatin ( ucs ) when no peak co-localized , ( ii ) Su ( var ) 39/HP1 ( H ) when H3K9me3 peaks co-localized , trithorax ( T ) when H3K27ac and-or H3K4me3 peaks co-localized , and polycomb when EZH2 and-or H3K27me3 peaks co-localized . Mixed statuses ( T-H , P/H , P/T , P/T/H ) were proposed when appropriate ( S1 and S2 Tables ) . 96 . 7% ( 1792/1855 ) of the Sil+ , Sil- and U elements had at least one annotation about their chromatin status . Consequently , we considered that “ucs” annotations were not due to weak mapping ability of Hsmar1 and Hsmar2 elements but rather to variation of the quality of the CHIP-seq signal probably because sequencing depths were not deep enough [90 , 91] and-or variation of “sequencing ability” from one locus to another [92–94] . Therefore , statistical analyses were performed using datasets in which polycomb , trithorax and Su ( var ) 39/HP1 frequencies were calculated and differences between MLEs Sil+/Sil- was tested using a Wilcoxon signed-rank test without “ucs” data ( S2 Table ) . Using HMMER , a HMM model for YY1 was calculated from the YY1 binding sites found in Mos1 and the consensus sequences of Hsmar1 , Hsmar2 , Himar1 and Mcmar2 . YY1 sites were then detected in genomic Hsmar2 sequences using HMMER and score for positive hits were recorded . Textual searches were done to identify putative NFAT-5 binding site using the motif AAGGG/CCCTT . The graphics calculated from data analysed with non-parametric statistics followed recommendations of the guidelines for journals of the American Society of Microbiology [95] . All values represented in graphics corresponded to the median value obtained from three experiments done in triplicate ( 9 data points ) . Bars corresponded to the values of quartiles 1 and 3 . In the text , figures and supplementary data , all the results indicated as being different were previously verified to be significant with a p-value < 0 . 05 , using a Kruskal-Wallis test . Wilcoxon signed-rank tests , Student t-tests and hierarchical clustering were done using R facilities and libraries [96] .
|
Transposons are mobile DNA sequences that have long co-evolved with the genome of their hosts . Consequently , they are involved in the generation of mutations , as well as the creation of genes and regulatory networks . Controlling the transposon activity , and consequently its negative effects on both the host soma and germ line , is a challenge for the survival of both the host and the transposon . To silence transposons , hosts often use defence mechanisms involving DNA methylation and RNA interference pathways . Here we show that mariner transposons can self-regulate their activity by using a silencer element located in their DNA sequence . The silencer element interferes with host housekeeping protein transcription factors involved in the polycomb silencing pathways . As the regulation of chromatin configuration by polycomb is an important regulator of animal development , our findings open the possibility that mariner silencers might have been exapted during animal evolution to participate in certain regulation pathways of their hosts . Since some of the TFs involved in mariner silencer activity play a role at different stages of nervous system development and neuron differentiation , it might be possible that mariner transposons can be active during some steps of cell differentiation . Interestingly , mariner transposons ( i . e . IS630-Tc1-mariner ( ITm ) DD34D transposons ) have so far only been found in genomes of animals having a nervous system .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
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2016
|
Mariner Transposons Contain a Silencer: Possible Role of the Polycomb Repressive Complex 2
|
Chandipura Virus ( CHPV ) , a negative-stranded RNA virus belonging to the Rhabdoviridae family , has been previously reported to bring neuronal apoptosis by activating several factors leading to neurodegeneration . Following virus infection of the central nervous system , microglia , the ontogenetic and functional equivalents of macrophages in somatic tissues gets activated and starts secreting chemokines , thereby recruiting peripheral leukocytes into the brain parenchyma . In the present study , we have systemically examined the effect of CHPV on microglia and the activation of cellular signalling pathways leading to chemokine expression upon CHPV infection . Protein and mRNA expression profiles of chemokine genes revealed that CHPV infection strongly induces the expression of CXC chemokine ligand 10 ( CXCL10 ) and CC chemokine ligand 5 ( CCL5 ) in microglia . CHPV infection triggered the activation of signalling pathways mediated by mitogen-activated protein kinases , including p38 , JNK 1 and 2 , and nuclear factor κB ( NF-kappaB ) . CHPV-induced expression of CXCL10 and CCL5 was achieved by the activation of p38 and NF-kappaB pathways . Considering the important role of inflammation in neurodegeneration , we have targeted NF-kappaB using a newly synthesised natural product nitrosporeusine analogue and showed incapability of microglial supernatant of inducing apoptosis in neurons after treatment .
Microglia , the developmental and functional equivalents of macrophages in somatic tissues [1] , exert a central role in a host defence and immune surveillance against infectious agents in the central nervous system ( CNS ) [2] . Microglia being multitasking act as scavengers and antigen-presenting cells in the CNS , control the proliferation of astrocytes and produce soluble factors associated with an immunologic response [3] , [4] . Under physiological conditions , microglia exist in a quiescent state lacking many of the effector functions and receptor expression patterns observed in macrophages within other tissues . However , in response to pathogen infection in the brain , microglia readily transform into an activated state , acquiring numerous if not all of the macrophage properties required to launch effective immune responses [5] . During viral infection activated microglia respond through a highly regulated network of cytokines and chemokines , which subsequently recruits the peripheral leukocytes into the CNS and orchestrate a multicellular immune response against the infectious agent [5] . Leukocytes are recruited into the CNS involves a sequence of process that can be mediated by chemokines . Chemokines are low molecular- weight and structurally related signalling molecules that are divided into four subfamilies , designated C , CC , CXC , and CX3C chemokine ligands based on the positions of their cysteine residues [6] . These molecules orchestrate efficient trafficking and recirculation of the leukocyte population within the blood vessels , lymph , lymphoid organs , and tissues , a necessary process during host immune surveillance and in acute and chronic inflammatory responses [7] , [8] . Increasing evidence suggests that CNS-resident cells secrete various kinds of signalling chemokines upon injury or infection and that attracts peripheral leukocytes , such as lymphocytes , monocytes , transmigrate toward the chemokine gradient , breaching the blood-brain barrier , and gain access to the brain parenchyma [9] , [6] . Most of the chemokines expression is regulated primarily at the level of transcription through activation of a definite set of transcription factors , such as nuclear factor κB ( NF-kappaB ) and interferon ( IFN ) regulatory factors [10] . It has also been reported that signal transduction pathways mediated by the mitogen-activated protein kinase ( MAPK ) family , including c-Jun N-terminal kinase ( JNK ) , and p38 , contribute to the activation of transcription factors [11] , [12] . Environmental stresses , such as bacterial endotoxins , proinflammatory cytokines , osmotic shock , UV irradiation , and virus infections are reported to activate p38 and JNK [13] , [14] . CHPV belonging to the Rhabdoviridae family has been a severe threat to the population in the Indian subcontinent , and several outbreak has claimed many lives for more than a decade [15] . It mainly infects children below 15 years and characterises influenza-like symptoms . It is a neurotropic virus that is transmitted majorly through sand flies , mosquito and ticks [16] . There is no specific treatment available to date; symptomatic treatment involves the use of mannitol to reduce brain edema . Previous studies , as well as data available from our lab , shows the role of microglia secreted inflammatory molecules in neurodegeneration in CHPV infected mouse [17] , [18] , [19] . In the present study , we have systemically examined the effect of a Nitrosporeusine derivative ( - ) -25b , an anti-inflammatory molecule against CHPV infection . Compound ( - ) -25b is one of the optimised compounds from the library of analogues synthesised based on a natural product Nitrosporeusine alkaloid of marine origin and has found to be effective against LPS induced inflammation in the mouse . Here , we have used Nitrosporeusine analogue ( - ) -25b , which was found to show the best activity against CHPV infection . Previously , we have demonstrated the capability of virus-induced microglial supernatant in inducing neuronal death in HT22 cells as well as primary neurons [17] . In this work we have targeted inflammation using nitrosporeusine analogue and showed the delayed survivability and disease progression post-CHPV infection . We also show that CHPV infection of microglia actively induces the gene expression and protein production of two chemokines , CXC chemokine ligand 10 ( CXCL10 ) and CC chemokine ligand 5 ( CCL5 ) . The activation of NF-kappaB and chemokines upregulation leads to infiltration of peripheral leukocytes and monocytes further deteriorating the condition . Furthermore , our data indicate that the CHPV-induced production of CXCL10 and CCL5 is positively and negatively regulated by the activation of cellular signalling pathways mediated by and NF-kappaB . Nitrosporeusine analogue ( - ) -25b directly or indirectly targets NF-kappaB activation , further inhibiting sequence of disease leading to the improved disease condition .
All animal experiments were approved by the Institutional Animal and Ethics Committee of the National Brain Research Centre ( approval no . NBRC/IAEC/2013/88 ) . The animals were handled in strict accordance with good animal practice as defined by the Committee for Control and Supervision of Experiments on Animals , Ministry of Environment and Forestry ( CPCSEA ) , Government of India . CHPV ( strain no . 1653514 isolated from the human patient in Nagpur , 2003 ) was propagated in Vero cell line . The virus was propagated in the Vero cell line , and viral titer was measured using plaque assay , which was found to be 3×109 pfu/ml . HT22 ( immortalised mouse hippocampal neuronal cell line was gifted by Dr Shiv Kumar Sharma , National Brain Research Centre ) cells were used for our experiment with prior permission from Dr Dave Schubert of Salk Institute from whom these cells were initially obtained . HT22 cells were grown at 37 °C in Dulbecco’s modified Eagle medium ( DMEM ) supplemented with 3 . 7% , Sodium bicarbonate ( Sigma , USA ) , 10% FBS ( Gibco , Thermo Fisher Scientific , USA ) and penicillin-streptomycin ( Sigma , USA ) . Mouse microglial cell line N9 was gifted from Prof . Maria Pedroso de Lima , Center for Neuroscience and Cell Biology , University of Coimbra , Portugal . The cell lines were grown at 37 °C in Roswell Park Memorial Institute medium ( RPMI-1640 ) supplemented with 10% fetal bovine serum , 100 units/ml penicillin , and 100 μg/ml streptomycin . Nitrosporeusines A and B are two recently isolated marine natural products with a novel skeleton and exceptional biological profile . Our previous data suggest it as a potential candidate possessing anti-inflammatory property [20] , so we planned and screened racemic as well as enantiopure forms . Preparation of several analogues followed the natural product synthesis , and all the synthesised compounds were evaluated for in vitro and in vivo anti-inflammatory potential . After extensive screening , an enantiomer ( - ) -25b was found to be most effective in CHPV induced inflammation . Animals were injected with 20mg/Kg body weight intraperitoneally . N9 microglial cells were used for experimental purpose . N9 cells were cultured in 10% serum containing media and were seeded in 60 mm plate at the density of 5×105 . After 12 hours the media was replaced by serum-free media to limit the growth rate of the cells so that a particular number of cells can be monitored for infection . Post 2 hours incubation N9 cells were infected with CHPV at the multiplicity of infection ( MOI ) 0 . 1 for 1 hours , and then cells were washed twice with 1X PBS to remove non-internalised virus present in media and then , nitrosporeusine ( - ) -25b was added at the concentration of 100μM . Cells at various post-infection time points were harvested , and the culture supernatants were collected and stored at − 80 °C . Mouse hippocampal cell line HT22 was plated at the density of 105 cells/well in 2-well chamber slide with serum containing media . Cells were incubated with UV inactivated N9 supernatant as per infection paradigm mentioned previously . At 12 hours post-infection period , cells were subjected to In situ Cell Death Detection Kit , TMR red as per the manufacturers’ guidelines ( Roche , Germany ) . Brain sections were washed with 1X PBXT and then blocked in 5%BSA for 1 hours . Further tissue was processed with TUNEL kit as described . An equal area from every section was taken , and cells were counted from it . Five sections were scored for each sample and average was calculated . Counting was done manually using ImageJ software . N9 cells and mouse brain tissues under different treatment conditions were harvested for obtaining total cellular extracts , and the protein isolation procedure and immunoblotting steps were performed according to standard procedure . After being blocked with 5% skimmed milk , the membranes were incubated with primary antibodies against NF-kappaB ( Cell signalling , USA ) , Cleaved caspase-3 ( Abcam , USA ) , iNOS ( Abcam , USA ) , Cox-2 ( Santa Cruz , USA ) , p-p38 ( Cell signalling , USA ) , p-JNK ( Cell signalling , USA ) , p-Iκκα/β ( Cell signalling , USA ) , p-Akt ( Cell signalling , USA ) , p38 ( Cell signalling , USA ) , JNK ( cell signalling , USA ) , NF- kappaB ( Cell signalling , USA ) , Akt ( Cell signalling , USA ) , Claudin-1 and occludin ( a kind gift from Dr . Guruprasad Medigeshi , Translational Health Science and Technology Institute , Faridabad ) , β-catenin ( Abcam , USA . a gift from Dr . Ellora Sen , NBRC ) and CHPV ( a kind gift by Bharat Biotech International Limited , Hyderabad , India ) at 1:1 , 000 dilutions . After extensive washes with 0 . 1% PBS-Tween , blots were incubated with the Anti-Rabbit peroxidase-conjugated secondary antibodies ( Vector Laboratories , USA ) . The blots were then processed for development using chemiluminescence reagent ( Millipore , USA ) . The images were captured and analysed using the Uvitech Cambridge using NineAlliance software ( Uvitech , United Kingdom ) . β -actin antibody ( Sigma , USA ) at 1:10 , 000 dilutions was used as loading control . Intracellular ROS production in Mock and treated cells was assessed using the cell permeable , non-polar H2O2 sensitive dye 5- ( and-6 ) -chloromethyl-2′ , 7′—dichloro dihydro fluorescein diacetate ( CM-H2DCFDA ) ( Sigma Aldrich , USA ) as described previously [17] . The extent to which H2O2 is generated is defined as the extent of ROS generation . Briefly , N9 cells of the different group , i . e . mock , CHPV infected and ( - ) -25b treated group were cultured , and then in serum-free media , it was infected and treated with ( - ) -25b . Incubation in serum-free media further followed this for 3 hours after which , the cells were further treated with H2DCFDA ( 1 μM ) for 30 minutes at 37 °C . Cells were washed twice with 1 × PBS , and fluorescent intensity of the cells was measured using in BD FACS verse in FACSuit software . Plaque assay was performed following previously published protocol [17] . Vero cells were grown till complete monolayer formation in 10% FBS containing DMEM seeded in 6 well plates at the density of 4×104 cells/well . After complete monolayer formation was achieved serum-containing media was changed to serum-free media and incubated for 2 hours to acclimatise the cells for serum starvation . Meanwhile , serial dilution was prepared in serum-free DMEM starting with a 1:10 dilution of the stock solution . The stock solution was serially diluted using 10-fold dilutions . Each dilution was added to each well of Vero cells . After two hours of incubation with the respective dilutions at 37 °C , supernatants were removed and washed twice with 1X PBS to avoid multiple infection cycles . 3ml of agarose overlay ( 9ml 2% agarose ( Roche , Germany ) , 10ml 2X Minimal essential media ( Sigma , USA ) , 1ml FBS ( Gibco , Thermo Fisher Scientific , USA ) , 100μL penicillin-streptomycin ( Sigma , USA ) ) was then added to each well . The plate was kept at 4 °C for solidifying the overlay after which it was returned to 37°C for incubation of 24 hours . 4% PFA was added post-incubation period for fixation of the cells for further analysis . Subsequently , the overlay was removed , and cells were stained with crystal violet and plaque was counted . The viral titers were expressed as PFU/ml , calculated as [ ( number of plaques per well ) × ( dilution ) ]/ ( inoculum volume ) . Mouse brain tissue and harvested N9 cells were homogenised using trizol reagent as per manufacturers’ protocol ( TRI reagent , Sigma , USA ) . For qPCR analyses , cDNA was synthesised using Advantage RT-for-PCR kit ( Clontech Laboratories , CA ) . Oligonucleotide primers specific genes , e . g . CHPV , CCL5 , CXCL10 , I-CAM , VCAM and MMP-9 , were used as mentioned in Table 1 . Power SYBR Green PCR master mix ( Applied Biosystems ) was used for the experiment . The qPCR results were analysed as per the user manual guidelines . The CBA kit ( BD Biosciences , NJ , USA ) was used to quantitatively measure cytokine levels in the N9 cells supernatant as well as mouse brain lysate . 50 μl of mouse inflammation standard and sample dilutions were used to perform , the assay according to the manufacturers’ instructions and analysed on the BD FACS Verse ( Becton Dickinson , CA , USA ) . A similar protocol was followed to analyse the cytokine levels for brain samples post-CHPV infection . Nitric oxide released from N9 supernatant following CHPV infection was assessed using Griess reagent as described previously . Briefly , 100 μL of Griess reagent ( Sigma , St . Louis , USA ) was added to 100 μL of supernatant and incubated in the dark for 15 minutes . The intensity of the colour developed was estimated at 540 nm with the help of a Benchmark plus 96-well ELISA plate reader ( Biorad , CA , USA ) . The mean fluorescent intensity was plotted for each sample . [17] . Fluorescence immunohistochemistry was performed for Mouse anti-Iba-1 ( 1: 300 , Chemicon , USA ) for activated microglia . For peripheral immune cells CD3 ( 1:200 , Millipore , USA , ) a marker for activated T-cells , CD-68 ( 1:400 , Abcam , USA ) a monocyte lineage marker and CD11b ( 1:200 , Millipore , USA ) a macrophage marker were used in brain samples . The corresponding secondary antibodies were used: goat anti-mouse Alexa Fluor 488 ( 1:1000; Molecular Probes ) for Iba-1 , goat anti-mouse Alexa Fluor 594 for both CD3 , CD11b and Rabbit anti-Rat fluorescein for CD68 Cell nuclei were stained with DAPI . ELISA was performed to examine protein expression level of chemokines . N9 cells were cultured and seeded in 60 mm dish , and then standard infection was followed as described earlier . Then supernatants at the different time point were collected , and ELISA was performed as protocol described . In short , plate was coated with antibody of CXCL10 ( R&D System , USA ) and CCL5 ( R&D System , USA ) ( diluted in coating buffer to 1 μg/ml ) using coating buffer and then incubated for overnight . Next day after blocking for an hour in blocking buffer ( 1% BSA in PBS ) supernatant was added to each well ( 100 μl ) and incubated at RT for 6 hours , followed by three PBST wash . Then secondary biotin antibody ( R&D System , USA ) was added in each well ( 100 μl diluted in blocking solution 1 μg/ml ) , and incubated at RT for 30 minutes followed by three PBST wash and then incubation with streptavidin ( Vector Laboratories ) for 30 minutes at RT . The substrate ( TMB solution , Vector Laboratories ) was added and was incubated for 20 minutes and then stop solution was added for 100μl per well . Optical Density was measured at 450nm ( Tecan infinite M200Pro , Switzerland ) . For mouse brain samples ELISA was performed from mouse brain protein samples using protocol mentioned above . Inhibition of MAPK , Akt and NF-kappaB signalling in microglia was carried out by specific inhibitors . Briefly , cells were incubated for 1 hours at 37°C in test media containing SB202190 for p38 , SP600125 for JNk , LY294002 for Akt and Dexamenthasone for NF- kappaB at 10μM concentration were used just before the experiment and were subjected to the analyses as mentioned above in the presence of these inhibitors . Under the assay conditions , these inhibitors did not induce any cytotoxic effects as judged by a dye exclusion test using trypan blue . CHPV inactivation was carried out with a UV cross-linker ( UVC 500 , Hoefer scientific , USA ) using short-wavelength UV radiation ( UVC , 254 nm ) at a distance of 5 cm for 25 minutes on ice as described earlier [17] . Inactivation of virus was verified by plaque assay for all three sets of treated supernatant which showed the absence of viral plaque formation in the UV treated culture supernatant . Experiments with paired treatment were analysed by t-tests . Experiments with >2 treatments were analysed by ordinary one-way analysis of variance ( ANOVA ) as appropriate with Holm-sidak correction for multiple tests . Prior to analysis data were tested for adherence to normality using the Shapiro-Wilk normality test . All analyses were conducted using GraphPad 13 . 0 .
Three different groups were mock treated , CHPV infected and nitrosporeusine treated after infection were analysed for survivability after infection . Kaplein-Meir graph plotted for our data shows mice treated with compound ( - ) -25b has enhanced survivability as compared to CHPV infected group ( Fig 1A ) Drastic weight loss is inevitable post infection , so we checked for disease progression by recording weight loss of animal post infection and we found delayed onset of symptom in compound ( - ) -25b treated group ( Fig 1B ) . After the appearance of full symptom in CHPV infected mice brain sample was collected from all three groups for experiments . Next was we analysed viral load in the brain samples through plaque assay and found a significant reduction in viral load in analogue treated samples ( Fig 1C ) . Similarly , qPCR data for the viral gene was found to be consistent with previous data ( Fig 1D ) . Moving on next , we checked for cell death using TUNEL assay as neurodegeneration is the hallmark of CHPV infection [21] . TUNEL data shows decreased positive cells in compound ( - ) -25b treated group as compared to only CHPV infected group ( Fig 1E ) . Western blots of N-protein of CHPV and cleaved Caspase 3 showed the significant reduction in expression in nitrosporeusine treated samples ( Fig 1F ) . Concomitantly these observations helped us to conclude that nitrosporeusine analogue hinders the replication of the virus in neurons and hence neurodegeneration in vivo . Chronic activation of microglia leads to secretion of cytokines that have the deleterious effect on neurons and acts as a significant player of neurodegeneration post-CHPV infection [21] . We investigated the functional profile of activated microglia by CBA analysis of inflammatory cytokines in N9 cells at 6 hpi revealed the effectivity of ( - ) -25b against the inflammation induced by CHPV infection . Analysis of TNF-α , CCL2 and IL-6 shows many-fold increase in cytokine level which decreases sharply in ( - ) -25b treated group as compared to only infected group ( Fig 2A ) . The bar graph shows relative fold change in expression of cytokines with mock . We validated the same data in the animal brain at 2dpi and were found to be correlated with in vitro data . CBA analysis of TNF-a , IL-6 and CCL2 shows a sharp decrease in cytokine level post ( - ) -25b treatment . Bar graphs show relative fold change with mock ( Fig 2A ) . Activated microglia are known for secretion of inflammatory mediators . Microglial activation was examined using Iba-1 antibody and found a decrease in the number of star-shaped cells , i . e . activated microglia in ( - ) -25b treated sample as compared to only infected samples ( Fig 2B ) . Generation of reactive oxygen species ( ROS ) and nitric oxide ( NO ) by microglial cells in response to infection is a crucial marker for oxidative stress in these cells . Further , we checked for ROS generation post CHPV infection which shows a decrease in ROS generation in ( - ) -25b treated samples . Nitric oxide measurement also indicates the reduction in its expression level in drug-treated samples ( Fig 2C ) . Our earlier reports suggest upon activation; microglia secretes iNOS and COX-2 [21] . We found a decrease in expression level of iNOS and COX-2 in N9 cells as well as brain samples ( Fig 2D ) . This data shows a significant reduction in inflammation upon ( - ) -25b treatment in virus-infected cells as well as in the brain . It is a well-documented fact that aggravated inflammation during viral infection leads to secretion of chemokines . These chemokines are meant for attracting immune cells at the site of infection , but uncontrolled increase creates a hostile environment for cells . We checked for different chemokines using qPCR and found a rise in CXCL10 and CCL5 post-CHPV infection ( Fig 3A ) in N9 cells . To check if enhance mRNA expression pattern is correlated with protein; ELISA was performed for CXCL10 and CCL5 proteins ( Fig 3B ) . Our ELISA data for CXCL10 and CCL5 shows an increase in expression after CHPV infection in N9 cells at 6hpi , which decreases significantly post ( - ) -25b treatment . Chemokine expression level was then checked in mouse brain sample . Our qPCR data for mRNA expression level shows a significant increase in CXCL10 and CCL5 at 2dpi . Then this data was further validated at protein expression level by ELISA . Our data was found to be correlated with PCR data suggesting enhanced expression of CXCL10 and CCL5 and were found to be a multifold increase in expression level of these chemokines at 2dpi ( Fig 3C & 3D ) . Taken together , these data demonstrate that the CHPV-induced expression of CXCL10 and CCL5 is triggered at the stage after viral infection . Enhanced chemokine level leads to infiltration of peripheral leukocytes and monocytes into the brain . To assessed the functional relevance of increased chemokines with leukocyte infiltration we performed IHC staining of brain sections which reveals the presence of CD3 positive cells in brain suggesting presence of activated T-cells which decreases significantly in ( - ) -25b treated section ( Fig 4A ) . Similarly CD68 a monocyte marker and CD11b a macrophage lineage marker shows strong presence in infected brain samples ( Fig 4A ) . Presence of peripheral cells encouraged us to check for BBB permeability . Occludin , Claudin -1 and β-catenin are tight junction protein which gets manipulated during pathogenic infection and blood-brain barrier breaching . We performed western blot to check expression level of these proteins in the presence of virus as well as in drug treatment . Our data show a significant decrease in expression of occludin post-CHPV infection which was found to be significantly higher in ( - ) -25b treated samples ( Fig 4B ) . Similarly , claudin-1 and β-catenin shows similar data suggesting suppression of CNS inflammation control BBB breaching . We performed qPCR to check for the expression level of BBB regulatory genes . Our data shows enhanced expression of matrix metalloproteinases 9 ( MMP-9 ) , ICAM , & VCAM in CHPV infected brain which decreases sharply after ( - ) -25b treatment ( Fig 4C ) . Our data signifies BBB breaching and infiltration of peripheral cells in the brain after CHPV infection . A definite set of transcription factor governs chemokine expression . The enhanced production of CXCL10 and CCL5 in CHPV-infected microglia implies the possibility that CHPV infection may stimulate the cellular signalling pathway underlying chemokine expression . Considering the importance of MAPKs in gene regulation and NF-kappaB in disease and pathogenesis we examined activation level of NF-kappaB , p38 , and JNK . In order to assess the activation of MAPK signalling pathways in microglia during the course of CHPV infection , cells were mock infected or infected with CHPV , and Western blotting examined the degrees of phosphorylation . We found CHPV infection in microglia leads to activation of NF-kappaB , p38 , JNK , and IKKα/β in N9 cells . Western Blot analysis shows increase expression of NF-kappaB , p38 , IKKα/β and JNK in CHPV infected cells which decreases in ( - ) -25b treated N9 samples at the early time point as 3hpi ( Fig 5A ) . We then moved to further validate the expression in brain samples . Mouse brain was processed for western blot , to check expression level of same genes as was in N9 cells . During early time of infection , brain protein shows increased expression of NF-kappaB , p38 , IKKα/β and JNK in CHPV infected brain samples . Here also we found effectivity of ( - ) -25b and can significantly downregulate expression level of these proteins ( Fig 5B ) . The bar graph shows relative fold change in expression level of proteins . Analysis of the CHPV-induced expression of cytokines and chemokines in the presence of MAPK inhibitors have revealed that the p38 and JNK pathways are not dominant in the production of these chemokines , but NF-kappaB inhibitor severely reduces cytokines level . We used the specific inhibitor of p38 ( SB239063 ) , JNK ( SP600125 ) and NF-kappaB at a concentration of 10μM to check chemokines and cytokines production . Our data suggest dexamethasone; a known NF-kappaB inhibitor is potent in blocking cytokines and chemokines level as compared to other two inhibitors . The p38 inhibitor was found to be effective against the production of CCL-2 only whereas JNK inhibitor was not impressive against cytokines and chemokines generation ( Fig 6A ) . Similarly , chemokine level was also found to decrease significantly in the dexamethasone-treated sample as compared to other inhibitors . This data suggest a critical role of NF-kappaB in CHPV mediated inflammation ( Fig 6A & 6B ) . Now we wanted to explore if viral infection is required for the microglial based inflammatory response or just virus interaction is sufficient to induce activation . We used UV inactivated virus and infected N9 cells , and followed by analysis of cytokines level using CBA . Our data reveal inactivated viruses are incapable of inducing cytokine level . We didn’t find any significant change in the level of TNF-a , CCL-2 and IL-6 in inactivated virus treated sample as compared to mock ( Fig 6C ) . This data suggest active virus is required for microglial activation . The next question asked was the pathway through which NF-kappaB gets activated . Several reports , shows the importance of Akt in NF-kappaB activation and Akt as a downstream molecule in NF-kappaB activation [22] . Additionally it is also reported that PI3K/Akt pathway is involved in NF-kappaB dependent CCL5 secretion . We have demonstrated the role of p-Akt in activation of the NF-kappaB pathway in N9 cells . Using PI3K inhibitor ( LY294002 ) at 10μM , which further inhibits Akt phosphorylation shows a decrease in NF-kappaB level ( Fig 6D ) . This data shows inhibition PI3k/Akt signalling inhibits NF-kappaB activation , suggesting role of Akt pathway in CHPV induced NF-kappaB activation . Our data confirmed the role of NF-kappaB activation in CHPV induced microglial activation that is taking place through PI3K/Akt pathway . CHPV activated microglial supernatant are capable of inducing neuronal death[21] . Here , we treated N9 cells with virus and in another group with virus and ( - ) -25b and then collected supernatant at 12 hpi . This supernatant was further UV-inactivated to get rid of any active viral particle . Our data show a significant reduction in caspase 3 level in ( - ) -25 treated cell supernatant as compared to the only virus infected supernatant in HT22 cells ( Fig 7A ) . Virus protein load was checked to make sure absence of viral replication in HT22 cells to confirm the death is not because of virus ( Fig 7A ) . TUNEL assay was performed to check cell death and was found to be correlated with western blot data . The UV treated supernatants were further analysed for CBA to confirm the presence of cytokines . This data shows nitrosporeusine treatment in N9 cells inhibits bystander death .
Microglia are resident immune effector cells within the CNS and are hence likely to get activated for encounter against infectious agents at very early stages of infection , as well as at later stages , when infiltration of peripheral leukocytes , such as lymphocytes and monocytes , are recruited into the brain parenchyma [23] , [24] . Recruitment of leukocytes into the CNS is usually preceded by chemokine production from microglia and other CNS-resident cells , which is the first line of defence against infectious pathogens[25] , [9] . CHPV replication is mainly limited to the neurons , and active replication has not been observed in any other resident cells of CNS [26] . However , in vitro studies provide evidence for the onset of viral gene expression in glial cells , implying that although CHPV virions were taken up by glial cells , in which virus gene expression occurs , the production of virus progenies is impaired at the later stage of the viral replicative cycle . In response to enhanced survivability and delayed disease progression post-CHPV infection , we have systemically examined the cellular response of microglia to CHPV infection , and the activation of cellular signalling pathways leading to an aggravated inflammatory response in these cell types . Pro-inflammatory cytokines and chemokines released by the microglial activation process bind to specific receptors of neurons that initiate the apoptotic mechanism in the cells . In order to validate the effect of nitrosporeusine through inhibition of inflammation , we analysed the cytokine and chemokine levels from the N9 cells as well as brain samples ( Fig 2 ) . Following CHPV infection expression levels of TNF-a , CCL2 and IL-6 were found to increase that was previously implicated in playing decisive roles in encephalitis[27] , [28] has decreased significantly after ( - ) -25b treatment . Regarding the cellular response of microglia to CHPV infection , we have observed expression of two chemokines , CXCL10 and CCL5 , is notably induced in CHPV-infected microglia in N9 cells as well as in brain samples . Furthermore , the data obtained here exhibit that CHPV infection initiates activation of multiple signalling pathways mediated by NF-kappaB , p38 , and JNK , in microglia and that viral gene expression is required for the activation of these signal-transducing molecules . Our data demonstrate NF-kappaB-dependent signal transduction is a critical process leading to the strong induction of CXCL10 and CCL5 expression in CHPV-infected microglia , and this signalling is indirectly augmented via the activation of the p38-mediated pathway . Our data also indicate that JNK , doesn’t contribute to the induction of CXCL10 expression and CCL5 expression as the inhibitor of JNK doesn’t reduce the expression level of chemokines ( Fig 6B ) . The previous report demonstrated that the gene expression levels of chemokines , including CXCL10 and CCL5 , in the CNS infected by Rabies virus are induced in the mononuclear cell [29] . Considering the strong chemotactic effects of CXCL10 and CCL5 on leukocytes , such as T cells and monocytes [30] , [31] , it is likely that the production of these chemokines is associated with the BBB breaching and infiltration of mononuclear cells into the CHPV-infected CNS . Though inflammation occurs , little information is available concerning the signalling of microglial activation and cascade of event occurs after CHPV infection in CNS . Our findings in the current study demonstrate the possibility that CNS-resident cells can produce CXCL10 and CCL5 via the recognition of CHPV infection . The data obtained here provide evidence for the CHPV-induced activation of intracellular signalling pathways in microglia . Recent extensive studies have indicated that microglia intrinsically produce CXCL10 and CCL5 upon infection with a variety of neurotropic viruses , including cytomegalovirus [32] , human immunodeficiency virus[33] , herpes simplex virus [34] Theiler’s murine encephalomyelitis virus [35] , and Japanese encephalitis virus [36] , [37] . However , the precise role of cell signalling molecules , especially that of MAPK subfamilies , in the CHPV-induced expression of these chemokines in microglia has not been studied before . As for NF-kappaB signalling being responsible for CXCL10 expression , a recent report suggests that the induction of CXCL10 production adenovirus infection is mediated by the Akt activation pathway[38] . The results obtained in the present study indicate that Akt phosphorylation is required for activation of NF-kappaB activation for CXCL10 expression in CHPV-infected microglia . Our findings are unique in that the activation of these MAPK pathways leading to CXCL10 and CCL5 expression is triggered at the step after virus entry because UV-inactivated CHPV virions failed to induce MAPK phosphorylation . It has been reported that p38 , as well as PI3K/Akt is required for CCL5 production in astrocytes following infection with HIV infection [39] . Our data suggest inhibition of NF-kappaB leads to inhibition of CCL5 expression and Akt phosphorylation is required for NF-kappaB activation suggesting the role of this axis in CCL5 and CXCL10 expression . Leukocyte infiltration is preceded by chemokines expression . Leukocyte influx into the brain is a defining feature of viral encephalitis . It has long been assumed that leukocyte infiltration into the CNS is critical for virus clearance and recovery[40] . Alternatively , infiltrating leukocytes may paradoxically contribute to a more severe outcome that results from the destruction of neuronal cells . Chemokines are pivotal regulators of leukocyte trafficking [41] , [6] . In the context of CHPV infection , it is not clear the mechanism for induction of inflammation and chemokines are essential for the attraction of leukocytes into the CNS and what role they play during viral infection of the brain , or indeed what impact this has on encephalitis-associated morbidity and mortality . Our data shows CNS leukocyte recruitment , and aggravated inflammatory response modulate host immune responses , which in excess may otherwise contribute to virus-induced damage and mortality . Nitrosporeusine is newly synthesised drug acts as an anti-inflammatory agent [20] . In our case , we have found that nitrosporeusine treatment in animal significantly increases animal survivability and decreases disease progression in BALB/c mouse ( Fig 1A & 1B ) . The decrease in viral load , as well as caspase level , intrigued us to investigate the mechanism of action of this drug . Inflammatory mediators are known to induce neurodegeneration if produce uncontrollably [19] . NF-kappaB is known to play a very crucial role in inflammation and also reported to get activated during viral infection [42] , . [43] . In conclusion , our work defines the mechanism of CHPV induced microglia-mediated death in CNS . We have shown the activation of NF-kappaB in microglial cells post CHPV infection . This activation is required for secretion of deleterious chemokines and cytokine that plays a pivotal role in recruiting peripheral immune cells at the CNS , thus further aggravating the condition . We have shown the therapeutic role of nitrosporeusine analogues , in particular , enantiomer ( - ) -25b in CHPV infection . Our data suggest compound ( - ) -25b inhibits microglial NF-kappaB activation and hence inhibits chronic inflammation in the brain .
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Chandipura virus is a negatively stranded single RNA virus , which has claimed many lives in several outbreaks in India and Indian subcontinent . Our previous study shows CNS inflammation plays a vital role in inducing neurodegeneration post infection . In continuation to that our present study systematically examined the mechanism of microglial activation and function of NF-kappaB in CNS inflammation . We have shown that though CHPV infection is not productive in microglia , it leads to NF-kappaB activation that regulates secretion of cytokines and chemokines . The uncontrolled expression of inflammatory molecule disturbs the brain homeostasis leading to alteration of tight junction proteins that are an essential component in Blood-Brain Barrier . The expression of CCL5 and CXCL10 initiates peripheral leukocytes recruitment at the site of infection leading to aggravated inflammatory response , which has the deleterious effect on neurons . Our result shows nitrosporeusine targets microglial activation , and inhibits inflammation in in vitro as well as in vivo . The inhibition of inflammation has a protective effect on brain leading to enhance survival in mouse model .
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2018
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Nitrosporeusine analogue ameliorates Chandipura virus induced inflammatory response in CNS via NFκb inactivation in microglia
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The genomes of five Cochliobolus heterostrophus strains , two Cochliobolus sativus strains , three additional Cochliobolus species ( Cochliobolus victoriae , Cochliobolus carbonum , Cochliobolus miyabeanus ) , and closely related Setosphaeria turcica were sequenced at the Joint Genome Institute ( JGI ) . The datasets were used to identify SNPs between strains and species , unique genomic regions , core secondary metabolism genes , and small secreted protein ( SSP ) candidate effector encoding genes with a view towards pinpointing structural elements and gene content associated with specificity of these closely related fungi to different cereal hosts . Whole-genome alignment shows that three to five percent of each genome differs between strains of the same species , while a quarter of each genome differs between species . On average , SNP counts among field isolates of the same C . heterostrophus species are more than 25× higher than those between inbred lines and 50× lower than SNPs between Cochliobolus species . The suites of nonribosomal peptide synthetase ( NRPS ) , polyketide synthase ( PKS ) , and SSP–encoding genes are astoundingly diverse among species but remarkably conserved among isolates of the same species , whether inbred or field strains , except for defining examples that map to unique genomic regions . Functional analysis of several strain-unique PKSs and NRPSs reveal a strong correlation with a role in virulence .
The filamentous ascomycete genus Cochliobolus ( anamorph Bipolaris/Curvularia ) is comprised of more than forty closely related , often highly aggressive , pathogenic species with particular specificity to their host plants . All members of the genus known to cause serious crop diseases fall in a tight phylogenetic group suggesting that a progenitor within the genus gave rise , over a relatively short period of time ( <20 MYA , Ohm et al . , [1] ) to the series of distinct species [2] , each distinguished by unique pathogenic capability to individual types of cereal ( Table 1 ) . Aggressive members include the necrotrophic corn pathogens Cochliobolus heterostrophus and Cochliobolus carbonum , the oat pathogen , Cochliobolus victoriae , the rice pathogen , Cochliobolus miyabeanus , the sorghum pathogen , Bipolaris sorghicola , the sugarcane pathogen , Bipolaris sacchari , and the hemibiotrophic generalized cereal and grass pathogen , Cochliobolus sativus . All of the known Cochliobolus pathogens are classified as necrotrophs , except for C . sativus , which , although previously classified as such , has more recently been described as a hemibiotroph [3] . Many necrotrophic Cochliobolus spp . and related taxa ( e . g . , Pyrenophora tritici-repentis , Stagonospora nodorum ) are notorious for their ability to evolve novel , highly virulent , races producing Host Selective Toxins ( HSTs ) and their concomitant capacity to cause diseases on cereal crops that were bred , inadvertently , for susceptibility to the HST-producing pathogen [4] , [5] . For example , in 1970 , race T , a previously unseen race of C . heterostrophus ( Bipolaris maydis ) caused a major epidemic [Southern Corn Leaf Blight ( SCLB ) ] , destroying more than 15% of the maize crop [6] . Before 1970 , C . heterostrophus was known as an endemic pathogen ( race O ) of minor economic importance , first described in 1925 [7] . Subsequent research over the ensuing four decades since 1970 has demonstrated that the epidemic was triggered by the unfortunate confluence of complex DNA recombination events in both the fungal pathogen and the plant host . Race T is genetically distinct from race O in that it possesses an extra 1 . 2 Mb of DNA [8] , [9] encoding genes for biosynthesis of the polyketide secondary metabolite , T-toxin , an HST essential for high virulence [4] . These genes are missing in race O . On the plant side , the presence in Texas male sterile cytoplasm ( Tcms ) maize , of a hybrid mitochondrial gene called T-urf13 , composed of segments of two mitochondrial and one chloroplast gene , is essential for susceptibility . Tcms corn does not need to be detasseled to prevent self-crossing because it is male sterile , a desirable trait for breeders producing hybrid seed . The resulting popularity of Tcms maize was disastrous from the perspective of pathogen attack , however , as it served as a monoculture of susceptible germplasm [10] , [11] . Similarly , C . victoriae ( Bipolaris victoriae ) , causal agent of Victoria Blight , produces the chlorinated cyclic pentapeptide HST , victorin , rendering it highly virulent to oats carrying the dominant Vb allele [12] . The fungus caused widespread destruction ( 20 states ) in the 1940's on oat varieties containing the recently introduced Pc-2 gene for resistance to crown rust caused by Puccinia coronata [13] . Like the C . heterostrophus T-toxin/Tcms case , the monoculture of Victoria oats carrying Pc-2 was the perfect milieu for attack by C . victoriae producing victorin , which elicits Pc-2-dependent Programmed Cell Death ( PCD ) . Recent work with Arabidopsis identified a resistance-like protein responsible for susceptibility to C . victoriae and victorin [14] , [15] . This work is seminal in demonstrating fungal HSTs can target resistance proteins to promote disease . In contrast to the dominant plant host genes required for susceptibility to C . heterostrophus and C . victoriae , susceptibility to Northern Corn Leaf Spot caused by C . carbonum ( Bipolaris zeicola ) is conferred by a homozygous recessive maize gene ( s ) [16] , [17] . C . carbonum race 1 produces the cyclic-tetrapeptide HST , HC-toxin , which is specifically active against corn with the genotype hmhm , as is the fungus itself [4] , [18] , [19] . Hm1 and Hm2 encode carbonyl reductases which inactivate the toxin [16]; hmhm lines , cannot inactivate the toxin , and are therefore sensitive . The site of action of HC-toxin in susceptible corn is histone deacetylase; it is hypothesized that HC-toxin acts to promote infection of maize of genotype hm1hm1 by inhibiting this enzyme , resulting in accumulation of hyperacetylated core ( nucleosomal ) histones . This then alters expression of genes encoding regulatory proteins involved in plant defense [20] , [21] . C . carbonum races 2 and 3 do not produce the toxin . C . miyabeanus ( Bipolaris oryzae ) is the causal agent of Brown Spot of rice which contributed , along with a cyclone and tidal waves , to the Bengal rice famine of 1942/1943 that resulted in starvation of more than two million people [22] . To date , no HST has been associated with virulence , although C . miyabeanus culture filtrates can suppress plant phenol production [23] . C . sativus ( Bipolaris sorokiniana ) causes diseases of roots ( Common Root Rot ) , leaves ( Spot Blotch ) , and spikes ( known as black point or kernel blight ) of cereals ( mainly barley and wheat ) [24] , [25] , but also attacks many grasses , including switch grass ( Panicum virgatum L . ) [3] , [26] , [27] and Brachypodium distachyon ( S . Zhong , unpublished ) . Three C . sativus pathotypes ( 0 , 1 and 2 ) have been described [28] , based on differential virulence patterns on three barley genotypes ( ND5883 , Bowman , and NDB112 ) . Pathotype 0 isolates show low virulence on all three barley genotypes . Pathotype 1 isolates show high virulence on ND5883 but low virulence on other barley genotypes . Pathotype 2 isolates show high virulence on Bowman but low virulence on ND5883 and NDB112 . Genetic analysis and molecular mapping indicates that a single locus , VHv1 , controls high virulence of the pathotype 2 isolate ND90Pr on Bowman [29] , [30] , however , the exact nature of the gene ( s ) was unknown before this study ( see Results ) . More recently , a new pathotype , highly virulent on NDB112 , the most durable spot blotch resistance source in barley [31] , has been found in North Dakota [32] and Canada [33] . Setosphaeria turcica ( Exserohlium turcicum , Helminthosporium turcicum ) , a hemibiotrophic vascular leaf pathogen , is a member of the closest genus to Cochliobolus ( see Figure 1 in Ohm et al . , [1] ) , and causes Northern Leaf Blight ( NLB ) , a major disease of maize and sorghum in the US and internationally [34] . To date , at least four races of S . turcica have been identified based on their differential virulence performance on maize carrying resistance genes known as Ht [35] , [36] . In recent work , Martin et al . [34] , identified new resistance genes ( named St ) in both maize and sorghum . The connections between the Ht and St resistance genes are unclear at this point . Until recently , it was assumed that necrotrophs use brute force methods ( e . g . , arsenals of cell wall degrading enzymes , HSTs ) to invade and kill host tissues and do not subtly manipulate the host during infection , as do biotrophs with their arsenal of effectors [37] . Several lines of evidence , from studies of the Dothideomycete , necrotrophic wheat pathogens , Pyrenophora tritici-repentis [38] and Stagonospora nodorum , [39] indicate that , like biotrophs , these pathogens secrete protein effectors ( in this case also called HSTs ) that interact with host targets in a gene-for-gene manner . Unlike biotrophs , however , interaction of the fungal effector and host protein results in susceptibility , rather than resistance . The above-mentioned research on C . victoriae , further indicates mechanistic overlap of pathogenic lifestyle and has major implications regarding the challenge plants face in defending themselves against both necrotrophs and biotrophs [37] , [40] . Recent studies involving Arabidopsis have revealed that victorin-induced PCD requires a host NB-LRR-type resistance protein [15] , [41] . Thus a protein with canonical resistance protein structure is required for susceptibility . These observations point toward victorin , subverting effector triggered defenses against biotrophs , such as P . coronata ( see above ) , to promote susceptibility to a necrotroph . Here we provide a comparative analysis of genome similarities and differences among Cochliobolus and Setosphaeria pathogens , with particular emphasis on strain and species-unique sequences , secondary metabolism genes , and genes encoding small secreted proteins . Identification of these key structural genomic and molecular differences is the first step in understanding species-specificity and how closely related necrotrophic and hemibiotrophic pathogens cause disease . As proof of concept , we offer several examples of how comparative approaches pinpoint virulence associated , species-specific regions of interest . The Joint Genome Institute ( JGI ) has sequenced five strains of C . heterostrophus ( three race T and two race O strains ) and four additional species of Cochliobolus , including C . carbonum , C . victoriae , C . miyabeanus , and C . sativus and one strain of S . turcica . Add to this that host genome sequences ( corn , rice , barley and B . distachyon ) for six of these pathogens are available and one has the information base for dissecting both sides of the interaction mechanism going forward .
Five strains of C . heterostrophus , one strain of C . sativus , and one strain each of C . victoriae , C . carbonum , C . miyabeanus , and S . turcica were sequenced by JGI ( Table 2 , Table 3 ) . Two C . heterostrophus strains and one strain each of C . sativus and S . turcica were fully sequenced as described in Materials and Methods , while the remaining genomes were sequenced using Illumina and assembled de novo using Velvet , as described in Materials and Methods . The highly inbred race O lab strain C5 was used as the reference sequence for all comparisons , as it is the most complete , consisting of only 68 scaffolds . Overall sequence assembly and annotation statistics are presented in Table 2 and Table 3 . All Cochliobolus genomes are in the 32–38 Mb range with an estimated gene content of 11 , 700–13 , 200 . The S . turcica genome is ∼43 Mb with ∼11 , 700 genes . Overall gene content and genome organization are highly similar within this group of fungi . In contrast , comparative analysis of C . heterotrophus , C . sativus and S . turcica in the context of 14 more distantly related Dothideomycetes genomes described elsewhere ( Ohm et . al . , [1] ) revealed significant variation . C . heterostrophus: A genetic map with 125 RFLP markers was constructed previously [8] using C . heterostrophus race O field strain Hm540 ( sequenced herein ) and race T C-strain B30 . A3 . R . 45 ( same backcross series as strains C5 and C4 , sequenced herein [42] , [43] ) as parents . RFLP sequences were used to refine the C . heterostrophus race O strain C5 physical assembly and link it to the genetic RFLP map ( Figure 1 , Text S1 ) . The interconnected genetic and physical maps allowed comparisons of physical and genetic distance , which was found to be ∼13 kb/cM on average ( Figure S1 ) . Correlations were also made between previously estimated chromosome sizes based on CHEF gel analysis [9] , and physical size based on sequence assemblies ( Tables S1 , S2 ) . Based on RFLP map data , parental field isolate Hm540 was reported to lack the dispensable chromosome present in the other parental strain ( B30 . A3 . R . 45 ) used to build the map [8] . Comparisons of all five sequenced C . heterostrophus strains , using the Mauve alignment tool [44] , supported this observation and revealed that all sequenced strains carried the previously recognized ‘B’ or dispensable chromosome ( which corresponds to strain C5 scaffold 16 ) ( Figure S2 ) . Race O chromosomes 6 and 12 are of interest because counterparts are reciprocally translocated in race T and the high-virulence conferring Tox1 locus , encoding genes for biosynthesis of T-toxin , maps genetically to both breakpoints [9] . Comparison of the race O and race T assemblies provided some clues as to the physical locations of these breakpoints , but the exact positions remain elusive , due to structural complexity associated with these regions [45] . Additional details regarding linkage of the C . heterostrophus physical assembly to the genetic map are available in Text S1 . C . sativus: Before mapping sequenced scaffolds to the previously constructed C . sativus genetic map [30] 121 polymorphic simple sequence repeat ( SSR ) markers were identified in the assembly sequences of the ND90Pr and ND93-1 parents , as described in Materials and Methods . Then , sequences of the SSRs and other markers were used to assign sequenced scaffolds to the updated map . Thirty of these linkage groups contained SSR markers and were found to be associated with 16 scaffolds , summing to 29 . 32 Mb . Seven linkage groups were unassigned ( Figure S3 ) . The two AFLP marker sequences ( E-AG/M-CG-121 and E-AG/M-CA-207 ) , cosegregating with the VHv1-associated high virulence of C . sativus pathotype 2 on cultivar Bowman [30] , were used as blast queries against the ND90Pr genome assembly . E-AG/M-CG-121 mapped to scaffold 5 , while E-AG/M-CA-207 mapped to scaffold 40 ( Figure S3 ) . Details of the construction , linking , and analysis of the C . sativus map are available in Text S1 . To begin to identify regions in the C . heterostrophus C5 assembly not represented in other strains , we first mapped gaps in the reference assembly ( thick vertical black bars , Figure 2 ) . A single gap was present in the assembly of 9 of 16 chromosomes and we speculate that these gaps correspond to centromeric regions . We then mapped sequence reads of all Cochliobolus genomes in this study to the C . heterostrophus C5 reference , identifying regions in the C5 reference that were not present in the query genome ( Table S5 ) . The sets of C5 genomic regions that were absent in a given query were combined to determine genomic regions unique and/or conserved at different taxonomic levels . Individual reference-unique region counts were recorded for each of the C . heterostrophus strains . There were 609 areas of the C5 assembly unique ( C4 reads did not map there ) to the C5 genome when C4 reads were mapped to it . For C . heterostrophus strains Hm540 , PR1x412 , and Hm338 , there were 3 , 279 , 4 , 383 and 1 , 480 such regions , respectively . When only regions greater than 5 , 000 bp were considered , there were 19 when C4 was used as the query , and 33 , 75 , and 30 , when PR1x412 , Hm540 , and Hm338 were used as the queries , respectively . Many of the gaps associated with Hm540 mapped to reference C5 scaffold 16 corresponding to the dispensable B chromosome , which is absent in Hm540 ( Figure S2 ) . The C5 reference-unique regions were then combined and filtered to identify conserved genomic regions at the strain or species level , where regions were unique in one type of comparison , but not in others ( Table S5 ) . We designated “inbred C strain”-specific regions as gaps found when all field strains were aligned to C5 but not when C4 was aligned to C5 , race O specific regions as gaps found when all race T strains were aligned to C5 but not when race O strain Hm540 was aligned to race O strain C5 , and C . heterostrophus-specific regions as gaps found when all Cochliobolus species were aligned to C5 , but not when any C . heterostrophus strain was aligned . A total of 28 , 556 bp is missing from all C . heterostrophus field strains , yet present in the C4 assembly ( Table S5 ) . This ∼30 kb of DNA represents sequence uniquely conserved in the inbred C strains . There are at least six fungal-specific Zn2Cys6 transcription factors ( ID# 1019013 , 1020538 , 1021058 , 1021066 , 1100349 , 1100899 ) present in this C strain unique cache that may signify “early action” strain diversification . Zn2Cys6 transcription factors were among the most abundant predicted domains in the C . heterostrophus gene catalogue [1] . There were almost no race O specific regions identified ( sequence found only in C5 and Hm540 ) ; only 11 regions , summing to 4 , 309 bp , were identified ( Table S5 ) . Our working hypothesis is that the essential difference between race O and race T is the 1 . 2 Mb of Tox1 race T DNA ( not in race O C5 and therefore not able to be aligned ) . Both race O only regions ( scaffold 12 , 732532–734880 , and scaffold 19 , 441888–443521 ) contain a single protein each ( ID# 59063 , 34937 ) with no conserved domains or predicted function . Most significantly , at the species level , a total of 11 . 76 Mb DNA was missing from all non-C . heterostrophus Cochliobolus genomes analyzed , yet found in all field strains of C . heterostrophus . Only 1 . 6 Mb of this was found in pieces larger than 5 kb . Most of the sequence that separates C . heterostrophus from other species , therefore , is not the result of large wholesale insertions or deletions of DNA , but from a more piecewise gain and loss . Nonribosomal peptide synthetases ( NRPSs ) , found in fungi and bacteria , are multimodular megasynthases that catalyze biosynthesis of small bioactive peptides ( NRPs ) , including virulence determinants , such as HSTs , via a thiotemplate mechanism independent of ribosomes [50] , [51] , [52] , [53] , [54] . NRPSs synthesize peptides using sets of core domains , known as modules , which consists of three domains: 1 ) an adenylation ( AMP ) domain which recognizes and activates the substrate via adenylation with ATP , 2 ) a thiolation ( T ) or peptidyl carrier protein ( PCP ) domain which binds the activated substrate to a 4′- phosphopantetheine ( PP ) cofactor via a thioester bond and transfers the substrate to 3 ) a condensation ( C ) domain which catalyzes peptide bond formation between adjacent substrates on the megasynthase complex . NRPSs can be mono- , bi- , or multi-modular and core domains in any particular multimodular enzyme may be most closely related to one another or to a domain from a different NRPS . The suites of NRPS encoding genes ( NPS ) in the C . heterostrophus C4 and C5 genomes were identified and annotated previously [55] , [56] , [57] . To address degree of conservation and evolutionary relationships of NRPS proteins in our subject species in order to make inferences about function , we used the fungal AMP-binding ( AMP ) domain Hidden Markov Model ( HMM ) developed by Bushley and Turgeon [57] to identify individual AMP domains in the additional strains of C . heterostrophus and other Cochliobolus species , plus S . turcica . Phylogenetic trees were built to develop a comparative NRPS AMP domain inventory and included the known C . heterostrophus C5 AMP domains as a reference ( Table S6 ) . Polyketide synthases ( PKSs ) , like NRPSs , are large multidomain enzymes that produce small molecules ( polyketides ) with functions that include HSTs . The suites of PKS encoding genes ( PKS ) in the C . heterostrophus C4 and C5 genomes were identified and annotated previously [65] . To address degree of conservation and evolutionary relationships of PKSs in our subject species in order to make inferences about function , we used the PFAM ketosynthase domain ( KS ) HMM as a query to search for orthologs in the additional strains of C . heterostrophus and other species , and the related maize pathogen , S . turcica . Several publications demonstrate that species/strain unique sequences tend to reside in variable regions of the genome such as in subtelomeric locations [68] , [69] and dispensable chromosomes [70] . All C . heterostrophus reference strain C5 NPS and PKS genes were mapped to the assembled linkage groups ( Figure 2 ) . For NPSs , 13 of the 14 total could be mapped to one of the 16 linkage groups/chromosomes , and 6 of the 13 were <200 kb from the end of the linkage group . Two ( NPS 5 , 9 ) of the six are unique to C . heterostrophus and two ( NPS1 , 11 ) have limited distribution in Cochliobolus spp . . For PKSs , 22 of the 25 total could be mapped to one of the 16 linkage groups , 9 of the 22 were <200 kb from the end of the corresponding linkage group and one ( PKS25 ) mapped to the B chromosome . Five ( PKS 13 , 16 , 17 , 20 , 25 ) of the ten are unique to C . heterostrophus and one more ( PKS11 ) has limited distribution in Cochliobolus spp . . In sum , approximately half of the NPSs and PKSs map to scaffold ends , in some cases with mapped telomeres ( Figure 1 ) . As chromosome ends are notoriously variable , this placement could indicate a mechanism for patchy phylogenetic distribution of these genes . Note that the two PKSs involved in T-toxin production by race T are absent in race O strain C5 , but map ( genetically ) to the breakpoints of race T chromosomes 12;6 and 6;12 which are reciprocally translocated with respect to chromosomes 6 and 12 in race O C5 [9] . Note also that PKS3 , which has a phylogenetic relationship , but without bootstrap support , to PKS2 ( Figure 10 , Figure 11B ) , maps internally to race O chromosome 6 ( Figure 2 ) , and that PKS7 , which is the closest ( with bootstrap support ) C . heterostrophus PKS to PKS1 ( Figure 11B ) , maps to the end of unplaced scaffold 20 ( Figure 2 ) . To identify candidate effector proteins , we searched the gene catalog of each species for proteins that were cysteine rich ( ≥2% cysteine ) , small ( <200 amino acids ) , predicted to be secreted ( using Phobius [71] ) , and without transmembrane domains . Between 141 and 289 SSPs per genome ( Table 10 ) were identified with C . sativus ND90Pr containing the most and C . heterostrophus Hm338 the fewest . We next conducted an all versus all blast analysis to determine if SSPs were strain or species-unique , using an 80% blast cutoff . Very few C . heterostrophus SSPs were unique to any particular strain as most could be found in at least one other C . heterostrophus field or lab strain . Using this approach , we identified between one and 21 unique SSPs ( Table 10 , master inventory Table S11 ) . We found more strain-unique SSPs in the other Cochliobolus genomes , as our analysis included five C . heterostrophus strains . S . turcica and C . sativus had the most isolate-unique SSPs , containing 191 and 167 candidates , respectively . As these are the two strains thought to act as hemibiotrophs , it is interesting that they contain more SSPs , and more unique SSPs , than the necrotrophic isolates , although this is only a correlation at this point . The C . heterostrophus C5 assembly has 180 predicted secreted proteins matching the criteria . We examined each of these in the JGI browser with respect to EST support , SNPs , and predicted functional domains . Seventy-two of these calls had absolutely no EST support , while 24 calls had incomplete EST support ( ESTs matching some portion , but not all , of the gene call ) , leaving 84 with complete ( spanning the entire gene model ) EST support ( Table 11 ) . Genes in the no-EST support category are of special interest , as they may be specifically expressed in planta and thus not expressed under conditions used for preparing our EST libraries ( fungus grown in vitro on a variety of complete ( CM ) and minimal ( MM ) media , and mixed into CM or MM pools ) . Lack of strong EST support may also suggest an erroneous gene call . As is typical with candidate effectors , functional domain predictions were lacking , with only 37 candidates having some predicted function , generally involved in cell wall or extracellular matrix function ( Table 11 , Table S11 ) . An additional 23 candidates were conserved in other fungi outside of the Dothideomycetes . The remaining 120 calls were featureless and seemingly unique to the Dothideomycetes . C . heterostrophus strain C5 SSP calls were rich in SNP calls to other Cochliobolus genomes: 101 candidate SSPs had SNPs with at least one other Cochliobolus genome . In our all versus all blast analysis , only 6 of the 180 C . heterostrophus C5 SSPs were found in all 10 strains examined and 14 were unique to strain C5 ( Table 11 ) . The presence or absence of most SSPs did not fall into easily categorized bins such as C . heterostrophus-specific , or maize-pathogens only . Instead , SSPs were present and absent in no particular pattern across the genomes . 115 SSPs were present in at least one other species ( C . victoriae , C . miyabeanus , C . carbonum , or S . turcica ) , with seven found in all species , and 27 in all Cochliobolus species . SSPs mapped to all scaffolds larger than S26 ( Figure 2 ) . Unlike those in some phytopathogens , such as Leptosphaeria maculans [72] , SSP encoding genes did not occur in clusters; candidates seldom were located within 10 kb of each other ( Figure 2 ) . These genes were , however , often located in or near regions we identified as C . heterostrophus species unique ( Figure 2 ) .
Our whole-genome alignment data support graduated degrees of similarity at the highly inbred strain , field strain , species and genus levels . C . heterostrophus strains C4 and C5 , offspring of successive backcrosses [42] , were highly similar to one another , with 20 fold fewer SNPs than pairwise comparisons of reference strain C5 to C . heterostrophus field strains . This remarkably low number of SNPs highlights the power of selective inbreeding in establishing uniformity across the genome . The two C . sativus field strains , when aligned to each other , had a comparable number of SNPs to those of C . heterostrophus field strains aligned to reference C . heterostrophus strain C5 . Other Cochliobolus genomes had roughly 50 fold more SNPs than C . heterostrophus field strains when aligned to the C . heterostrophus C5 reference . This level of similarity was seen when comparing any two Cochliobolus species to one another , with the exception of comparing C . victoriae to C . carbonum . These two species are capable of successfully mating , although progeny of crosses are unable to cross to each other or to their parents ( Turgeon lab , unpublished ) . We have hypothesized that C . victoriae may have evolved from a C . carbonum strain [49] . This similarity is seen at the whole genome level , as C . victoriae and C . carbonum share an intermediary number of SNPs compared to C . heterostrophus inter- and intra-species comparisons . Our SNP data show that approximately one quarter of the genome differs between Cochliobolus species and that only about one tenth of this is found in segments larger than 5 kb . We and others [72] , [73] , [74] have recently introduced the term mesosyteny [1] to describe organizational conservation between species . Genetic content is conserved across chromosomes , but not co-linearly . It seems possible that our findings here , showing that many small , scattered differences summing to significant quantitative differences ( i . e . , 25% dissimilar ) , could be the product of the same mechanisms that result in mesosyntenic patterns . Pathogens of the same host ( e . g . , C . carbonum and C . heterostrophus on maize ) or lifestyle were not more similar to each other than those of different hosts; instead overarching genetic patterns followed phylogenetic lines . As it is estimated that the Pleosporaceae arose as a group less than 20 MYA ( see Figure 1 in Ohm et al . , [1] ) and the genus Cochliobolus is young in the group , genome comparisons provide us with an overall picture of a timeline of how genome diversity varies with speciation . Our intra-species SNP tallies are comparable to SNP tallies found when strains of other species are examined . For example , there were 10 , 495 SNPs called between two Fusarium graminearum strains [75] , and a range of 13 , 274–188 , 346 SNPs called for 18 Neurospora crassa classical genetic mutants [76] . Both dataset tallies are in the same range as our C . heterostrophus field strain comparisons . With respect to SNPs in different species of the same genus , it is unusual that we were able to perform a whole-genome SNP analysis at all , without limiting our scope to coding sequence . We owe this to the very close phylogenetic relationship of these species . Although individual functional domains of NPS/PKS proteins can be identified bioinformatically , attempting to predict their corresponding metabolite product is challenging . The genes encoding these proteins evolve rapidly and through complex mechanisms [57] , [65] and whole gene alignment methods provide misleading or unclear results when determining presence or absence of a particular NPS or PKS gene . Here , we extracted individual conserved signature catalytic domains , i . e . , the AMP-binding domains from mono- or multi-modular NRPSs or the ketosynthase ( KS ) domain from multidomain PKSs using customized HMM models , then built alignments and phylogenetic trees with these individual units to determine the presence or absence of whole or partial NRPS and PKS proteins , and their evolutionary relationships . In our opinion this is a necessary first step towards understanding evolutionary history of the corresponding genes and the possible small molecules produced by these highly diverse proteins . We found that within a Dothideomycete genus , in this case Cochliobolus , approximately half of the NPS and a third of the PKS genes are well conserved ( present in all strains ) . When related S . turcica was considered these numbers dropped to a third and a fifth , respectively . The rest were found to be poorly conserved or species-unique when the highly curated C . heterostrophus gene sets were used as reference . The small molecules produced by the corresponding non-conserved proteins are largely uncharacterized , but the differences between strains and species imply that the potential for production of biochemically unique molecules is large and considerably beyond that expected for closely related strains ( housekeeping genes share ∼95% identity ) . These findings refine our understanding of NPS and PKS genes , as very few are conserved . For example , only two NPS genes and one PKS gene were found when 18 Dothideomycete genomes were analyzed [1] . Broadly conserved secondary metabolism genes , where they have been characterized , produce small molecules that serve basic cellular functions ( ferricrocin , melanin [58] , [77] , [78] , [79] ) . Poorly conserved NPS and PKS genes , while still largely uncharacterized , can include those involved in host-specific high virulence . NPS1 and NPS3 AMP domains are discontinuously distributed and expanded across the Cochliobolus and Setosphaeria isolates sequenced and are sources of much of the NRPS diversity ( Figure 5 , Figure 6 and [57] ) . The individual AMP modules do not cluster by protein , but instead , NPS1 , NPS3 and NPS13 AMP domains occur in two distinct , and mixed , groups ( Figure 6 ) . Strikingly , each Cochliobolus species possesses either a complete C . heterostrophus NPS1 or NPS3 ortholog , but never both . Furthermore , all species have one or more additional NRPS proteins consisting of NPS1/NPS3 related domains , and a NPS13 related domain that is absent from all C . heterostrophus genomes except Hm540 ( Figure 5 ) . The C . heterostrophus Hm540 genome includes all four corresponding genes: NPS1 , NPS3 , NPS13 , and the additional NPS1/NPS3/NPS13 gene ( Figure 5 ) . The pattern of duplication and loss appears to have been very rapid to account for this distribution , and is further complicated by the presence of additional bi- , mono- , tri- , and tetra-modular proteins , particularly in C . sativus , whose AMP domains group with NPS1/NPS3/NPS13 proteins , ( Figure 5 , Figure 7 , Figure S5 ) . The origin of strain or species unique secondary metabolism genes is of great interest and horizontal gene transfer is a common , but not the only , explanation for their appearance [65] , [80] , [81] , [82] . The volatility of the NPS1 , NPS3 and NPS13 family raises the possibility that genes we presume are horizontally transmitted could have vertical histories obfuscated by species and strain sequence depth . We speculate that partial or whole genes encoding individual domains or whole proteins recombine and expand quickly , and , when they confer high virulence , as in the case of HSTs , can spread rapidly throughout a population . When the susceptible host allele is not present in the population , the gene is lost or not conserved in the majority , but not the entirety , of the population , as is the case for C . heterostrophus race T and genes for T-toxin production; race T is difficult to find in the field currently [43] . As we sequence more and more isolates , we might find that the T-toxin genes are present in strains of many more Dothideomycetes than we originally expected . This is certainly the case with the HC-toxin genes which is not so surprising , given that the Hm alleles are present in most plants . Identifying secondary metabolites that function as virulence factors ( such as HSTs ) is a primary goal when studying a pathogen's genome . The impact of HSTs was realized early on because they render the producing fungi pathogenic or highly virulent to principal crops . Thus , most were characterized physiologically and genetically decades ago [4] , [18] , [19] , [40] , [83] , [84] , [85] . The pivotal point of our comparative analyses is the strikingly obvious observation that secondary metabolite genes , when unique to a species or strain , are likely to encode a virulence determinant . We provide several examples . The first example is the C . heterostrophus PKS1 and PKS2 genes involved in production of the HST T-toxin . These genes reside in 1 . 2 Mb of DNA , not found in race O and located at the breakpoints of two race T chromosomes ( 12;6 , 6;12 ) , reciprocally translocated with respect to race O counterparts ( chromosomes 6 , 12 ) . The T-toxin genes are not in race O or any other Cochliobolus species . Deletion of either PKS eliminates T-toxin production and drastically reduces virulence of the fungus on Tcms maize , as reported earlier [45] , [67] . Known Tox1 genes , such as PKS1 are on very small scaffolds ( ∼25 kb ) in race T strains C4 , Hm338 , and PR1x412 ( Figure 11 ) , which cannot be further assembled due to the repetitive and AT-rich nature of the locus . Thus the physical structure of the Tox1 locus remains elusive , but its association with a unique genomic region , however complex , is clear-cut . Although T-toxin is unique to C . heterostrophus , a closely related fungus , Didymella zeae maydis ( formerly , Phyllosticta maydis , Mycosphaerella zeae maydis ) , produces a polyketide HST , PM toxin , with the same biological specificity as T-toxin . The central PKS for PM-toxin is the closest PKS to C . heterostrophus PKS1 , but still only ∼60% identical at the amino acid level and organization of the cluster of genes required for toxin production differs; in D . zeae-maydis , the genes are present in a single tight cluster [86] , [87] . The second example , examined here , is the NRPS , HTS1 , for HC-toxin production . The genes for HC-toxin produced by C . carbonum , were identified two decades ago in a tour de force molecular manipulation exercise [88] , [89] . A strong genomic signature attends these genes as they reside in an ∼600 kb region not found in other races of C . carbonum , or in any of the additional Cochliobolus genomes examined then or here . Two copies of a cluster of HC-toxin genes are located in this region and both copies of the core NRPS , HTS1 , had to be deleted to demonstrate elimination of toxin production and reduction of virulence [88] , [89] . In current investigations , we , Manning et al . , [61] and Wight and Walton [62] have discovered that HC-toxin like genes are present in S . turcica ( Figure 8 ) , P . tritici-repentis and A . jesenskae , respectively . These genes are also apparent orthologs of the genes for apicidin ( APS1 ) production by some Fusarium species [90] . Thus , genes for HC-toxin or HC-toxin-like metabolites are more broadly distributed than previously thought . In terms of amino acid identity , the S . turcica and P . tritici-repentis NRPS HTS1 proteins are 79% identical at the amino acid level , but identity drops to 39–43% when these are compared to the C . carbonum HTS1 or APS1 proteins . The S . turcica and P . tritici-repentis HTS1 orthologs lack the C-terminal condensation domain found in C . carbonum HTS1 and F . semitectum APS1 , suggesting S . turcica and P . tritici-repentis make a different product . Whether or not S . turcica and P . tritici-repentis are capable of producing HC-toxin is unknown , however , it has been reported [62] that A . jesenskae does . That HC-toxin producing capability might be found in pathogens other than C . carbonum is not unreasonable , considering the maize defense gene Hm1 , necessary to detoxify the toxin , is found in all grasses [91] . The third example concerns C . sativus . Two of the NPS genes unique to C . sativus ND90Pr ( IDs 115356 and 140513 , Figure 7 ) are present at the VHv1 locus associated with high virulence on cultivar Bowman . The entire VHv1 locus is absent in the low virulence isolate , ND93-1 , and the two NPSs are not found in any other genomes examined here , or in Genbank . Our data show that these genes are up-regulated 12 hrs after inoculation , and that deletion of one of them , ( 115356 ) , significantly reduces virulence on barley cultivar Bowman ( Figure 7 ) . Recent work on deletion of the gene encoding 4′-phosphopantetheinyl transferase provided indirect evidence that a secondary metabolite is involved in the biosynthesis of the virulence factor in ND90Pr [92]; our current work directly confirms this . The phylogenetic location of these VHv1 NPS genes is revealing in that they are either in branches with no close sister members ( ID# 140513 , Figure S5A ) or in the NPS1/NPS3/NPS13 expansion clade ( ID# 115356 , Figure 5 , Figure 6 ) . In addition to these two genes , we have evidence based on real time expression data on RNA from inoculated barley , that the C . sativus genes corresponding to protein ID#s 49884 and 130053 are also up-regulated at 12 hrs post inoculation . These genes are found in the same C . sativus specific clade ( New_8 , Figure 3 , Figure S5A ) as protein ID# 140513 , and although we have not yet made mutants , our prediction is that these will also contribute to virulence . Our analyses of the hemibiotroph , S . turcica , is in its infancy , however , as noted in the Results we have identified 13 unique PKSs , three of which ( ID# 161586 , 30113 , 34554 ) grouped together in a S . turcica- specific clade , called ‘New_10’ ( Figure 10 , Figure S8 , Table S10 ) . As preliminary support for the importance of unique PKSs , we used real time PCR , to examine in planta expression of one of these S . turcica unique genes ( 161586 ) and found that expression was indeed increased ( >500 fold ) by three days post inoculation . Although we haven't yet deleted this gene , it is tempting to predict that the in planta expression pattern is indicative of a role in virulence . One of the species-unique NRPSs in C . victoriae ( on node 1179 , gene #7087 ) is a NRPSs with two AMP domains clustering with 99–100% bootstrap support to AMP domains from the bimodular A . fumigatus NRPS , GliP , which produces the ETP toxin , Gliotoxin . Related to these NRPSs is the L . maculans NRPS , SirP , which produces sirodesmin . Candidate orthologs of these NRPSs have been reported in Chaetomium globosum , Magnaporthe oryzae , and Fusarium graminearum [93] . Gliotoxin is associated with virulence of A . fumigatus to immune-compromised patients [94] . Functional characterization of the newly discovered C . victoriae counterpart is necessary to determine the type of ETP produced and whether or not it might play a role in virulence , as Gliotoxin does . Note the entire Gliotoxin gene cluster [64] is present in C . victoriae ( Figure 9 ) . Gene knockout and screening for alteration in virulence to oats , due to victorin production , indicates no change from that of wild type ( Wu , Turgeon , unpublished ) . This C . victoriae NRPS is not found in other Cochliobolus genomes , yet it clusters with A . fumigatus GliP , exemplifying the patchy distribution signature of most members of the NPS family of genes . Effectors are pathogen produced small secreted proteins ( SSPs ) /small molecules that interact with the host plant to promote disease . Effectors historically were called avirulence proteins ( as their discovery hinged on association with a corresponding plant resistance gene ) , but we now recognize that effectors are virulence factors that aid the pathogen by specifically targeting aspects of host cell defense and recognition . Evading detection is a necessary strategy for ( hemi ) biotrophs , where triggering the host hypersensitive response curtails disease . Necrotrophs , on the other hand , benefit from the death of host cells , and have evolved molecules such as HSTs , like victorin which subverts function of an R gene ( Lov1/Pc-2 ) to trigger susceptibility and plant cell death intentionally [14] , [15] , [95] . Protein HSTs such as P . tritici-repentis and Stagonospora nodorum ToxA are clear examples of secreted , necrotrophic , proteinaceous , host-selective virulence factors acting to effect virulence in host cells , like any other effector , but which , in the presence of a R-protein look-alike , is necessary for susceptibility [96] . The lingering question is whether or not necrotrophs utilize SSP effectors in the traditional ( and difficult to identify ) sense of micromanipulation of the host environment , or , instead , use effectors to trigger host cell death through abuse of ( hemi ) biotrophic defenses . In this regard , given our clear discovery that at least one NRPS metabolite ( ID# 115356 ) , when deleted has a much reduced phenotype reminiscent of a necrotrophic HST phenotype , we question whether C . sativus should truly be considered a hemibiotroph or a necrotroph . On the other hand , our SSP analysis shows that C . sativus and S . turcica have an expanded SSP repertoire compared to the other species examined , which is consistent with a hemibiotroph strategy , i . e . , arsenals of effectors are used to evade host detection . The repertoire of candidate effectors in necrotrophs , nevertheless , is quite large . If only a small subset of these is involved in virulence , it would mean that Cochliobolus , and perhaps other necrotrophs , use effectors more expansively than is recognized . This is a difficult question to address , and our in silico analysis requires experimental confirmation of in planta expression and secretion before we can be sure Cochliobolus species utilize protein effectors . Perhaps a strategy prioritizing species or strain unique regions would aid characterization attempts . Bearing on this point , the SSP catalogue differed markedly from secondary metabolites in their conservation across , and within , species . Only six of the 180 ( 3% ) C . heterostrophus C5 SSPs were identified in all genomes examined ( including S . turcica ) , unlike the 7/25 ( 28% ) PKSs and 6/14 ( 43% ) NRPSs . Considering C . heterostrophus genomes only , 27 of these 180 SSPs were present in each ( 15% ) , again , far fewer than the 21/25 ( 84% ) PKS and 13/14 ( 93% ) NRPS C . heterostrophus C5 genes conserved throughout all C . heterostrophus genomes . This indicates that , more so than secondary metabolite genes , SSP encoding genes are extraordinarily volatile in the evolutionary history of the genus . The stories of the SCLB and Victoria blight epidemics are dramatic examples of interactions between crops , whose ‘evolution’ is driven by human intervention ( breeders ) and their pathogens , which evolve naturally to exploit new genetic susceptibilities . Both the Tcms and Pc-2 genes were introduced into maize and oats , respectively , by breeders fewer than 30 years before the epidemic outbreaks . Specifically , Tcms was discovered in the 1940's , incorporated into elite corn inbred lines increasingly throughout the 1960's , and was present in almost all of the hybrid corn in the US by 1970 . The vast monoculture of Tcms maize was the perfect host for the previously unknown race . Species of Cochliobolus spp . clearly have proven their ability to cause extraordinary crop losses . As we begin to understand the intimidating capacity for diverse production and evolution of new HSTs , we must also look for ways to apply this knowledge to our disease response strategies .
C . heterostrophus strains sequenced by JGI included inbred strains C5 ( ATCC 48332 , race O , MAT1-1 , Tox1− ) and C4 ( ATCC 48331 , race T , MAT1-2 , Tox1+ ) and field strains Hm540 ( geographical origin North Carolina , race O , MAT1-1 , Tox1− ) , Hm338 ( New York , race T , MAT1-2 , Tox1+ , ATCC 48317 ) , and PR1x412 , ( a progeny of a cross between PR1C from Poza Rica , Mexico and strain 412 , unknown geographical origin , race T , MAT1-1 , Tox1+ ) . In addition , the genomes of C . victoriae strain FI3 ( unknown geographical origin , MAT1-2 , victorin+ ) , C . carbonum strain 26-R-13 [MAT1-1 , HC-toxin+ , a progeny of a cross between C . carbonum strains 2-R-6 ( alb2; MAT1-1 ) and Five Points ( unknown geographical origin , MAT1-2 ) performed by Dr . Steve Briggs] . C . miyabeanus strain WK1C ( Wuankuei , Yulan county China , MAT1-2 ) , C . sativus isolate ND90Pr ( North Dakota , ATCC 201652 , MAT1-2 , pathotype 2 on barley cv . Bowman ) and S . turcica strain St28A ( New York , race 2 , 3 , N , MAT1-1 ) were sequenced by JGI . C . sativus isolate ND93-1 ( North Dakota , ATCC 201653 , MAT1-1 , pathotype 0 on barley cv . Bowman ) was sequenced at the University of Hawaii . The highly inbred C . heterostrophus reference race O lab strain C5 was sequenced using the Sanger whole genome shotgun approach , with paired end reads and improved by manual finishing and fosmid clone sequencing ( http://www . jgi . doe . gov/sequencing/protocols/prots_production . html ) . Four different sized libraries were sequenced: 3 . 1 kb , 6 . 8 kb , and two fosmid libraries ( 32 . 3 kb and 35 . 3 kb ) , to a total coverage of 9 . 95× . ESTs were generated by growing strains in complete and minimal medium under many conditions [on complete and minimal medium , on sexual reproduction plates , stress medium ( -N , -Fe , etc ) ] and pooled as complete or minimal samples for sequencing and support of gene annotation . The genome of isogenic C . heterostrophus race T strain C4 was sequenced using Illumina technology ( 300 bp insert size , 2×76 bp reads to a nominal depth of 200× ) , assembled using Velvet [97] and AllPathsLG [98] and annotated using ESTs from C . heterostrophus strain C5 . The genome of C . sativus pathotype 2 isolate ND90Pr was sequenced using a hybrid approach , which included 40 kb fosmid Sanger reads , shredded consensus from Velvet assembled Illumina data ( 300 bp insert size , 2×76 bp reads ) , Roche ( 454 ) standard and Roche ( 454 ) 4 kb insert paired ends , all assembled using Newbler [99] and annotated using C . sativus ND90Pr ESTs as described below . The genome of the second C . sativus isolate , ND93-1 , was sequenced at the University of Hawaii by paired end 454 runs and assembled using Newbler . S . turcica strain 28A was sequenced using Roche ( 454 ) , Sanger fosmids , and shredded consensus from Velvet assembled Illumina data; EST libraries were prepared from S . turcica strains using conditions described above for C . heterostrophus . The JGI annotation pipeline was used to annotate C . heterostrophus strains C5 and C4 , C . sativus ND90Pr , and S . turcica . For this , the assembled genomic scaffolds were masked using RepeatMasker [100]with the RepBase fungal library of 234 fungal repeats [101] and genome-specific libraries derived using [102] . Multiple sets of gene models were predicted for each assembly , and automated filtering based on homology and EST support was applied to produce a final non-redundant GeneCatalog representing the best gene model found at each genomic locus . The gene-prediction methods were: EST-based predictions with EST map ( http://softberry . com ) using raw ESTs and assembled EST contigs for each genome; homology-based predictions with Fgenesh+ [103] and Genewise [104] , with homology seeded by BLASTx alignments of the GenBank non-redundant sequence database ( NR: http://www . ncbi . nlm . nih . gov/BLAST/ ) to the genomic scaffolds; and ab initio predictions using Fgenesh [103] ) and GeneMark [105] . Genewise models were extended to include 5′ start and/or 3′ stop codons when possible . Additional EST-extended sets were generated using BLAT-aligned [106] EST data to add 5′ UTRs , 3′UTRs , and CDS regions that were supported by ESTs but had been omitted by the initial prediction methods . All genome annotations can be interactively accessed through MycoCosm [107] , http://jgi . doe . gov/fungi . Because the subject genomes are all closely related to the fully sequenced reference C . heterostrophus strain C5 , additional C . heterostrophus field strains Hm540 , Hm338 and PR1x412 , C . victoriae , C . carbonum , and C . miyabeanus , were ( re ) -sequenced using using Illumina technology . DNA was randomly sheared into ∼200 bp fragments using Covaris E210 according to the manufacturer's recommendation and the resulting fragments were used to create an Illumina library . 2×76 bp reads were assembled using Velvet ( version 1 . 1 . 04 ) , [97] . There are no ESTs available for these organisms . Assembled contigs were mapped to the reference C . heterostrophus C5 for analysis of genome variation and rearrangements . Assembled reads are called ‘nodes’ ( scaffolds ) . Overall sequence assembly and annotation statistics are presented in Table 3 . The genomes without JGI annotation pipeline gene predictions ( ChHm540 , ChHm338 , ChPR1x412 , C . carbonum , C . miyabeanus , C . victoriae ) were annotated using Augustus [108] . Assembled genomes were mapped individually to the C5 reference strain using the nucmer program of MUMmer v 3 . 22 [46] , a program that finds unique , exact matches to build whole genome alignments . SNPs were called and analyzed using the dnadiff wrapper on the filtered MUMmer delta files . Unassembled reads were also aligned to the C . heterostrophus C5 reference genome for low coverage analysis using maq-0 . 7 . 1 . Regions of the reference genome under a depth of three aligned reads were considered “low coverage” for our analyses . Adjacent low coverage regions were merged if they were separated by less than 100 bp in order to minimize noise from mis-mapping of occasional low quality reads . After low coverage regions were identified for pairwise comparisons to C5 , regions were identified that were shared as low coverage in multiple genomes: i . e . , C . carbonum , C . victoriae , C . miyabeanus , and C . sativus for C . heterostrophus specific regions; C . heterostrophus Hm540 , Hm338 , and PR1x412 for C strain specific regions; and C4 , Hm338 , and PR1x412 for race O specific regions . Cross genome comparisons were visualized using Mauve [44] , a multiple genome alignment tool that visualizes localized collinear blocks ( LCB ) between genomes . Cloned RFLP markers [8] were sequenced and sequences used in blast queries against the C . heterostrophus C5 assembly . Top hits were filtered using Bioperl and manually confirmed to span the entire RFLP with very high stringency to rule out markers that might exist as more than one copy . Physical and genetic distances of adjacent RFLPs mapping to the same scaffold were plotted and used to calculate an average ratio of physical to genetic distance ( Figure S1 , Table S2 ) . Relative RFLP location was used to orient scaffolds along the linkage group when possible . A genetic linkage map was generated previously using the mapping population derived from a cross between parental isolates ND93-1 and ND90Pr [30] and amplified fragment length polymorphism ( AFLP ) and RFLP markers . To add simple sequence repeat ( SSR ) markers to the map , the draft sequence assembly of the C . sativus isolate ND93-1 was screened for SSR loci with di- and tri-nucleotide units tandemly repeated six or more times using a Perl script ( provided by Zheng Jin Tu at the University of Minnesota , St . Paul ) . The SSR-containing sequences from ND93-1 were aligned to the draft genome sequence of isolate ( ND90Pr ) of C . sativus . Only those sequences that were polymorphic between the two C . sativus parents ( ND93-1 and ND90Pr ) were used for primer design and tested for segregation in the mapping population used previously [30] . PCR conditions and detection of SSR markers were as previously described [109] . Map construction was performed by using MAPMAKER version 2 . 0 [110] . A minimum LOD value of 4 . 0 and a maximum theta of 0 . 3 were used to group all SSR markers with previously mapped AFLP , RFLP and PCR markers [30] . The Kosambi mapping function was used to calculate the map distance . To associate linkage groups to the sequenced scaffolds of C . sativus , the sequences of mapped SSR markers were used as queries to blast against the draft genome assembly of C . sativus isolate ND90Pr and the coordinates of each SSR marker were recorded for the associated scaffold . NRPS and PKS proteins were identified using our custom fungal AMP domain model [57] for the former and an HMM model build from C . heterostrophus KS domains plus sequences from the C-terminal and N-terminal ketosynthase ( KS ) Pfam domains for the latter ( PF00109 and PF02801 ) . Proteins were identified in two ways . In the first case , genome nucleotide sequences were searched using Genewise [111] and sequences extracted and concatenated by a Perl script utilizing Bioperl's searchIO system [112] . In the second case , Augustus protein models ( see above ) and JGI protein models ( C . heterostrophus , C . sativus , S . turcica ) were searched with the PKS KS and NRPS AMP HMM using HMMER 3 . 0 [113] , and the sequences extracted and concatenated using a Perl script with Bioperl's searchIO . AMP and KS domains were aligned , separately , with MAFFT ( http://mafft . cbrc . jp/alignment/software/ ) and manually inspected to remove columns of poor alignment . ProtTest [114] was run on both alignments and identified the RTREVF model as the best fit for the AMP domains and the WAGF model as the best fit for the KS domain alignments , respectively . RAxML [115] using the RTREVF and WAGF models with a gamma distribution was used to infer maximum likelihood trees and bootstrap support was determined using the fast-bootstrap method with 1000 bootstrap replicates [116] . The CIPRES portal ( http://www . phylo . org/sub_sections/portal/ ) was used for inference of phylogenetic trees . C . sativus ND90Pr NRPS AMP domains were used as blast queries to identify AMP domain orthologs in ND93-1 with the methods above . An ND93-1 AMP was considered orthologous if it was at least 95% identical to the ND90Pr query . Candidate small secreted proteins ( SSP ) were identified by screening the gene catalogue of each genome . Proteins smaller than 200 amino acids and containing more than 2% cysteines were searched for transmembrane domains and secretion tags using Phobius [71] . Those without transmembrane domains were retained . EST support and domain prediction for C . heterostrophus C5 SSPs was performed using the JGI portal . Cross-genome comparisons were made based on all vs . all reciprocal best hit analysis with an 80% similarity cutoff . Three EST libraries were constructed . A mycelia-only library was constructed by harvesting mycelia grown on different media including Potato Dextrose Agar ( PDA ) , minimal medium ( MM ) [117] , V8PDA ( 150 ml V8 juice , 850 ml H2O , 10 g PDA , 10 g Agar and 3 g CaCO3 ) , and water agar ( 15 g agar , 1000 ml water ) . Mycelia were harvested after 12 , 24 , 48 , 76 and 96 hours of growth , and time points from different media were mixed together for RNA extraction . Equal amounts of extracted RNA from each of the 5 time points was bulked to construct the mycelia only library To construct the in planta cDNA libraries , two week old barley cv . Bowman and 4 week old Brachypodium distachyon line Bd21were inoculated with conidia of ND90Pr at a concentration of 5×103/ml [118] . Inoculated plants were incubated in a humid chamber for 24 hours and moved to the greenhouse . Leaves were harvested at 6 , 12 , 24 , 48 , 72 , and 96 hours after inoculation and total RNA extracted from each sample . The final in planta cDNA libraries were constructed by mixing the equal amounts of total RNA from different time points . Total RNA was isolated from all samples using the PureLink RNA Mini Kit ( Invitrogen , Carlsbad , CA ) and purified by treatment with DNase I ( Invitrogen , Carlsbad , CA ) . These three libraries were sequenced by JGI . For C . sativus , total RNA extracted as described above at six time points ( 6 , 12 , 24 , 48 , 72 , and 96 hours ) after inoculation was used for RT-PCR . The reverse transcription reaction was performed on 2 µg of total RNA using the SuperScript III First-Strand Synthesis System ( Invitrogen , Carlsbad , CA ) . cDNA was diluted 20 times and used as the template for quantitative RT-PCR , which was performed with the AB7500 real time PCR system ( Applied Biosystems , Foster , CA ) ( Table S9 ) . For each cDNA sample , three replications were performed . Each reaction mixture ( 20 µl ) contained 5 µl of the cDNA template , 10 µl of SYBR Green PCR Master Mix ( Applied Biosystems , Foster , CA ) and 0 . 3 µl of each primer ( 10 mM ) . All samples were normalized using RT-Actin-F and RT-Actin-R primers as a control , and values were expressed as the change in the increase/decrease of the relative levels of the control sample ( M96 , which is the mixture of mycelia harvested from different media including PDA , MM , V8PDA , and water agar ) . For S . turcica , leaf samples with lesions were collected at five time points ( 3 , 5 , 6 , 7 and 8 days ) after inoculation with 9×104 spores per plant ( three weeks old ) and total RNA was extracted and qPCR done as described [119] . The actin gene was used as internal control using ATC1 primers [119] . The S . turcica gene primers corresponding to protein ID 161586 are listed in Table S9 . Expression level was expressed as fold change versus mycelial samples harvested on Lactose Casein Agar ( LCA ) plates . Fungal transformation and molecular characterization of gene knockout mutants were conducted according to the methods of [120] . The split marker system [121] was used for gene deletion . The 5′ and 3′ flanking sequences of the NPS gene encoding protein ID 115356 were amplified from ND90Pr DNA using primer pairs GTCGACTGCCATCTGGAAAC/CACTGGCCGTCGTTTTACAACGTCCACTCGACAGGTCCGTAGGT and TCATGGTCATAGCTGTTTCCTGTGGTATCCACAAAGCCACAGCA/GACGAACCAGA GATGCATGA ) respectively . To verify deletion of the gene corresponding to protein ID 115356 , primers CAN1-F3: AGTTGTTGGGGAGTTGTTGG and CAN1-F4: TGAGCCGTTGTCATGTATCG matching the deleted portion of the gene were used . The expected PCR product was obtained from WT DNA , but not when DNA of the deletion mutant was used as template . To further confirm that the hygromycin resistance gene replaced the target gene at the native locus , PCR was conducted using a primer located outside the 3′ flank used for gene deletion ( CsNPS1-F0: GTCCTACGGCAATTGTGGAC ) and a second primer ( HY: GGATGCCTCCGCTCGAAGTA ) located in the hygromycin resistance gene . No amplification occurred when WT DNA was used , while the expected amplicon was observed when DNA of the mutants was used as template . For C . sativus , virulence of the mutant ( ID# 115356 , Figure 7C ) and wild type strains was tested on barley cv . Bowman by spray inoculation as described in Fetch and Steffenson ( 1999 ) , except 2×103 conidia/ml were used . Inoculated plants were incubated in a humid chamber for 18–24 hours , and then transferred to a greenhouse room ( 20+/−2°C ) . For S . turcica , three week old W64A maize plants were sprayed with 9×104 spores per plant , and plants grown under conditions described previously for C . heterostrophus [119] . MAT1-1 and MAT1-2 mating type regions were identified by blasting the corresponding known C . heterostrophus MAT sequences ( MAT1-1: accession CAA48465 , MAT1-2: accession CAA48464 ) against each genome . Regions immediately 10 kb upstream and downstream were aligned pairwise to C . heterostrophus C5 ( MAT1-1 ) or C4 ( MAT1-2 ) MAT regions using ProgressiveMauve [44] to generate SNP data , and as a group ( with and without S . turcica for MAT1-1 ) for visualizing the alignment . Genome assemblies and annotations are available via JGI Genome Portal MycoCosm ( http://jgi . doe . gov/fungi , [107] and DDBJ/EMBL/GenBank under the following accessions Cochliobolus heterostrophus ATCC 48331 ( race T , strain C4 ) : AIHU00000000 , Cochliobolus heterostrophus ATCC 48332 ( race O , strain C5 ) : AIDY00000000 , Cochliobolus sativus ND90Pr: AEIN00000000 , Setosphaeria turcica Et28A: AIHT00000000 , C . sativus ND93-1:PRJNA87041 , Cochliobolus carbonum 26-R-13: AMCN00000000 , Cochliobolus miyabeanus ATCC 44560: AMCO00000000 , Cochliobolus victoriae FI3: AMCY00000000 .
|
The filamentous ascomycete genus Cochliobolus includes highly aggressive necrotrophic and hemibiotrophic pathogens with particular specificity to their host plants , often associated with production of host selective toxins ( HST ) that allow necrotrophs to trigger host cell death . Hemibiotrophs must keep their hosts alive during initial infection stages and rely on subverting host defenses by secreting small protein effectors . Many Cochliobolus species have emerged rapidly as devastating pathogens due to HSTs . The genomes of Cochliobolus and related pathogens that differ in host preference , host specificity , and virulence strategies have been sequenced . Our comparative results , at the whole-genome level , and with a spotlight on core genes for secondary metabolism and small secreted proteins , touch on how pathogens develop and hone these tools , according to host or lifestyle . We suggest that , while necrotrophs and hemibiotrophs employ fundamentally contrasting mechanisms of promoting disease , the tools they utilize ( HSTs and protein effectors ) overlap . The suites of secondary metabolite and SSP genes that each possesses reflect astounding diversity among species , hinting that gene products , particularly those associated with unique genomic regions , are candidates for pathogenic lifestyle differences . Manipulations of strain-unique secondary metabolite genes associated with host-specific virulence provide tangible examples .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genomics",
"plant",
"biology",
"crops",
"genetics",
"biology",
"computational",
"biology",
"genetics",
"and",
"genomics",
"agriculture"
] |
2013
|
Comparative Genome Structure, Secondary Metabolite, and Effector Coding Capacity across Cochliobolus Pathogens
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Genome sequences of Plasmodium falciparum allow for global analysis of drug responses to antimalarial agents . It was of interest to learn how DNA microarrays may be used to study drug action in malaria parasites . In one large , tightly controlled study involving 123 microarray hybridizations between cDNA from isogenic drug-sensitive and drug-resistant parasites , a lethal antifolate ( WR99210 ) failed to over-produce RNA for the genetically proven principal target , dihydrofolate reductase-thymidylate synthase ( DHFR-TS ) . This transcriptional rigidity carried over to metabolically related RNA encoding folate and pyrimidine biosynthesis , as well as to the rest of the parasite genome . No genes were reproducibly up-regulated by more than 2-fold until 24 h after initial drug exposure , even though clonal viability decreased by 50% within 6 h . We predicted and showed that while the parasites do not mount protective transcriptional responses to antifolates in real time , P . falciparum cells transfected with human DHFR gene , and adapted to long-term WR99210 exposure , adjusted the hard-wired transcriptome itself to thrive in the presence of the drug . A system-wide incapacity for changing RNA levels in response to specific metabolic perturbations may contribute to selective vulnerabilities of Plasmodium falciparum to lethal antimetabolites . In addition , such regulation affects how DNA microarrays are used to understand the mode of action of antimetabolites .
Malaria parasites infect over 300 million people around the world and the most virulent species , Plasmodium falciparum , kills 1–2 million individuals per year [1]–[3] . The availability of genome-wide DNA microarrays for P . falciparum , has facilitated insights into many complex biological problems . During erythrocytic development , over 3 , 000 genes are expressed in a cascade of simple , mostly unique , sigmoidal patterns [4]–[7] . While some genes are expressed at a steady rate , hundreds of genes show at least 30-fold change in expression during the erythrocytic cycle [4] . In addition DNA microarrays have been used to understand stage-specific differentiation [8]–[10] , to determine invasion preferences [11] , [12] , and possibly pathogenesis [13]–[16] . At first , it was expected that DNA microarrays would also permit a quick , unbiased look at the mode of action of antimalarial drugs , particularly simple antimetabolites [17] . An underlying premise was that parasites would sense metabolic perturbations from a drug and make compensatory changes in its transcriptome to adjust for the perturbations . Indeed , studies in other organisms have demonstrated the value of such approaches . In Saccharomyces cerevisiae , Mycobacterium tuberculosis , Candida albicans , mammalian cells and even plants , specific antimetabolites up-regulated dozens of target-related RNA by greater than 10-fold [18]–[23] . In many of these systems confidence in the power of DNA microarrays to reveal mechanisms of drug action come from perturbation of well-understood metabolic pathways . Our early preliminary studies had suggested that RNA levels for metabolic targets in malaria parasites are not sensitive to lethal antifolates nor to resulting specific metabolic perturbations [24]–[27] . Here , in a complete , carefully controlled microarray study , we definitively demonstrate that global gene expression in malaria parasites is regulated in a fundamentally different way from model organisms such as E . coli and yeast . Parasite transcription for intermediary metabolism is hard-wired and not responsive to specific , lethal , metabolic perturbations . We further demonstrate that candidate pathways involved in drug-induced death may still be identified through unconventional strategies , including probing for subtle RNA changes with a large number of replicates and tracking alterations in the hard-wired transcription program itself .
To guard against broad pleotropic transcriptional effects that may be difficult to interpret in drug-treated parasites , our study exploits the potency and specificity of the antifolate WR99210 against P . faciparum ( Structure , Figure S1 ) . The parasite clone Dd2 fails to proliferate when exposed to 10 nM WR99210 for 48 h [25] , [28]–[30] . A concentration of 10 nM was selected because it is enough to kill all sensitive Dd2 cells ( EC50 = 0 . 1 nM ) . Biochemical assays and genetic complementation studies ( using human DHFR ) have established P . falciparum DHFR-TS as the major target of WR99210 [25] , [28]–[30] . To identify transcriptional changes that were directly related to death events caused by the lethal effects of WR99210 on DHFR , the present battery of microarray hybridizations included a control WR99210-resistant cell-line , B1G9 , which harbors a single integrated copy of human DHFR in a Dd2 background [25] , [29] . B1G9 is resistant to as much as 500 nM WR99210 . Finally , to help frame drug-induced changes in RNA levels in the context of cell physiology , Dd2 and B1G9 parasite lines were exposed to 10 nM WR99210 for varying time periods and the effects assessed with respect to clonal cell viability , continued synthesis of nucleic acids , RNA levels for individual genes coding for the effected pathways , and the global transcriptome . A comparison of biochemical changes , morphological alterations and loss of cell viability in WR99210-treated Dd2 provided the first indication that malaria parasites resisted broad metabolic or developmental arrests in response to specific lethal perturbations . Using clonal viability as a measure of drug-induced death [31] , 50% of P . falciparum trophozoites became less viable after as little as 6 h of exposure to 10 nM WR99210 ( p<0 . 01 ) and practically all parasite cells were non-viable after 12 h of drug exposure ( Figure 1A ) . However , even after 24 h of WR99210 treatment , trophozoites continued to follow a preordained metabolic program for converting short pulses of radioactive hypoxanthine into DNA , albeit with a lower amplitude ( Figure 1B ) . Microscopic examination of WR99210-treated trophozoite forms of the parasite failed to show morphological changes until about 24 h after treatment when the schizonts appeared unhealthy ( Figure 1C ) . At subsequent hours , control cells released merozoites and generated healthy rings but the WR99210-treated parasites remained as ill schizonts . In parallel assays , WR99210-resistant B1G9 cells behaved like untreated Dd2 ( data not shown ) . In a single large controlled experiment , Plasmodium transcriptome changes were followed in WR99210-treated parasites using DNA microarrays with 7 , 685 oligonucleotide probes per slide , representing all open reading frames in the genome [4] , [17] . For added value and confidence , the custom array also carried multiple probes per gene for key enzymes in the target pathway of folate and pyrimidine metabolism ( Table S1 and S3 ) . RNA samples from synchronized Dd2 and B1G9 trophozoites , that had been treated with 10 nM WR99210 for varying durations , were hybridized against a common pool of trophozoite RNA from a cognate clone ( Figure 2A ) . For each time point , samples from biological duplicates were hybridized to four microarray slides , including dye exchanges . The biological duplicates were from independently propagated cultures to minimize misleading , stochastic variations in gene expression for surface proteins ( [32] , Figure S2 and Figure S3 for description of data normalization and variation between technical and biological variability ) . In total , the present normalized data is derived from 123 microarray hybridizations . As discussed below , this redundancy and accuracy was necessary to interpret some small but informative perturbations in RNA in P . falciparum . For some genes , it was possible to detect as little as 10–20% changes in RNA levels , with statistically significant reproducibility . Details of the experimental design , raw output , and statistical analysis are presented in MIAME-compliant format to the NIH-based GEO database ( Accession # GSE9724 for WR99210 data from Seattle and # GSE9853 and # GSE9868 for the pyrimethamine data from Bangkok ) . During normal 48-hour developmental changes in erythrocytes , RNA levels for individual enzymes for pyrimidine and folate biosynthesis change nearly 10-fold [4] . During continual WR99210 treatment for 24 h , malaria parasites showed very little deviation in their transcriptome , even as they died from antifolate treatment . First , expression of DHFR-TS , the immediate target of antifolates was examined in detail . Fluorescently labeled cDNA , generated from WR99210-treated and non-treated cells , was hybridized to 12 unique oligonucleotides derived from different parts of the 1 , 863 bp DHFR-TS coding strand ( Figure 2A and 2B ) . Regardless of the WR99210-susceptibility status of the parasite clone , and regardless of whether the cells were treated with solvent or the antifolate , hybridization of fluorescent cDNA to most probes for DHFR-TS did not increase ( and actually decreased slightly ) during the 24 h normal progression of trophozoites to schizonts , ( Figure 2D ) . Based on combined data from all 12 probes , RNA coding for DHFR-TS did not increase by more than 20% at any time point after WR99210-treatment ( Figure 2C ) . This microarray-based analysis of DHFR-TS is consistent with earlier limited measurements using RNA Protection Assays ( RPA ) [25] and qRT-PCR [33] . For additional certainty , given recent description of differential expression of small regulatory RNA within the coding region of mammalian DHFR [34] , [35] , we undertook an independent detailed analysis of DHFR-TS expression using qRT-PCR spanning twelve different parts of the DHFR-TS coding region . As seen with the different unique oligonucleotide microarray probes ( Figure 2D ) , qRT-PCR ( Figure 2E–F ) unambiguously confirmed that DHFR-TS RNA , or parts of DHFR-TS RNA , were not overproduced at protective levels in parasites treated with the lethal antimetabolite WR99210 . Since the 10 nM WR99210 treatment was at 100-times the EC50 for Dd2 ( 0 . 1 nM ) , complete protection through increases in target RNA levels would require at least 100-fold increases in DHFR-TS RNA . Even a 10% protection from a RNA-based mechanism would require at least a 10-fold increase in DHFR-TS RNA . Second , in addition to DHFR-TS , the expression of fourteen additional enzymes in the folate and pyrimidine biosynthesis pathways also did not deviate significantly from their normal transcriptional patterns . This analysis was based on at least two different microarray probes for each of the enzymes listed in Figure 3 . The only two RNA species that showed small , consistent , significant changes from their normal expression program were serine hydroxymethyltransferase ( SHMT ) and a ribonucleotide reductase small subunit gene ( RNR2 ) . However , even these two responsive RNA species showed , at most , only a 2-fold change at very late time points ( maximum 105% increase at 24 h , p<0 . 002 ) . Even though the methylenetetrahydrofolate-using thymidylate synthase is thought to be the ultimate target of DHFR inhibition , the unresponsiveness of pyrimidine biosynthesis to DHFR-TS inhibitor have not previously been reported . Just as our preliminary microarray data had reported [24] , [26] , [27] , a qRT-PCR study from an independent group has also shown that RNA for folate-biosynthesis enzymes are not up-regulated in response to pyrimethamine-treatment [33] . This other study did not look at changes in RNR2 and did not pickup the subtle drug-dependent alterations in transcripts for SHMT that are seen with the current large microarray data set . Given the inability of P . falciparum transcriptional program to overproduce RNA for DHFR-TS and related enzymes after antifolate treatment , we wanted to understand the extent of the overall transcriptional obstinacy in drug-sensitive parasites . In the first 3 h after WR99210 treatment , when death events were underway ( p<0 . 05 , Figure 1A ) , no genes showed statistically significant deviations from their normal developmental program that was greater than 2-fold in expression . Even 24 h after WR99210-treatment , when all cells are completely committed to die , greater than 99% of the genes in the Plasmodium transcriptome did not deviate significantly in gene expression . By exploiting our experimental design , which included side-by-side treatment of isogenic sensitive and resistant malaria parasite cell lines with a potent , specific , and lethal antifolate , it was possible to detect small reproducible changes in RNA in dying cells and only in dying cells . Out of 7 , 685 oligonucleotides examined , there were 34 genes whose expression levels increased at least at one time point during the 24 h WR99210 study ( Figure 4 , and Table S4 for gene names , gene ID numbers , and fold-changes ) . There were 21 genes whose expression levels decreased at least at one time point ( Figure 5 and Table S5 ) . These changes were considered antifolate death-related genes ( AFDG ) because they were not seen in solvent-treated , drug-sensitive Dd2 parasites , and were not seen in solvent-treated or WR99210-treated resistant B1G9 cells . The numerous death-related RNA changes were subtle and probably not protective , but they were statistically significant and possibly reflected larger physiologically important metabolic perturbations in dying parasite cells . The death-related transcripts provide the first insights into downstream events that may connect inhibition of DHFR-TS by antifolates to ultimate loss of cell viability ( Table S4 and S5 for PlasmoDB gene numbers ) . Given the known mechanism of action of antifolates , the presence of ribonuceotide reductase and DNA repair endonuclease on this list was satisfying . The intriguing presence of putative cell-signaling proteins ( phosphotyrosine phosphatase activator; a GTP binding protein; a Rab18 GTPase; and a calcium-dependent protein kinase ) and some potential degradative enzymes ( cysteine protease SERA3; protease; 20S proteosome beta 4 ) on the list of death-related genes should stimulate investigations of their possible role in antifolate toxicity . The list of death-related genes also included some known enzymes whose role in antifolate-mediated death is not obvious but should also be of interest ( inorganic pyrophosphatase; gamma-glutamylcysteine synthetase; elongation factor Tu; and long-chain fatty acid ligase-oxalyl CoA decarboxylase ) . Finally , this genome-wide analysis revealed that , of the 55 antifolate-triggered small transcript changes , there were 25 hypothetical gene products with no previous known functions in other cell types . To evaluate the validity and reliability of the small changes in RNA levels detected in the DNA microarray experiments , two different approaches were taken . Conventional quantitative RT-PCR was used to compare RNA expression in independently cultivated P . falciparum Dd2 cells , before and after 10 nM WR99210 treatment for 24 h . This time point was used because the most significant changes on the microarray occurred after 24 h and the magnitude of the changes approached the resolution limits of RT-PCR . Out of 3 randomly chosen up-regulated genes , all 3 showed the expected small up-regulation upon WR99210 treatment ( Table 1 ) . Out of 3 randomly chosen down-regulated genes , 2 showed down-regulation as expected but one did not . Overall , we concluded that , though small , most RNA “whispers” picked up by the DNA microarrays were verifiable by qRT-PCR . In a completely different approach , two partner labs from two different parts of the world compared microarray data to determine if antifolate-treated parasites shared some common signatures in their transcriptome . Realistically , all the WR99210-reponsive genes from the Seattle study were not expected to show up in the Thailand study because the two groups were working with independently printed arrays , different parasites strains ( Dd2 vs TM4/8 . 2 ) , different antifolates ( WR99210 vs pyrimethamine ) , and different treatment antifolate doses ( 99 . 9% vs 50% IC values ) . Yet , some of the most relevant changes should be shared , given the common mechanism of action of WR99210 and pyrimethamine . Indeed , a genome-wide comparison of transcript differences in antifolate-treated parasites versus solvent-treated parasites not only revealed 4 genes that were up-regulated and 5 genes that were down-regulated in concordance but the changes occurred at approximately the same time after antifolate treatment ( Figure 6 ) . In addition to some new proteins with no prior known functions , this set included a DNA repair endonuclease ( MAL13P1 . 346 ) and a ubiquitin-conjugating enzyme ( PF10_0330 ) . It is not clear why the drug-induced down-regulation of RNA for the cytoadherance linked protein CLAG2 ( PFB0935w ) [36] , [37] was seen in this set , but it appeared consistently in all other measures of antifolate toxicity in the present microarray study . It was hypothesized that even if a genetically determined hard-wired transcriptome is insensitive to real-time arrival of antimetabolites in the cell , perhaps the hard-wired program itself may evolve to tolerate an antimetabolite , particularly if given a chance to adapt over successive generations . Indeed , in the present large experiment with multiple controls , it was possible to identify genes whose expression in the resistant B1G9 cells was rewired in concordance with protection against the drug WR99210 ( Figure 7 and Figure S4 ) . In this most compelling gene set , expression in the drug-resistant B1G9 cells behaved normally ( as in non-treated , drug-sensitive Dd2 cells ) only when B1G9 cells were under WR99210 pressure ( Figure 7 and Table S6 ) . In other words , expression of these genes was rewired so that it matched normal cells only when the antifolate WR99210 was present . The expression pattern of these genes was dissimilar in dying , drug-sensitive Dd2 cells exposed to WR99210 , or resistant B1G9 cells grown without WR99210 ( Figure 7 ) . This data set included up-regulation of subtilisin-like protease 2 ( PF11_0381 ) which might promote egress and reinvasion [38]–[42] , an ADP-ribosylation factor-like protein ( PFI1005w ) [43] , [44] , and several “hypothetical proteins” . While detailed follow up studies will clearly be needed , the alteration in subtilisin-like protease expression ties nicely with morphological arrest of WR99210-treated parasites as ill schizonts and failure to see reinvasion and rings at later time points ( Figure 1C ) . A second set of permanent genetic alterations in the transcription program involved genes whose expression in the resistant B1G9 cells was different from the WR99210 sensitive Dd2 , regardless of whether the antifolate was present or not ( Figure S4 and Table S7 ) . While it is possible that these genes also contribute to antifolate resistance mechanisms , this second data list should be accepted with caution since at least some of the RNA changes may have arisen through adventitious changes in the cell line due to collateral genetic damage during WR99210 adaptation .
Many commonly accepted paradigms for designing selective antimetabolites originate from the study of antifolates . Prior to the availability of genome sequences , mechanisms of drug action and the role of active site in selectivity were identified through intuitive comparisons to normal metabolites [45] and validation through resistance and transfection [46]–[48] . Our understanding of selective and potent antimetabolites continues to improve: Low , fixed levels of target enzyme in the parasite and the selective ability of host cells to overexpress target enzymes can play an important role in drug selectivity [25] . While useful , all such approaches have always relied on existing knowledge of biochemistry , metabolism , and pharmacology . Now , with the availability of genome sequences for Plasmodium parasites [49]–[51] , and accompanying tools such as DNA microarrays [52] , [53] , there is much enthusiasm about using open ended tools to decipher drug action [17] , particularly downstream biochemical and cellular events that lead to cell death . First , the present study shows that WR99210 treatment of P . falciparum does not trigger overexpression of RNA for DHFR-TS , the known target of the antifolate ( Figure 2 ) . Casual observation of the microarray data clusters may suggest that DHFR-TS levels increase slightly . However , the color representations can be deceiving , in part because they are set at very high sensitivity ( Figure 2D ) . The actual DHFR-TS increases are neither large nor statistically significant . While the follow up quantitative RT-PCR ( Figure 2E and 2F ) did show slightly higher levels of DHFR-TS RNA in treated cells , this was not due to RNA induction . The small “increases” in DHFR-TS RNA level in WR99210-treated Dd2 cells arise from a slight delays in normal degradation of DHFR-TS RNA , mostly at very late time points in the dying cell . When one asks the big question , do malaria parasites overexpress the target DHFR-TS RNA to protective levels when treated with the antifolate WR99210 ? The answer is clearly negative . Can DNA microarrays be used to unambiguously assign mechanisms of antifolate action through real-time changes in RNA levels after drug exposure ? The answer , again , is unambiguously negative . Beyond the immediate target DHFR-TS , the parasites also do not overproduce RNA for any of the enzymes of pyrimidine or folate metabolism , two pathways known to be effected by antifolates ( Figure 3 ) . Most importantly , long after P . falciparum cells treated with WR99210 were committed to death , there were no large consistent reliable increases in RNA for any of the genes in the P . falciparum genome . The last observation shows that the transcriptional obstinacy of P . falciparum is not just restricted to folate biochemistry but permeates through much of the parasite's metabolic network involved in control of cell proliferation , and eventually cell death . Since this large project started and has been reported in preliminary form at scientific meetings , several other smaller DNA microarray studies have also encountered transcriptomes resistance to antimetabolites . This includes work on the antimalarial choline analogue T4 , the mitochondrial inhibitor atovaquone , and most recently the polyamine biosynthesis inhibitor DFMO [54]–[56] . Earlier claims that malaria parasites show specific large transcriptional responses to chloroquine [57] , were reversed [58] . The later conclusion appears to be correct since an independent study has also recently claimed a lack of real-time changes in RNA levels in chloroquine-treated P . falciparum [59] . Doxycycline a protein synthesis inhibitor for subcellular organelles caused a whole-sale shutdown of apicoplast RNA , not specific up-regulation of RNA for the target protein [60] . The emerging general consensus is that malaria parasites do not mount large increases in RNA in response to antimetabolites . Our conclusions from the original discovery using antifolates remains compelling because the study uses a potent , specific inhibitor ( WR99210 ) that targets a genetically validated target ( DHFR ) : Drugs with broad or poorly defined mechanism of action were avoided because they can add to existing uncertainty about malarial transcriptional responses . Even though large protective changes in RNA were not seen in drug-treated malaria parasites , the tightly controlled nature of the present study led to unbiased glimpses into small , subtle downstream RNA changes in drug treated malaria parasites . One type of change involved small , reproducible real-time changes in RNA in sensitive cells and only in the sensitive cells ( Figure 3 , 4 and 5 ) . These include RNA coding for proteins involved in relevant target pathways: ribonucleotide reductase of nucleotide metabolism and serine hydroxymethyltransferase of folate metabolism . In addition , there were dozens of new genes whose expression was perturbed and whose role in folate pharmacology would be new and unexpected , including enzymes involved in DNA replication , cell signaling , and protein turnover . Secondly , while the hard-wired transcription for metabolic genes in malaria parasites was largely unresponsive to drug-treatment in real-time , the hard-wiring program itself could evolve in a population that is under continual drug pressure . Such alterations in the transcriptomes offer glimpses into new biochemistry related to drug action ( Figure 7 ) . The last finding presents a clear caution to malaria scientists who rely heavily on DNA transfections to study drug action . Transfection of the malarial parasite line Dd2 with single copy of the human DHFR originally proved the primary mechanism of action of WR99210 [29] . However , emergence of transformants always involve long delay phases , very similar to those seen during in vitro selection for drug resistance in the laboratory [61] , [62] . It is very likely that while human DHFR helps confer resistance to WR99210 , additional genetic changes in the hard-wiring of gene expression are necessary to fully realize the WR99210 tolerance and optimum growth in the transformed cell . Identification of these secondary genetic changes are expected to be fertile grounds for fully understanding downstream biochemical changes that lead from DHFR-TS inhibition to cell toxicity . The present findings add some general rules to help us understand how some good antimalarial drugs work . First , as gene amplification can contribute to drug resistance [63] , unconditional suppression of target protein and RNA for metabolic enzymes may contribute to unusual vulnerabilities in the parasite . Previously , P . falciparum DHFR-TS protein was shown to bind its cognate RNA sequence differently than the host protein [25] . Now we show that the production of RNA itself may be severely limited and this repression of transcription is largely insensitive to metabolic changes . Second , in principle , while natural metabolites and their toxic homologues may not induce large scale changes in target gene expression in malaria parasites , it is conceivable that some other antimalarial molecules could exhibit broad toxicity , in part , by misdirecting the hard-wired transcriptional program of P . falciparum . Third , if there is limited flexibility in regulating gene-expression , perturbations by drugs must be balanced by compensating mutations affecting the transcriptome [64] . This would influence frequencies of drug resistance in unpredictable ways [62] , [65] , as well as leave molecular footprints of prior drug exposure throughout the genome [66] , [67] . The evolutionary implications of the hard-wired malaria parasite transcriptome to control metabolism are significant . Parasites appear to have at least two fundamentally different strategies for gene regulation: Alterations in expression of genes for surface proteins occur randomly to help parasites “outmaneuver” unpredictable immune responses from each new host the parasite encounters [68]–[70] . In contrast , for intermediary metabolism , the obligatory parasites seem to have evolved a deterministic transcriptional program to match the defined and predictable biochemical makeup of host cells . Compared to variations in immune responses , the biochemical environment between different host individuals probably does not vary significantly . The biochemical adaptations which accompany these evolutionary choices in gene regulation must be significant . A priori , the systematic , sequential , rhythmic expression of metabolic genes in malaria parasites must be determined by a sequential expression of regulatory molecules which are largely insensitive to intracellular levels of important metabolites . Of course , while such a model could apply to repeated erythrocytic cycles , it need not preclude a signal-based strategy for influencing differentiation of parasites .
The present experiments used spotted DNA microarrays [71] that were fabricated as previously described [9] , [32] . Commercially available malaria oligonucleotides ( Operon , version 1 . 1 , https://www . operon . com/arrays/oligosets_malaria . php ) were combined with in-house oligonucleotides representing P . falciparum genes in folate and nucleic acid metabolism . The in-house oligos were designed at the University of Washington using ArrayOligoSelector [52] and synthesized by Operon Biotechnologies , Inc . The arrays , each representing the majority of malarial open reading frames plus the custom oligonucleotides , were printed on polylysine-coated slides using an ultra fast , linear servo driven DeRisi microarrayer . Slides were post-processed and hybridized following the protocols as previously described ( http://cmgm . stanford . edu/pbrown/protocols/index . html ) . Each platform used in the following experiments is described and can be accessed at the NIH-based GEO database ( Accession # GPL6187 for WR99210 data from Seattle and # GPL6187 and # GPL6269 for the pyrimethamine data from Bangkok ) . At the University of Washington , Seattle , WA , P . falciparum clones Dd2 and B1G9 were used for treatment with WR99210 . Clone Dd2 was derived from clone W2 from Southeast Asia and is resistant to a variety of antimalarials , but not WR99210 [25] , [65] , [72] . The isogenic clone B1G9 , which harbored a single integrated copy of human DHFR in a Dd2 background , conferred resistance to WR99210 and was kindly provided by Drs . David Fidock and Thomas Wellems [25] , [29] . At the National Center for Genetic Engineering and Biotechnology ( BIOTEC ) , Bangkok , Thailand , a pyrimethamine-sensitive laboratory clone TM4/8 . 2 was treated with pyrimethamine prior to microarray analysis . The TM4/8 . 2 clone was obtained from Dr Sodsri Thaithong , Chulalongkorn University , Thailand . At both sites , parasites were cultured in vitro by standard methods [73] . Clonal viability of WR99210-treated parasites was based on a previously established assay [31] . Forty 10 ml cultures , with synchronized early-trophozoite forms of infected erythrocytes , were treated for different durations with 10 nM WR99210 in a final concentration of 0 . 1% DMSO . Treatment times ranged from 3 h to 24 h , with two flasks per time-point . At appropriate times , cells from each flask were washed and clonally diluted in drug-free medium . Representative dilutions from each flask were plated at a density of about one infected erythrocyte per well in 24 wells of a 96-well plate ( 4 samples per time point ) . Growth in each well was monitored microscopically in drug-free medium for two to four weeks . On average , 20 “colonies” ( i . e . parasite containing wells ) were identified from solvent treated , time-zero parasite populations . Trophozoite forms of infected erythrocytes in ten ml cultures were incubated with 10 nM WR99210 for 1–24 h and then pulsed with radioactive hypoxanthine ( 2 µCi per flask , 22 mCi/umol ) for one hour prior to collection of precipitable radioactive DNA on glass fiber filters . In the absence of drug treatment , maximum incorporation was seen at 4–10 h into trophozoite development ( approximately 5 , 000 cpm; 500 pmol/flask ) . Overall , in Seattle , for the master DNA microarray experiment , two large populations of Dd2 cells and two large populations of B1G9 cells were prepared for treatment with WR99210 . Dd2 clones , starting with about 100 infected erythrocytes per flask , were setup in 60 flasks and propagated until 30 ml cultures were established at 5% parasitemia , 2% hematocrit in each flask . These independently propagated parasites were pooled and redistributed into 60 flasks before drug treatment ( see below ) . A second set of biological replicates of Dd2 were propagated , pooled , and split independently to give two truly independent populations of Dd2 . The overall goal was to neutralize stochastic changes in gene expression for surface proteins [32] . This whole exercise was repeated separately with clone B1G9 . To describe the details of the parasite preparation , in each set above ( each biological replicate of Dd2 or B1G9 ) , parasites were initially seeded in 10 ml cultures with about 100 infected erythrocytes per flask . When parasitemia reached about 5% , the cells were transferred to 30 ml cultures . When parasitemia reached 5% in each 30 ml culture the first time , the infected cells were synchronized with 5% sorbitol [74] . When parasitemia reached 5% again , and when most of the infected erythrocytes were in the ring stage , the synchronization was repeated once more . These parasites ( in one set ) were pooled and redistributed into 30 ml flasks , until they reached early trophozoite stage . Half of these samples were saved as “Time zero , trophozoites” and served as reference RNA for the time-course experiments . Of the remaining parasites , half the 30 ml flasks were treated with 10 nM WR99210 ( 1∶1 , 000 dilution of 10 µM WR99210 stock in 100% DMSO ) and the other half were treated with solvent ( final concentration of 0 . 1% DMSO ) . At various time-points ( 3 h to 24 h ) , infected erythrocytes were centrifuged down , and parasites were released by saponin treatment [75] . After two washes in PBS , the parasites were resuspended in lysis buffer ( RNAqueouse Kit , Ambion ) and RNA was isolated according to the manufacturer's instructions . As discussed above , 2 preparations of independently derived Dd2 were treated with solvent or with 10 nM WR99210 . Similarly , 2 preparations of independently propagated B1G9 were treated with DMSO solvent or with 10 nM WR99210 . In Thailand , for each treatment , three 30 ml culture plates ( 90×15 mm ) of TM4/8 . 2 parasites were set up at 4% hematocrit and 7–10% parasitemia . After two rounds of synchronization , an early trophozoites culture ( approximately at 18–20 h post invasion ) was treated with 0 . 5 µM pyrimethamine . The final concentration of DMSO solvent in each treatment was 0 . 1% . A culture containing 0 . 1% DMSO lacking drug was used as a control . After 2 h , 4 h , 8 h and 24 h of drug exposure , parasites were collected and extracted from erythrocytes by saponin lysis . Total RNA was purified from parasite cells using Trizol reagent ( Invitrogen ) according to the manufacturer's instructions . Experimental treatments were carried out from at least two independent cultures to wash out stochastic biological variation . For RT-PCR based confirmation experiments , appropriate clones of independently seeded parasites were grown in 10 ml cultures and RNA was collected by RNAqueouse Kit ( Ambion ) according to the manufacturer's instructions . Contaminating DNA was removed from the total RNA samples using RQ1 RNase-Free DNase ( Promega ) . For each hybridization , 10 µg of total RNA was annealed with 5 µg pd ( N ) 6 random primers ( Amersham Biosciences Corp . ) and reverse transcribed to produce aminoallyl-dUTP ( Sigma ) -labelled cDNA using StrataScript reverse transcriptase ( Stratagene ) . Oligo- ( dT ) 21 primer was used instead of the random hexamer for pyrimethamine treated samples from Thailand . The labelled cDNAs were coupled with either monoreactive-Cy3 or -Cy5 ( Amersham Biosciences ) as previously described [32] . Purified Cy3- and Cy5-labelled cDNAs were resuspended and mixed in 24 µl of hybridization solution containing 3× SSC , 0 . 2% SDS , 0 . 025 M HEPES and 0 . 75 µg/µl of poly A ( Sigma ) and hybridized on a P . falciparum 70-mer microarray for 16 h at 63°C . After washing , slides were scanned in an Axon GenePix 4000B microarray scanner and the intensity of spots was quantified using GenePix Pro 3 . 0 Software ( Axon Instruments , Inc . ) as previously described [32] . Briefly , during the gridding process , images were inspected and visually problematic spots were manually flagged and removed . Spots with foreground intensity less than 2 . 1-fold of background intensity were considered too weak to be reliable and also removed . A scaled print-tip intensity-dependent lowess within-slide normalization was performed on each slide , followed by an across-slides normalization , using Aroma package version 0 . 89 [76] run in R project environment ( http://cran . r-project . org ) . Differential expression and statistical analysis of Seattle data were done using a linear model method package Limma [77] . The criteria of the p-value of <0 . 05 and expression ratio≥2-fold change were employed for selection of differentially expressed genes . For Thailand data , statistically significant differences in gene expression were monitored using the Significance Analysis of Microarrays ( SAM ) program [78] and genes showing false discovery rate ( FDR ) = 0% and expression ratio of ≥1 . 5-fold in both directions were considered differentially expressed . Cluster analysis was performed using CLUSTER and visualized using TREEVIEW [79] . Six of AFDG genes whose ratio of SD/SS was greater than 2 at 24 h of WR99210 exposure were selected for qRT-PCR validation . These were PFD1120c , PF10_0330 , PFC0710w , PFB0915w , PFB0635w and MAL6P1 . 231 . Three hundred nanograms of total RNA was primed with Oligo ( dT ) /random nanomers mix ( at final concentration of 100 ng each/reaction ) and converted to cDNA in 20 µl reactions using AffinityScript™ QPCR cDNA synthesis kit ( Stratagene ) as recommended by the manufacturer . The reverse transcription reaction was then diluted with 40 µl of nuclease-free PCR-grade water before using in the PCR amplification step . Real-time quantitative PCR was performed on a thermal cycler ( DNA Engine , BioRad ) equipped with a detector ( Chromo4 , BioRad ) . Primers were designed using PRIMER3 ( http://www-genome . wi . mit . edu/genome_software/other/primer3 . html ) and optimized for annealing/extension temperature , concentration , and single product amplification . The designed primers are listed in Table S2 . Amplifications were performed in 25 µl final volume , using 12 . 5 µl of 2× Brilliant® SYBR® Green QPCR Master Mix ( Stratagene ) , 2 µl of the cDNA template , and 250 nM of each primer . Cycling conditions were: 10 min at 95°C and 40 cycles of 95°C for 15 sec followed by 58°C for 60 sec . The specificity of the amplifications was monitored by melting curve analysis and gel electrophoresis . The threshold cycle of fluorescence ( Ct ) was determined by Opticon Monitor 3 software ( BioRad ) . The quantity of cDNA for each gene was normalized to the Seryl-tRNA synthetase ( PF07_0073 ) concentration in each sample ( ΔCt , Ctgene−CtPF07_0073 ) . Relative gene expression was calculated by 2−ΔΔCt method [80] , where ΔΔCt is the ratio of expression of each treatment relative to that of the trophozoite stage reference . Each PCR experiment was performed in duplicates with at least three RNA templates prepared from independent parasite cultures . Analysis of the DHFR-TS oligos was performed with slight modifications to the procedure described above . Two hundred seventy nanograms of total RNA was used per cDNA synthesis reaction and then diluted with 25 µL of nuclease-free PCR-grade water . The annealing/extension temperature was reduced to 56°C . Analysis was performed using the Pfaffl method [81] with each primer pair's efficiency taken into account . Each experiment was performed in triplicate and the 24 h time-point was analyzed in three independent cultures . A representative run is shown and plotted error bars are the SEM of that run .
|
Traditional knowledge of gene regulation , learned largely from non-pathogenic model organisms such as E . coli , yeast , and mice , suggests that RNA for metabolic pathways are regulated in large part by DNA-binding transcriptional factors that are responsive to cellular metabolic needs . We demonstrate that the malaria-causing Plasmodium falciparum parasites , under lethal drug pressure from an antifolate with a known mechanism of action , are incapable of large reproducible changes in RNA levels for the target pathways , or for any other gene throughout the genome . Small RNA changes , possibly informative of perturbed pathways , can be detected in dying parasites . In addition , significant RNA changes are seen when the hard-wired program , governing RNA levels , itself is altered . Our data formally proves that RNA levels for intermediary metabolism in malaria parasites are largely predetermined . We propose that as a parasite with a complex life cycle travels from one largely predictable intracellular biochemical environment to another , such hard-wiring may be sufficient to manage transcript levels for intermediary metabolism without employing sensory functions . Such a system-wide host–parasite difference in gene regulation may create unexpected pharmacological opportunities when important target pathways are rigid in the parasite but dynamically regulated in host cells .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"and",
"genomics/microbial",
"evolution",
"and",
"genomics",
"evolutionary",
"biology/microbial",
"evolution",
"and",
"genomics",
"cell",
"biology/gene",
"expression",
"genetics",
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"genomics/functional",
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"infectious",
"diseases/neglected",
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2008
|
A Genetically Hard-Wired Metabolic Transcriptome in Plasmodium falciparum Fails to Mount Protective Responses to Lethal Antifolates
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Elevation of aspartate aminotransferase ( AST ) and alanine aminotransferase ( ALT ) is prominent in acute dengue illness . The World Health Organization ( WHO ) 2009 dengue guidelines defined AST or ALT≥1000 units/liter ( U/L ) as a criterion for severe dengue . We aimed to assess the clinical relevance and discriminatory value of AST or ALT for dengue hemorrhagic fever ( DHF ) and severe dengue . We retrospectively studied and classified polymerase chain reaction positive dengue patients from 2006 to 2008 treated at Tan Tock Seng Hospital , Singapore according to WHO 1997 and 2009 criteria for dengue severity . Of 690 dengue patients , 31% had DHF and 24% severe dengue . Elevated AST and ALT occurred in 86% and 46% , respectively . Seven had AST or ALT≥1000 U/L . None had acute liver failure but one patient died . Median AST and ALT values were significantly higher with increasing dengue severity by both WHO 1997 and 2009 criteria . However , they were poorly discriminatory between non-severe and severe dengue ( e . g . , AST area under the receiver operating characteristic [ROC] curve = 0 . 62; 95% confidence interval [CI]: 0 . 57–0 . 67 ) and between dengue fever ( DF ) and DHF ( AST area under the ROC curve = 0 . 56; 95% CI: 0 . 52–0 . 61 ) . There was significant overlap in AST and ALT values among patients with dengue with or without warning signs and severe dengue , and between those with DF and DHF . Although aminotransferase levels increased in conjunction with dengue severity , AST or ALT values did not discriminate between DF and DHF or non-severe and severe dengue .
Dengue is a mosquito-borne arboviral infection endemic to most tropical and subtropical countries [1] . Elevation of the liver enzymes aspartate aminotransferase ( AST ) and alanine aminotransferase ( ALT ) is common in acute dengue illness , occurring in 65–97% [2] , [3] , [4] , [5] of dengue patients , peaking during the convalescent period of illness ( days 7–10 ) [2] , [4] , [6] . In dengue-endemic countries , dengue is an important cause of acute viral hepatitis [7] . Elevated AST and ALT levels have been associated with bleeding [2] , [4] , [6] and dengue hemorrhagic fever ( DHF ) [3] , [8] . Liver failure has been recognized as a complication and unusual manifestation of dengue [9] , [10] but occurred infrequently in 3 of 270 patients in Taiwan [6] and 5 of 644 patients in Vietnam [4] . In Malaysia , 8 of 20 pediatric DHF patients developed liver failure , 1 died , and the rest recovered completely [11] . In Singapore , AST or ALT levels were not independent predictors of DHF in 1973 adult dengue patients [12] . In 2009 , the World Health Organization ( WHO ) revised its dengue guidelines and proposed severe organ impairment as one category of severe dengue in addition to severe plasma leakage and severe bleeding [1] . Severe liver involvement was defined as AST or ALT≥1000 units/liter ( U/L ) . In Taiwan , AST>10 times the upper limit of normal ( ULN ) occurred in 11% of dengue patients [6] , while in Brazil this occurred in 4% of their cohort [3] . In this study , we aimed to evaluate the clinical relevance of elevated AST and ALT levels and correlate liver aminotransferase levels with dengue severity according to WHO 1997 and 2009 classifications .
All laboratory-confirmed dengue patients identified from our hospital microbiology database and treated using a standardized dengue clinical care path at the Department of Infectious Diseases , Tan Tock Seng Hospital ( TTSH ) , Singapore from 2006 to 2008 were retrospectively reviewed for demographic , serial clinical and laboratory , radiological , treatment , and outcome data . These cases were positive by real-time polymerase chain reaction ( PCR ) [13] . We included patients with only positive dengue serology in only subgroup analyses , as we did not have paired sera , and other etiologies for elevated AST and ALT could not be excluded without more extensive evaluation . Cases were categorized using serial clinical and laboratory data from the entire clinical course as dengue fever ( DF ) , DHF , or dengue shock syndrome ( DSS ) using WHO 1997 classifications [9] . Dengue fever classification requires fever and at least two of the following: headache , eye pain , myalgia , arthralgia , rash , bleeding , and leukopenia . Dengue hemorrhagic fever requires all of the following: fever , platelet count ≤100×109/liter , bleeding , and plasma leakage [9] . Dengue shock syndrome is a case of DHF with either tachycardia and pulse pressure <20 mmHg or systolic blood pressure <90 mmHg [9] . Cases were also categorized as dengue without warning signs ( WS ) , dengue with WS , or severe dengue using WHO 2009 classifications [1] . Dengue ( WHO 2009 ) requires fever and two of the following: nausea , vomiting , rash , aches and pains , leukopenia , or any warning sign [1] . Warning signs include abdominal pain or tenderness , persistent vomiting , clinical fluid accumulation , mucosal bleeding , lethargy or restlessness , hepatomegaly , or hematocrit rise ( ≥20% ) with rapid drop in platelet count ( <50 , 000/liter ) [1] , [14] . We modified the WHO 2009 warning sign of rise in hematocrit concurrent with rapid drop in platelet count by quantifying it as hematocrit ≥20% concurrent with platelet count <50 , 000/liter , as this was shown to correlate significantly with dengue death in our adult dengue death study [14] . Severe dengue includes severe plasma leakage , severe bleeding , and severe organ impairment [1] . We performed a subgroup analysis for median maximum AST and ALT values stratified by febrile ( days 1–3 of illness ) , critical ( days 4–6 ) , and convalescent ( days 7–10 ) phases as defined by WHO 2009 [1] and compared across dengue severity classification according to WHO 1997 [9] and 2009 [1] . We excluded severe dengue due to isolated elevation of AST or ALT≥1000 U/L from our definition of severe dengue outcome , as this would be a confounder in assessing the relevance of AST or ALT levels in defining dengue severity . Patients had AST/ALT taken at presentation and then throughout hospitalization at the physician's discretion . Maximum AST and ALT values recorded at a median of 4 days of illness ( interquartile range [IQR]: 3–5 days ) were used in this analysis . Those with pre-existing liver diseases were excluded . At TTSH , the ULN for AST is 41 U/L; for ALT , it is 63 U/L for males and 54 U/L for females . We assessed the clinical relevance of elevated AST or ALT levels using four liver failure criteria—two for acute liver failure , and two that determine prognosis from chronic liver disease . The American Association for the Study of Liver Diseases ( AASLD ) recommends defining acute liver failure in a patient as: international normalized ratio ( INR ) ≥1 . 5 , any degree of altered mental status , and illness <26 weeks in duration without pre-existing cirrhosis [15] . The King's College criteria assess prognoses in those with acute liver failure; the criteria are: prothrombin time >100 seconds or 3 of the following: age >40 years , prothrombin time >50 seconds , serum bilirubin >18 mg/dL , time from jaundice to encephalopathy >7 days [16] . The model for end-stage liver disease ( MELD ) determines three-month mortality based on the following formula: 3 . 8× ( log serum bilirubin [mg/dL] ) +11 . 2× ( log INR ) +9 . 6× ( log serum creatinine [mg/dL] ) +6 . 4 [17] . The Child-Pugh criteria include assessment of degree of ascites , serum bilirubin and albumin , prothrombin time , and encephalopathy to determine one- and two-year survival [18] . The Mann-Whitney U and Kruskal-Wallis tests were used to determine statistical significance for continuous variables , and chi-square or Fisher's exact test for categorical variables . Statistical tests were conducted at the 5% level of significance . Receiver operating characteristic ( ROC ) curves showing the area under the curve ( AUC ) were generated to determine the discriminatory performance of aminotransferase values . All statistical analyses were performed using Stata 10 ( Stata Corp . , College Station , TX ) . This was a retrospective study involving data collection from medical records . All patient data were anonymized during analysis . This study was approved by the Institutional Review Board , National Healthcare Group , Singapore [DSRB E/08/567] .
Overall , 595 ( 86% ) had AST above the ULN , and 316 ( 46% ) had ALT above the ULN . Seven patients ( 1 . 0% ) had severe dengue according to WHO 2009 criteria concurrent with AST or ALT≥1000 U/L while three additional patients had severe dengue due to AST or ALT≥1000 U/L only . Of the former seven patients , 86% had severe plasma leakage , 29% had severe bleeding , and none had severe organ impairment other than isolated AST or ALT≥1000 U/L . Among the 3 patients admitted to the ICU , AST or ALT values were above the ULN but below 1000 U/L . No patients in our cohort developed acute liver failure under AASLD or King's College criteria . With Child-Pugh scoring , 2 ( 0 . 3% ) belonged to Child-Pugh class C . With MELD scoring , predicted three-month mortality of 6% were identified in 68 ( 10% ) patients in our cohort and 19 . 6% in 2 ( 0 . 3% ) patients . The same two patients who were Child-Pugh class C also had a predicted 19 . 6% three-month mortality using MELD scoring; they both had DSS and severe dengue . Median AST and ALT values for dengue without warning signs , dengue with warning signs , and severe dengue ( Table 1 ) were 83 . 5 U/L , 92 U/L , and 124 U/L , respectively ( p<0 . 001 ) ; median ALT values were 49 U/L , 53 U/L , and 73 . 5 U/L ( p = 0 . 002 ) . Table 2 shows median AST and ALT values for patients with DF , DHF , and DSS . Median AST values for these categories were 93 U/L , 103 U/L , and 137 . 5 U/L , respectively ( p = 0 . 01 ) , and median ALT values were 52 U/L , 60 U/L , and 74 U/L ( p = 0 . 05 ) . In a separate analysis of our serology-positive cohort ( n = 1487 ) , median AST values for dengue without WS , dengue with WS , and severe dengue were 84 U/L , 114 U/L , and 147 U/L ( p<0 . 001 ) . Median ALT values were 56 U/L , 73 U/L , and 97 . 5 U/L ( p = 0 . 01 ) . For patients with DF , DHF , and DSS , median AST values were 105 U/L , 130 U/L , and 129 U/L ( p<0 . 001 ) , and median ALT values were 68 U/L , 78 U/L , and 85 . 5 U/L ( p = 0 . 008 ) . In other hemorrhagic fevers , higher AST∶ALT ratios correlated with disease fatality [20] . In our PCR-positive cohort , median AST∶ALT ratios for DF , DHF , and DSS were 1 . 68 , 1 . 68 , and 1 . 88 ( p = 0 . 29 ) and for dengue without WS , dengue with WS , and severe dengue , they were 1 . 60 , 1 . 68 , and 1 . 78 ( p = 0 . 10 ) , respectively . The majority of our patients' maximum AST and ALT values were recorded during febrile ( n = 258 ) and critical ( n = 377 ) phases of acute dengue illness . By WHO 2009 dengue severity classification , the median AST and ALT values were significantly higher for severe dengue compared to dengue with and without warning signs during both the febrile and critical phases but not the convalescent phase ( Table 3 ) . By WHO 1997 classification , the median AST and ALT values were significantly higher for DHF versus DF and DSS in the febrile phase only but not critical and convalescent phases ( Table 4 ) . In order to determine the reliability of AST and ALT values in defining dengue severity , ROC curves for AST and ALT against severe dengue excluding isolated transaminitis were determined ( Figure 1 ) . The AUC for AST was 0 . 62 ( 95% confidence interval [CI]: 0 . 57–0 . 67 ) and for ALT , 0 . 60 ( 95% CI: 0 . 54–0 . 64 ) . This demonstrates that AST or ALT levels are insufficient to differentiate among the WHO 2009 dengue classifications . They were also poorly discriminatory between DF and DHF , as the areas under the curve ( AUC ) for AST and ALT were 0 . 56 ( 95% CI: 0 . 52–0 . 61 ) and 0 . 55 ( 95% CI: 0 . 51–0 . 59 ) , respectively ( Figure 2 ) . In our serology-positive cohort , the AUC values for AST and ALT were 0 . 56 and 0 . 54 for differentiating between DF and DHF . The AUC values for severe and non-severe dengue were 0 . 64 and 0 . 60 for AST and ALT , respectively . The box plots in Figure 3 for the distributions of AST values show considerable overlap among the liver enzyme values for those with dengue with and without warning signs , and severe dengue . Because there were extreme outliers in our cohort , only those with AST below 1000 U/L were included in these plots . Figure 4 shows overlapping AST values among those with DF and DHF . Similarly , considerable overlap was observed in ALT values for patients with dengue with and without warning signs , and severe dengue , as well as for DF versus DHF ( data not shown ) .
Our analysis showed that liver aminotransferase levels were associated with but did not adequately differentiate between dengue severity . Although median AST and ALT values were significantly higher in those with DHF/DSS versus DF , and severe dengue versus non-severe dengue , very few ( 1 . 0% ) had AST or ALT≥1000 U/L . Notably , none developed liver failure , and death occurred in only 1 patient ( 0 . 1% ) . The majority of patients recovered uneventfully . The lack of acute liver failure in our study was not unusual , as the incidence of acute liver failure in dengue patients was 1 . 1% in studies by Trung and Kuo [4] , [6] . The largest study to date reported no acute fulminant hepatitis [3] . In contrast to these adult studies , it is noteworthy that in dengue-endemic countries , dengue may be an important cause of acute liver failure in children [21] , [22] . While some studies have shown that AST and ALT values differ between DF and DHF [3] , [4] , [8] , few studies support AST or ALT as an independent predictor of DHF [23] . Two studies in Singapore found liver aminotransferase levels to be significantly elevated among DF and DHF patients [12] and survivors and non-survivors of dengue [24] on univariate analysis , but this association was lost after adjusting for confounders on multivariate analysis . Trung et al . showed significant differences comparing other febrile illness , dengue without plasma leakage , and dengue with plasma leakage with and without shock during critical and convalescent phases for AST but during critical phase for ALT only [4] . We made the novel finding that liver aminotransferase levels may significantly vary according to dengue severity during the febrile phase . For DHF by WHO 1997 classification , both AST and ALT were significantly higher during the febrile phase compared to DF or DSS , and for severe dengue by WHO 2009 , AST and ALT were significantly higher during the febrile and critical phases . The impact of co-infection with hepatitis viruses or concomitant hepatotoxic drugs was not assessed in our retrospective study , although we did exclude those with known liver comorbidities . Kuo et al . found that hepatitis B or C did not increase the extent of liver aminotransferase elevation in a retrospective adult dengue study in Taiwan [6] . In contrast , Trung et al . found that hepatitis B co-infection modestly increased ALT levels without significant clinical impact in a prospective adult dengue study in Vietnam [4] . Tang et al . showed that dengue and hepatitis B co-infected patients showed an aberrant cytokine secretion profile compared with those with dengue alone . [25] . In Singapore , seroprevalence for hepatitis B was 2 . 8% [26] and hepatitis C 0 . 37% [27] . The etiology of elevated aminotransferase levels during acute dengue illness is unclear since AST is expressed in the heart , skeletal muscle , red blood cells , kidneys , brain , and liver , while ALT is secreted primarily by the liver [28] , [29] . Because dengue infection can cause acute damage to these non-hepatic tissue types that express AST , raised aminotransferase levels may not be entirely due to severe liver involvement . It is therefore possible that the patients with high AST levels were also more likely to be classified as severe dengue under the 2009 criteria due to the common pathways to non-hepatic tissue damage , even though there is no association with poorer outcome . Our retrospective study has some limitations . Aspartate and alanine aminotransferase values were tracked according to clinical judgment rather than at regular intervals during illness . We did not have dengue serotype data for each patient , but in 2006 , DENV-1 was predominant in Singapore with a switch to DENV-2 in 2007–2008 [30] . Serology-positive cases were not included in primary analyses because our clinical laboratory used a rapid diagnostic test with potential for false positive results [31] , we did not have paired sera to confirm dengue diagnosis [9] , and not every patient with elevated AST or ALT was comprehensively evaluated for other etiologies of viral and non-viral hepatitis . Although serology-positive cases presented later during illness , we saw no difference in outcome . Five serology-positive patients ( 0 . 34% ) required ICU admission versus 0 . 43% of PCR-positive cases , while four patients ( 0 . 27% ) died in the serology-positive cohort , versus 1 patient ( 0 . 14% ) among PCR-positive cases . However , relative data accuracy in our retrospective study was made possible by using a standardized dengue clinical care path . Another limitation of this study is the relatively few cases with substantially elevated liver aminotransferase levels . At the same time , since our cohort comprised primarily adults , additional studies in pediatric populations will be useful to confirm our findings . In patients with DHF/DSS or severe dengue , early diagnosis and proper management may improve outcome in most patients without comorbidities . However , in resource-limited countries , patients with severe disease may present late to the hospital with shock , with or without organ impairment at the time of admission . Our study highlights that early diagnosis and proper management of dengue patients may lead to excellent prognosis without organ injury . In conclusion , elevated aminotransferase levels were associated with DHF/DSS and severe dengue in our cohort of adult patients with confirmed dengue . However , no threshold values discriminated between DF and DHF or between severe dengue and non-severe dengue .
|
Dengue is a global public health problem , as the incidence of the disease has reached hyperendemic proportions in recent decades . Infection with dengue can cause acute , febrile illness or severe disease , which can lead to plasma leakage , bleeding , and organ impairment . One of the most prominent clinical characteristics of dengue patients is increased aspartate and alanine aminotransferase liver enzyme levels . The significance of this is uncertain , as it is transient in the majority of cases , and most patients recover uneventfully without liver damage . In this study , we characterized this phenomenon in the context of dengue severity and found that , although liver enzyme levels increased concurrently with dengue severity , they could not sufficiently discriminate between dengue fever and dengue hemorrhagic fever or between non-severe and severe dengue . Therefore clinicians may need to use other parameters to distinguish dengue severity in patients during early illness .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"viral",
"hemorrhagic",
"fevers",
"infectious",
"diseases",
"public",
"health",
"and",
"epidemiology",
"clinical",
"epidemiology",
"clinical",
"research",
"design",
"epidemiology",
"infectious",
"disease",
"epidemiology",
"dengue",
"fever",
"neglected",
"tropical",
"diseases",
"retrospective",
"studies",
"arboviral",
"infections",
"statistical",
"methods"
] |
2012
|
Clinical Relevance and Discriminatory Value of Elevated Liver Aminotransferase Levels for Dengue Severity
|
Elucidation of regulatory roles played by microRNAs ( miRs ) in various biological networks is one of the greatest challenges of present molecular and computational biology . The integrated analysis of gene expression data and 3′-UTR sequences holds great promise for being an effective means to systematically delineate active miRs in different biological processes . Applying such an integrated analysis , we uncovered a striking relationship between 3′-UTR AU content and gene response in numerous microarray datasets . We show that this relationship is secondary to a general bias that links gene response and probe AU content and reflects the fact that in the majority of current arrays probes are selected from target transcript 3′-UTRs . Therefore , removal of this bias , which is in order in any analysis of microarray datasets , is of crucial importance when integrating expression data and 3′-UTR sequences to identify regulatory elements embedded in this region . We developed visualization and normalization schemes for the detection and removal of such AU biases and demonstrate that their application to microarray data significantly enhances the computational identification of active miRs . Our results substantiate that , after removal of AU biases , mRNA expression profiles contain ample information which allows in silico detection of miRs that are active in physiological conditions .
MicroRNAs ( miRs ) are a growing class of non-coding RNAs that is now recognized as a major tier of gene control , predicted to target more than 30% of all human protein-coding genes [1] , [2] . miRs suppress gene expression via binding to regulatory sites usually embedded in the 3′-UTRs of their target mRNAs , leading to the repression of translation occasionally associated with mRNA degradation . Target recognition involves complementary base pairing of the target site with the miR's seed region ( positions 2–8 at the miR's 5′ end ) , although the exact extent of seed complementarity is not precisely determined , and can be modified by 3′ pairing [2]–[4] . Despite intensive efforts in recent years , biological functions carried out by miRs have been characterized for only a minority of these genes , and therefore , elucidating regulatory roles played by miRs in various biological networks constitutes one of the major challenges facing biology today . Bioinformatics analyses can significantly contribute to elucidation of miR functions; in particular , the integrated analysis of gene expression data and 3′-UTR sequences that holds promise for systematic dissection of regulatory networks controlled by miRs and of cis-regulatory elements embedded in 3′-UTRs . Similar bioinformatics approaches that integrates gene expression data and promoter sequences proved highly effective in delineating transcriptional regulatory networks in a multitude of organisms ranging from yeast to human [5]–[7] . Microarray measurements reflect the total effect of all regulatory mechanisms that control gene expression , including both transcriptional and post-transcriptional mechanisms; thus , genome-wide expression profiles should yield ample information not only on transcriptional networks , but also on regulatory networks regulated by miRs and RNA binding proteins ( RBPs ) that modulate mRNA stability , and that usually act via regulatory elements in 3′-UTR of their target genes [8] . Although mRNA degradation seems to be a secondary mode of miRs' action ( with inhibition of translation being the primary one ) , since each miR is predicted to directly affect the expression level of dozens of target genes , such an orchestrated effect should be discernable by statistical analysis of wide-scale mRNA expression data , even if the effect on each target is only a subtle one . This orchestrated effect could serve as a molecular fingerprint for miRs activity under given biological conditions . Indeed , several pioneering studies provided strong evidence of the ability to computationally decipher miR-mediated regulatory networks from mRNA expression data alone or in correlation with miR expression profiles [9]–[14] . In this study , we applied an integrated analysis of gene expression data and 3′-UTR sequences aimed at identifying miRs that are active in a given biological process . Applying such analysis we discovered in numerous microarray datasets a major bias that resulted in a striking relationship between 3′-UTR AU content and gene response . We show that this surprising link between gene's response and 3′-UTR base composition is secondary to a more basic relationship between gene's response and base composition of its probes on the chip . We demonstrate that this bias causes many false positive calls in computational searches for active miRs from mRNA expression data . Therefore , removal of this bias , which is in order in any analysis of microarray datasets , is of crucial importance when integrating expression data and 3′-UTR sequences to identify regulatory elements embedded in this region . Our results substantiate that computational analysis of mRNA expression data , after appropriate removal of AU biases , can accurately detect active miRs that control various biological processes under physiological conditions .
We set out to demonstrate that integrated computational analysis of mRNA expression data and 3′-UTR sequences can accurately uncover miRs that participate in the regulation of a given biological process . As the role of miRs in different branches of hematopoiesis is well characterized [15]–[18] , we first analyzed a dataset that recorded global gene expression profiles for multi-potent hematopoietic progenitor cells ( HPCs ) undergoing multi-lineage differentiation [19] . Since miRs often induce degradation of their target mRNAs , we expected the 3′-UTR of genes whose expression is induced during differentiation to be enriched for seed signatures of miRs that become inactive in this process , and vice versa—that the 3′-UTR of genes whose expression is repressed would be enriched for seed signatures of miRs that become active during the process . Before employing statistical tests to identify over-represented seed sequences among up- or down-regulated genes , we examined whether a more global trend in base composition could be detected in the 3′-UTR sequences of the responding genes . For example , if the 3′-UTRs of the up-regulated genes are generally more AU-rich compared to the 3′-UTRs of the non-responding genes , then any statistical search for over-represented seed signatures among the up-regulated genes is expected to yield false positive calls for miRs whose seed signature is AU-rich . One effective means for detecting such false positive calls is to repeat the over-representation tests with randomly permuted miR seeds ( which preserve the seed's base composition ) . If an enrichment of a certain miR seed is accounted for merely by base composition , then it is expected to be non-specific and detected also for randomly permuted seeds derived from the original one . Therefore , as a first step in the analysis of the HPC dataset , we checked whether a global 3′-UTR base composition trend is associated with the multi-lineage differentiation . We detected a very strong correlation between 3′-UTR base composition and gene response at several time points in this dataset . For example , there was an exceptionally strong relationship between AU content and gene response at the 16 hr time point after induction of HPC differentiation into megakaryocytes: 3′-UTRs of down-regulated genes were significantly more AU-rich than those of up-regulated ones ( Figure 1 ) . ( The mean 3′-UTR AU content of the 5% most down-regulated and most up-regulated genes were 60 . 6% and 52 . 7% , respectively , p<10−99 , Wilcoxon test . ) The other three lineages in this dataset displayed similarly strong trends ( Figure S1 ) . The strength of the relationship between 3′-UTR AU content and gene response in the HPC dataset prompted us to search for such trends in other datasets . Surprisingly , we found such relationships , with similarly high statistical significance , in numerous microarray datasets ( data not shown ) . Still more suspicious , we observed the relationship even when we compared different control samples within a dataset . This led us to question whether the relationship observed between 3′-UTR AU content and gene response reflects any true biological regulatory mechanism , or is rather a result of some technical artifact in microarray measurements . We found a definitive answer to this question by analyzing a technical dataset published by van Ruissen et al . [20] . This dataset profiled a universal reference RNA pool in two independent oligonucleotide chips ( Affymetrix HGU133A ) . Comparing the data from these two arrays , which measure identical and artificial RNA pools , we again found a striking relationship between 3′-UTR AU content and difference in gene expression level ( Figure 2 ) , pointing to a major AU bias in microarray measurements . This AU response bias is not specific to a particular data preprocessing method , as it existed in data under different preprocessing and normalization schemes; namely , rma , gcrma , and mas5 ( Figure S2 ) . In this technical dataset , we detected no preference for A or U in the bias , and no major 3′-UTR length bias ( Figure S3 ) . Next , we sought to elucidate the sources of the AU response bias . A well-documented bias in microarray measurements is the one between probe intensity and response [21] , which is routinely visualized using M-A plots . We first suspected that the observed association between 3′-UTR AU content and gene response is a mere reflection of the intensity-response bias . However , there was no intensity-bias in the above technical dataset , which points that the 3′-UTR AU response bias is distinct from the intensity-response bias ( see Figure 3A and 3B; in the latter , adopting the concept of M-A plots , we introduced the M-AU plot to visualize the AU response bias ) . The AU response bias exists over a large range of intensities ( Figure S4 ) , and , furthermore , the gcrma method which takes into account the correlation between probe's AU content and intensity did not cancel it . In the vast majority of present chips , probes are selected from the 3′-end of target transcripts . This is also the case for the technical dataset that we have analyzed , which used the Affymetrix HGU133A chip . Therefore , as expected , we observed in this dataset also a strong relationship between probeset AU content and response ( similar to the one observed between gene's 3′-UTR AU content and response ) ( Figure S5 ) . To test whether the AU artifact origins either from base-composition properties of 3′-UTR of target transcripts or of that of the chip probes , the sequence of probes and target 3′-UTRs need to be uncoupled . The new generation Affymetrix chips break this coupling as their probes are selected from all regions of target transcripts . We therefore analyzed a second technical dataset , recently published by Pradervand et al . [22] which used the new Affymetrix Human Gene 1 . 0 ST Array . In this dataset too , we detected a strong AU response bias . That is , we observed a significant relationship between probeset AU content and response in a comparison between duplicate control chips . Importantly , carrying out a probe-level analysis , we found that probes located at 5′-UTR and CDS regions show a similar AU bias as probes located at 3′-UTRs ( Figure 4 ) . This finding indicates that the link between gene's response and 3′-UTR base composition is secondary to a more basic bias in microarray measurements which links gene response with base composition of its probes . We next evaluated the effect of the AU bias on computational identification of active miRs from microarray data . Searching for miRs that are active in biological conditions examined in a dataset , we utilized miR target prediction generated by TargetScanS [2] , and applied the following statistical test: for each miR family and for each condition in a dataset , we tested whether the set of predicted miR target genes is significantly induced or repressed compared to a background set consisting of all the non-target genes ( see Methods ) . The technical dataset which profiled the universal reference RNA pool served us as a negative test case in which no real biological signal exists . Applying the statistical tests to this dataset , we identified nine miR families whose target sets showed statistically significant response ( Table 1 ) . Of course , in this negative test case , all calls are false positive ones; and , as expected , all the falsely identified miR families had an AU-rich seed ( the seed of eight out of the nine calls contained at least 5 A or U bases , while the prevalence of miRs with such seed among all the miRs tested was less than 25%; Table 1 ) . Next , for each miR family identified as significant , we repeated the statistical tests , but this time with randomly permuted miR seeds . In all cases , permuted seeds showed similar statistical significance to the original seeds ( Table 1 ) , demonstrating the utility of such permutation tests in detecting non-specific results caused by correlation between base composition of miR-seeds and 3′-UTRs of the responding genes . As shown , the AU response bias causes many false positive calls in computational search for active miRs from expression data , and therefore its removal is crucial when carrying out integrated bioinformatics analysis of mRNA expression data and 3′-UTR sequences . To remove this bias , we adopted the lowess normalization method which is routinely used to remove intensity biases from microarray data [21] , and adjusted it to cancel AU biases ( Figure 5 ) ( see Methods ) . Applying AU normalization did not distort the normalization at the M-A plane ( Figure S6 ) . Importantly , after applying AU normalization to the negative control dataset , no miR family passed the statistical significance threshold ( 0 . 0003 , which corresponds to 0 . 05 after Bonferroni correction for multiple testing ) ( Table 1 ) . We next searched for an expression dataset that would serve as a positive test case; that is , a dataset that contains known miR signals . We preferred physiologically relevant datasets over ones that over-expressed miRs , which often give expression levels that are far above physiological ones . ( Statistical searches for active miRs applied to several datasets that profiled cells over-expressing specific miRs readily detected the correct signals both without and after AU normalization ( data not shown ) . ) A recent study that compared expression profiles between stimulated T-cells derived from miR-155 deficient and control mice met this requirement [23] . As in many other datasets , we observed a strong AU bias in this dataset too , and removed it using the AU normalization ( Figure 6 ) . Without AU normalization , the statistical tests identified eleven significant miR families; the true hit ( miR-155 ) was the third most significant one ( Table 2 ) . ( Note that five out of the six most significant miRs falsely identified on the negative dataset were detected also in this positive dataset ( compare Tables 1 and 2 ) ) . Here too , permutation tests found , in most cases , random seeds whose significance scores were similar to the ones obtained by the original seeds ( Table 2 ) . In sharp contrast , after AU normalization , only the true miR ( miR-155 ) was detected and its statistical significance was substantially improved ( Table 2 ) . Importantly , none of the permuted seeds derived from the seed of miR-155 obtained a statistically significant score . For a more challenging test case we used a dataset that monitored gene expression profiles in five distinct human T cells sub-populations representing five phases of T cell differentiation [24]: intrathymic T progenitor ( ITTP ) cells , double positive ( DP ) thymocytes , CD4 single positive ( SP4 ) , naïve CD4 T cells from cord blood ( CB4 ) , and naïve CD4 T cells from adult blood ( AB4 ) . To obtain fold-change measures , we divided the expression level at each development phase by the one measured in the mature AB4 T cells . Without AU normalization , the statistical tests identified six significant miR families: the target sets of three were down-regulated in ITTP cells , and the target sets of the other three were up-regulated in the SP4 cells ( Table 3 ) . After applying the AU normalization to the data , only the three miR-families whose target sets were repressed in ITTP ( miR-17 . 5p , miR-19 and miR-181 families ) remained significant ( Table 3 ) , suggesting that members of these three miR families are active in early phases of T cell development and become inactive as T cells mature . There is evidence that all three miR families detected by the statistical analysis play a role in thymocyte maturation and therefore are true hits . Li et al . recently [25] showed that miR-181a is highly expressed in immature T cells and that its expression level goes down as T cells proceed through differentiation . That study further showed that miR-181a plays a critical role in augmenting T cell sensitivity , a propensity that is vital to the elimination of self-reacting T cells early during maturation . Regarding miR-17 . 5p and miR-19 families , Landais et al . recently reported that the miR-106-363 cluster is over-expressed in 46% of human T-cell leukemias tested [26] . The miR-106-363 cluster is homolog to the miR-17-92 cluster , and miR-19 is contained in both clusters but carries a seed which is different from the one of the other miRs in these two clusters . It is possible that up-regulation of members of the miR-106-363 and miR-17-92 clusters in T-cell leukemia endows these cells with propensities normal to immature T-cells , most probably enhanced proliferation capacity . The identification of true hits on this dataset further demonstrates that computational analysis can accurately dissect active miRs from gene expression data probing cells under physiological conditions . Our statistical analysis utilizes target prediction based on miR seed signatures and therefore cannot distinguish between miRs sharing seed sequences . Empirical biological testing is required to pinpoint which members of the miR-17-92 and miR-106-363 clusters that carry a common seed sequence are actually active during T cell maturation .
In the course of this study we observed in many gene expression datasets a striking association between gene response and 3′-UTR base composition . The high prevalence of such a relationship in microarray datasets , its exceptional statistical strength , and its detection in technical comparisons between replicate arrays , point unequivocally to a major bias in microarray measurements that was heretofore missed . Such a major AU bias in microarray measurements might have gone undetected because gene expression data are commonly analyzed in association with promoter , rather than 3′-UTR sequences , in attempts to unravel cis-regulatory promoter elements that control gene transcription . Only recently , with the emergence of miRs and RNA-binding proteins as key post-transcriptional regulators of gene expression , has gene expression analysis been coupled with analysis of 3′-UTR sequences . Indeed , it was the search for active miRs that motivated us to integrate gene expression and 3′-UTR sequence data , and led us to the detection of the AU response bias in microarray data . We demonstrated that this bias is distinct from the well-documented intensity-response and AU intensity biases , and that it originates from a systematic association between probe base composition and response . Using the new generation Affymetrix chips that contain probes selected throughout the transcripts , we uncoupled the sequences of probes and target 3-UTRs . We show that probes exhibit similar AU response bias irrespective of their location in the target transcripts . Therefore , the major link between gene response and 3′-UTR base composition that we observed in vast microarray datasets , is secondary to the general probe AU response bias , and simply reflects the fact that chip probes were selected from 3′-UTRs . A reasonable explanation to the AU response bias is that there are subtle differences in hybridization conditions for different arrays in a dataset , and that the effect of such differences is dependent on probe base composition . Further technical examinations are required to test this point . Bioinformatics analysis that integrates gene expression data and 3′-UTR sequences holds promise for systematic dissection of regulatory networks controlled by miRs . However , we demonstrated that the AU response bias causes many false positive calls in such analysis . Permutation tests were highly effective in revealing such false positive hits . Removal of this bias is of crucial importance when aiming to uncover miR-signatures as well as other cis-regulatory elements embedded in 3′-UTRs from mRNA expression profiles . We therefore developed visualization and normalization schemes for the detection and removal of AU biases , and demonstrated that their application to microarray data significantly enhances the computational identification of active miRs . In the case of Affymetrix chips , the normalization scheme that we implemented works at the probe-set or transcript level , and corrects the AU bias in a post-processing step ( i . e . , ran after probe intensity levels were calculated ) . A normalization scheme that takes into account the AU response bias at the phase of probe intensity calculation ( similar to gcrma , which cancels AU intensity biases ) is still required . Our results further substantiate that mRNA expression data contain ample information that allows , after proper removal of AU biases , in silico detection of active miRs . Importantly , this is also true when mRNA profiles were measured under physiological conditions . In view of the importance of elucidating regulatory roles played by miRs in various biological networks , we anticipate that the methods introduced in this study for detection , visualization and removal of the AU response bias from microarray data will be in wide use by the research community .
In this study , we analyzed four microarray datasets which used 3′-UTR Affymetrix oligonucleotide chips ( that is , chips in which probes are selected from targets' 3-UTRs ) , and one dataset that used the new generation Affymetrix Human Gene 1 . 0 ST Array , in which probes are located throughout the target transcripts . Raw data files ( CEL files ) were downloaded from GEO ( http://www . ncbi . nlm . nih . gov/geo/ ) or ArrayExpress ( http://www . ebi . ac . uk/microarray-as/aer/#ae-main0 ) DBs , or obtained directly from the authors of the data . 3′-UTR sequences and miR target prediction for human and mouse were downloaded from TargetScanS ( http://www . targetscan . org/; version 4 . 0; July 2007 ) . TargetScanS predicts gene targets of miRNAs by searching 3′-UTRs for the presence of conserved 8-mer and 7-mer sites that match the seed region of each miRNA family [2] . In case a gene has several annotated 3′-UTRs , the longest one is considered .
|
MicroRNAs are a novel class of genes that encodes for short RNA molecules recognized to play key roles in the regulation of many biological networks . MicroRNAs , predicted to collectively target more than 30% of all human protein-coding genes , suppress gene expression by binding to regulatory elements usually embedded in the 3′-UTRs of their target mRNAs . Despite intensive efforts in recent years , biological functions carried out by microRNAs have been characterized for only a small number of these genes , making elucidation of their roles one of the greatest challenges of biology today . Bioinformatics analyses can significantly help meet this challenge . In particular , the integrated analysis of microarray mRNA expression data and 3′-UTR sequences holds great promise for systematic dissection of regulatory networks controlled by microRNAs . Applying such integrated analysis to numerous microarray datasets , we disclosed a major technical bias that hampers the identification of active microRNAs from mRNA expression profiles . We developed visualization and normalization schemes for detection and removal of the bias and demonstrate that their application to microarray data significantly enhances the identification of active microRNAs . Given the broad use of microarrays and the ever-growing interest in microRNAs , we anticipate that the methods we introduced will be widely adopted .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"genetics",
"and",
"genomics/bioinformatics",
"genetics",
"and",
"genomics/gene",
"expression"
] |
2008
|
Removal of AU Bias from Microarray mRNA Expression Data Enhances Computational Identification of Active MicroRNAs
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The growing availability of sequence information from diverse parasites through genomic and transcriptomic projects offer new opportunities for the identification of key mediators in the parasite–host interaction . Functional genomics approaches and methods for the manipulation of genes are essential tools for deciphering the roles of genes and to identify new intervention targets in parasites . Exciting advances in functional genomics for parasitic helminths are starting to occur , with transgene expression and RNA interference ( RNAi ) reported in several species of nematodes , but the area is still in its infancy in flatworms , with reports in just three species . While advancing in model organisms , there is a need to rapidly extend these technologies to other parasites responsible for several chronic diseases of humans and cattle . In order to extend these approaches to less well studied parasitic worms , we developed a test method for the presence of a viable RNAi pathway by silencing the exogenous reporter gene , firefly luciferase ( fLUC ) . We established the method in the human blood fluke Schistosoma mansoni and then confirmed its utility in the liver fluke Fasciola hepatica . We transformed newly excysted juveniles of F . hepatica by electroporation with mRNA of fLUC and three hours later were able to detect luciferase enzyme activity , concentrated mainly in the digestive ceca . Subsequently , we tested the presence of an active RNAi pathway in F . hepatica by knocking down the exogenous luciferase activity by introduction into the transformed parasites of double-stranded RNA ( dsRNA ) specific for fLUC . In addition , we tested the RNAi pathway targeting an endogenous F . hepatica gene encoding leucine aminopeptidase ( FhLAP ) , and observed a significant reduction in specific mRNA levels . In summary , these studies demonstrated the utility of RNAi targeting reporter fLUC as a reporter gene assay to establish the presence of an intact RNAi pathway in helminth parasites . These could facilitate the study of gene function and the identification of relevant targets for intervention in organisms that are by other means intractable . More specifically , these results open new perspectives for functional genomics of F . hepatica , which hopefully can lead to the development of new interventions for fascioliasis .
Parasitic diseases are a major problem worldwide being not only a health issue but also , when affecting productive species , an important factor in the economy . Advances in biochemistry and molecular biology of parasites have made possible to identify at the molecular level several candidate mediators in the parasite-host interaction . However , the validation of the role of these molecules is hampered in many cases by the absence of appropriate tools of analysis , in particular functional genomics approaches to address the importance of a target gene in the pathogen . While functional genomics of parasitic nematodes have benefited from the advancements in the model species Caenorhabditis elegans , in parasitic flatworms , this avenue has been explored almost exclusively in species of the genus Schistosoma , but the techniques are still in their infancy ( reviewed in [1]–[4] ) . Transfection with reporter genes using endogenous promoters has been attempted successfully in adults , miracidia and sporocysts of S . mansoni by biolistic approaches [5]–[11] . Transient transfection by electroporation was achieved in schistosomules and adults of S . mansoni [12] and S . japonicum [13] . More recently advancements towards stable transduction using retroviral and transposon retroposon sequences have been made [14] , [15] . Gene silencing by RNA interference was first demonstrated in schistosomules by soaking worms in double stranded RNA [16] and later extended to sporocysts [17] . Electroporation was also tested as a delivery method for dsRNA providing for stable and long term effects [18] . These initial reports paved the ground for the use of the methodology in the analysis of gene function [19]–[23] . Although the possibilities of this techniques in parasitic flatworms are still far away from the systematic use of RNAi at genomic scale as initiated in free living planarians [24]–[27] , the existence of extensive genomic and expression information for S . mansoni and S . japonicum [28]–[32] provide opportunities for advances in the discovery of new anti-schistosome interventions . Apart from schistosomes , reverse genetics techniques have been reported in just three other species of parasitic flatworms , but they are prominent examples of the relevance of these techniques for parasite biology and control . Germinal cells from the cestode Echinococcus multilocularis were successfully maintained in culture and transfected with reporter plasmids . Furthermore , these cells were also experimentally infected with Listeria monocytogenes , a facultative intracellular bacteria used as DNA delivery system for genetic manipulation of mammalian cells [33] . Germ cells development in the monogenean Neobenedenia girellae was altered by soaking with dsRNA of two vasa related genes , opening promising avenues for control measures [34] . Also , the role of cysteine proteases in parasitic invasion was validated in an assay using RNAi directed against cathepsins L and B in the trematode Fasciola hepatica [35] . We are interested in extending these methodologies to other parasitic flatworms , and to gain insights from comparative analysis of genes in less comprehensively studied flukes . For this purpose , we attempted to build on the growing schistosome reverse genetics background , in order to generate a simple reporter gene assay to be tested in other helminthes . While schistosome infection is the most relevant trematode infection of humans worldwide , fascioliasis holds a similar status in ruminants , with at least 700 million animals infected , and at least one fourth of the world livestock grazing at areas where the parasite is present . The economic losses caused by the infection are conservatively estimated at USD 3 . 2 billion per annum , with a more pronounced impact to rural agricultural communities in developing countries [36] . Recently , the disease has also emerged as a major zoonosis mainly in rural areas of central South America , Northern Africa and Central Asia , with approximately 2 . 4 million infected people worldwide and 180 millions at risk [37] , [38] . Although the anthelmintic triclabendazole is effective for controlling Fasciola infection [39] , reports on drug resistance are increasing , indicating that selection of resistant parasites may eventually compromise its use ( reviewed in [40] ) . Genomic approaches have begun in Fasciola , with a small set of ESTs available ( ftp://ftp . sanger . ac . uk/pub/pathogens/Fasciola/hepatica/ESTs/ ) , and proteomics had also been applied in the analysis of parasite excreted/secreted products [41] , [42] . However , despite this growing catalogue of sequences , the functional analysis and characterization of potential intervention targets is hampered by the lack of reliable methods of reverse genetics . F . hepatica represents a relevant candidate to evaluate the amenability of transferring reverse genetics techniques already applied in schistosomes [2] , [20] , particularly the suppression of gene activity mediated by double stranded RNA . At the same time that this work was under way , other colleagues demonstrated for the first time the existence of a viable RNAi pathway in this trematode [35] . In this report , we established a model test system in S . mansoni to indicate the presence of a viable RNAi pathway , based on the inactivation of an exogenous reporter . We transferred transfection technologies tractable in S . mansoni to F . hepatica juveniles which allowed not only an effective electroporation-based delivery method for nucleic acids to the liver flukes but also provided a method of quantitative analysis of a powerful reporter expression system . In addition , we confirmed the existence of a functional RNAi pathway in Fasciola using both the exogenous luciferase reporter and an endogenous protease gene . Finally , these methods should also find utility in investigation of other less characterized helminth parasites .
Biomphalaria glabrata snails infected with the NMRI ( Puerto Rican ) strain of Schistosoma mansoni were supplied by Dr . Fred Lewis , Biomedical Research Institute ( Rockville , MD , USA ) . Cercariae released from infected B . glabrata snails were concentrated by centrifugation ( 2000 rpm/10 min ) , washed once with somule wash medium , and mechanically transformed by shearing off the tails by 20 passes through 22G emulsifying needles . Schistosomule bodies were isolated from free tails by Percoll gradient centrifugation [43] , washed three times in wash medium and cultured at 37°C under 5% CO2 in modified Basch's medium supplemented with washed human erythrocytes [44] . Culture media were changed every second day , and the viability of the schistosomules was monitored under the microscope . Metacercariae of Fasciola hepatica were purchased from Baldwin Aquatics Inc . ( Monmouth , Oregon ) . The in vitro excystment was performed as described [45] with minor modifications . Briefly , metacercariae were placed in a 100 µm filter and incubated 5 min at room temperature with 1% sodium hypochlorite to remove the outer cyst wall . After an exhaustive wash in PBS , the metacercariae were incubated at 39°C in activation medium ( 25 mM HCl and 16 , 5 mM L-cysteine , 0 . 1% sodium taurocholate , 60 mM NaHCO3 , 70 mM NaCl pH 8 . 0 ) , and the excystment process was monitored under the microscope . After 90–180 min of incubation , newly excysted juveniles ( NEJs ) began to emerge , and were collected , washed several times with RPMI-1640 , transferred into 6 wells plates and keep in culture at 37°C under 5% CO2 with Basch's medium or schistosomule wash medium ( RPMI 1640 supplemented with 200 U/ml Penicillin G sulfate , 200 µg/ml streptomycin sulfate , 500 ng/ml amphotericin B , 10 mM HEPES ) . For longer term culture up to days , larval flukes were incubated in Basch's medium [44] , culture media were replaced every fourth day and viability of NEJs monitored microscopically . To synthesize firefly luciferase mRNAs ( mLuc ) , DNA template were prepared by PCR from pGL3-Basic ( Promega , Madison , WI , USA ) templates as described [12] . In vitro transcriptions of capped RNAs from PCR DNA templates were accomplished using the mMessage mMachine T7 Ultra kit ( Ambion , Austin , TX , USA ) according to the manufacturer's instructions . Subsequently , LiCl-precipitated RNAs were dissolved in nuclease-free water and quantified by spectrophotometer ( ND-1000 , NanoDrop Technologies , Wilmington , DE ) . All of the dsRNA used in the experiments were generated by in vitro transcription using as templates PCR products generated with gene specific primers tailed with the T7 promoter sequence . A luciferase dsRNA template encoding the full length 1 , 672 kb was generated using the pGL3-basic plasmid ( Promega , Madison , WI ) as template ( F: 5′-TAA TAC GAC TCA CTA TAG GG T GCG CCC GCG AAC GAC ATT TA-3′; R: 5′-TAA TAC GAC TCA CTA TAG GGG CAA CCG CTT CCC CGA CTT CCT TA-3′ ) . A 808 bp fragment of the E . coli malE gene included in the control plasmid LITMUS 28iMal from the HiScribe RNAi Transcription kit ( New England BioLabs , Ipswich , MA ) was amplified by PCR from the opposing T7 promoters of the vector with a T7 promoter-specific primer , generating a template for the double-stranded MalE ( dsMalE ) control . 482 bp from the 5′ portion of the Fasciola hepatica leucine aminopeptidase gene ( FhLAP , GenBank accession AY64459 ) was generated by PCR from the full length FhLAP cDNA [46] using gene-targeted primers containing T7 promoter sequence F: 5′- TAA TAC GAC TCA CTA TAG GG ATC TGC TAC TCA ATG CTC TG-3′ and R: 5′- TAA TAC GAC TCA CTA TAG GGCAC TCC GTT CGC CTT GAT GT-3′ ( spanning coding DNA position 389–871 ) . dsRNA was synthesized and purified using the Megascript RNAi kit ( Ambion ) according to the manufacturer's instructions . Integrity of the dsRNAs was verified by non-denaturing 1% agarose gel electrophoresis and purity was accessed by the ratio A260/A280 . dsRNA was precipitated with 1 volume of 5 M ammonium acetate and 2 . 5 volumes of 95% ethanol after which the RNA pellet was dissolved in water . Concentration of dsRNA was determined spectrophotometrically ( ND-1000 , NanoDrop Technologies , Wilmington , DE ) . Schistosomules of Schistosoma mansoni or NEJs of Fasciola hepatica removed from culture at different time points after cercarial transformation or excystment respectively , were transformed with nucleic acids preparation as described [12] . In brief , 1 , 500–2 , 000 NEJs or 10 , 000 schistosomules resuspended in 100 µl of wash medium containing 5 µg of luciferase mRNA ( final concentration 50ng/µl ) in 4 mm gap cuvettes were subjected to square wave electroporation ( 125 V , 20 ms ) in a BTX ElectroSquarePorator™ ECM830 ( BTX , San Diego , CA ) . Immediately after electroporation , the larval flukes were transferred to prewarmed Basch's Medium and maintained in culture as indicated . Fluorescent siRNA molecules ( Silencer Cy 3-Labeled Negative Control #1 siRNA , Ambion , Austin , TX , USA ) were used for transfection , following the same electroporation protocol describe above . Parasites were electroporated with 0 , 50 or 100 ng/µl of fluorescent siRNA , respectively . Three hours after electroporation , worms were rinsed in fresh culture medium , observed in a Olympus IX81 fluorescent microscope and photographed with an Hamamatsu C4742-8012AG . Digital Camera . The parasites were maintained in culture and fluorescence detected and registered 24 hours after treatment . For dsRNA electroporation the same protocol was followed using 300 ng/µl of dsRNA , in a final volume of 100 µl of somule wash medium . Ten µg of a plasmid DNA construct encoding luciferase driven by the actin 1 . 1 gene promoter of S . mansoni ( pLuc ) ( from ref [14] ) was used to transform schistosomules pretreated with or without luciferase dsRNA , following the same electroporation settings described above . Since there were abundant previous data on electroporation , reporter systems and RNAi in S . mansoni we conducted several single experiments testing different timings for the electroporation . All the results obtained were consistent within them and with previously published data . All the experiments involving F . hepatica juveniles were repeated three times . Parasites were harvested at different hours after electroporation , washed three times with schistosomule wash medium and stored as wet pellets at −80°C . Luciferase activity in extracts of these parasites was monitored using Promega's luciferase assay reagent system and a Sirius luminometer ( Berthold , Pforzheim , Germany ) [12] . In brief , pellets of parasites were subjected to sonication ( 3×5s bursts , output cycle 4 , Heat Systems-Ultrasonics , Plainview , NY , USA ) in 250 µl CCLR lysis buffer ( Promega ) . Aliquots of 100 µl of sonicate were injected into100 µl luciferin substrate ( Promega ) at room temperature , mixed , and the relative light units ( RLUs ) were determined in the luminometer 10 s later . Duplicate samples were measured , with results presented as the average of the readings per mg of soluble fluke protein . The protein concentration in the soluble fraction of the extract was determined using the bicinchoninic acid assay ( BCA kit , Pierce , Rockford , IL ) . Recombinant luciferase ( Promega ) was included as a positive control . A digoxigenin labeled antisense Firefly Luciferase riboprobe was generated by transcription according to the manufacture's protocol ( Roche Applied Science ) using as template a 553pb PCR product amplified from the pGL3-basic plasmid ( Promega , Madison , WI ) with a gene specific forward primer ( F: 5′-GTG CCA GAG TCC TTC GATAG-3′ ) and a gene specific reverse primer tailed with the T7 promoter sequence ( R: 5′- TAA TAC GAC TCA CTA TAG GG ACA ACT TTA CCG ACC GCG CC-3′ ) . Worms were electroporated with 0 ( “mock control” ) , or 50 ng/µl of luciferase mRNA ( mLuc ) and fixed three hours after treatment at 4°C overnight in 4%paraformaldehyde in PBS , washed in PBS , dehydrated and stored at −20°C in 100% ethanol . After rehydration with 75% , 50% and 25% ethanol in Holfreter buffer ( NaCl 2 . 188 g , KCl 0 . 031 g , CaCl2 0 . 063 g , NaHCO3 0 . 125 g , in 1000 ml H2O ) , the parasites were washed in PBS-T ( PBS with 0 . 1% Triton X-100 ) 30 mins , and treated with proteinase K ( 20 µg/ml in PBS-T ) for 5 minutes at 37°C . Digestion was stopped with cold Holfreter , and worms were postfixed in 4% formalin in Holfreter for 60 minutes , rinsed in Holfreter two times ( 20 mins each ) , and prehybridized for 120 minutes at 55°C in hybridization solution ( 50% formamide , 5× SSC , 1 mg/ml yeast tRNA , 100 µg/ml heparin , 0 . 1% Tween-20 ) . The riboprobe was desnaturalized by heating to 70°C for 3 minutes , immediately transferred to ice for 5 min , diluted to 1 ng/µl in hybridization solution and added to samples for hybridization at 55°C for 16–36 hours . After hybridization , worms were washed in 50% formamide , 5× SSC , and 0 . 1% Tween 20 , for 60 mins , four times at 55°C , rinsed twice in MAB-T ( Triton-X 100 0 . 1% in MAB ( maleic acid 11 . 6 g , NaCl 9 . 76 g , NaOH 2N 95 ml , in 1000 ml H2O , pH 7 , 5 ) , and then incubated for 30 minutes at room temperature in blocking solution ( 0 . 5% Roche blocking reagent in MAB-T , 5% ) . After blocking , parasites were incubated overnight at room temperature with 1∶2000 alkaline-phosphatase ( AP ) -conjugated anti-digoxigenin antibody ( DIG Nucleic Acid Detection KIT- Roche Applied Science ) in MAB-T , rinsed four times in MAB-T ( 5 min the first wash and 60 min the remaining washes ) , and once in AP buffer ( 100 mM Tris pH 9 . 5 , 150 mM NaCl , 25 mM MgCl2 , Triton X-100 0 . 1% ) for 5 min . Signal was detected following incubation of the organisms in NBT- BCIP stock solution ( Roche Applied Science ) 5 mM levamisol in buffer AP in dark . When the chromogenic reaction was complete ( 2 to 4 hours ) , the organisms were washed twice in PBS 1× , postfixed for 20 minutes in 4% paraformaldehide and stored in glycerol at 4°C . Worms were visualized and photographed using an OLYMPUS BX 40 microscope equipped with a SAMSUNG SDC–310 camera connected to a computer running Image Pro software . The endogenous expression of FhLAP mRNA ( GeneBank AY644459 ) was determined in NEJs cultured for 48 hours . RNA was extracted from the worms using the RNAqueus-Micro Kit ( Ambion , Austin , TX ) following the manufacturer's instruction . Any residual DNA remaining in the RNA preparations was removed by DNase digestion using TurboDNase ( Ambion ) . cDNA was synthesized from 500 ng , 50 ng and 5 ng ( three 10-fold serial dilutions ) of NEJ RNA using the iScript cDNA Synthesis Kit ( BioRad , Hercules , CA ) . FhLAP cDNA was amplified using F: 5′- ATG TGG CCG ATG AGA TTC TGG T-3′; R: 5′-AAT CCA CTA GCC AAT GCC AT-3′ , ( spanning LAP coding DNA position 1010-1437 ) amplifying a 427 bp product that span a 3′region from the dsRNA target sequence . F . hepatica GAPDH ( GenBank AY005475 ) was used as a control housekeeping gene using the primers F: 5′-GCG CCA ATG TTC GTG TTC GG -3′ ; R: 5′-TGG CCG TGT ACG AAT GCA C -3′ generating a product of 172 bp . F . hepatica Cathepsin L3 ( FhCathL3 ) ( GenBank DQ534446 ) was amplified with primers L3F: 5′- TTT CAT ATG AAG CCG AAG GC -3′ and L3R: 5′- GGC TAC TCC AAG CTC TTT CC -3′ producing a 344 bp fragment . F . hepatica Cathepsin B2 ( FhCathB2 ) ( GenBank DQ534444 ) was amplified , with primers CB2F: 5′- CAC GGC GGC AGC CAG TG-3′ and CB2R: 5′- TTC GAG AGT CAC CAA CGT GAT C-3′ generating a 250 bp fragment . The presence of the luciferase mRNA was determined by amplification with primers LUCF F: 5′-GTG CCA GAG TCC TTC GAT AG- 3′ and LUCR F: 5′- ACA ACT TTA CCG ACC GCG CC -3′ generating a fragment of 555 bp . The PCR conditions included an initial denaturation at 94°C for 30 sec followed by 35 cycles of 30 sec at 94°C , 60 sec at 55°C for the FhLAP amplicon , 58°C for the GAPDH amplicon , 63°C for the CathL3 and Cath B2 amplicon , and 51°C for LUC amplicon , 60 sec at 72°C and a final extension at 72°C for 10 min . Images of PCR products in ethidium-stained gels were documented using a Versadoc imaging system and QantityOne software ( BioRad ) . Soluble protein extracts from treated and control juveniles were prepared by sonication-induced lysis ( 5×5 s bursts on ice , output control value 3 , model W-220F Sonicator , Heat Systems—Ultrasonics , Inc . , Plainview , New York ) in 100 mM glycine , 1 mM MnCl2 , pH 8 . 5 . After centrifugation of the lysate for 10 min at 4°C at 14 , 000 rpm , the supernatant was employed as soluble F . hepatica juvenile extract . The protein concentration in the soluble fraction was determined using the bicinchoninic acid assay ( BCA kit , Pierce , Rockford , IL ) . Enzymatic reactions were performed in triplicates using 1 µg of soluble protein and 50 µM of the fluorogenic substrates H-Leu-AMC ( Bachem ) for LAP or Z-Phe-Arg-AMC ( Bachem ) for cathepsins L , in a total reaction volume of 200 µl of the appropriate buffer ( 100 mM glycine , 1 mM MnCl2 , pH 8 . 5 for LAP , and 50 mM sodium phosphate buffer pH 6 , 1 mM DTT , 1 mM EDTA for cathepsins ) . Reactions were incubated at 37°C and the release of AMC was continually monitored at 355 nm ( excitation ) and 460 nm ( emission ) for 40 min in a FluoStar Galaxy spectrophotometer ( BMG Lab technologies , Toronto ) . Student's t-test was employed to assess the statistical significance of differences observed . p values of the difference are given in the Results section or figure legends .
In order to establish an informative assay to detect the presence of a viable RNAi pathway , we hypothesized that expression of an exogenous transgene might be inhibited or knocked down by transgene specific dsRNA if the RNAi pathway was present and functional . To validate the system , we tested a protocol using schistosomules of S . mansoni as the target helminth because the RNAi pathway is active in the species , and consequently constitutes a positive control for the system . Schistosomules of one , two days or twelve days after mechanical transformations from cercariae were transformed with luciferase dsRNA ( dsLuc ) by electroporation . At different time points after this treatment with dsLuc , the schistosomules were subjected to a second electroporation in order to introduce luciferase mRNA ( mLuc ) into their bodies . Three hours after the second electroporation , the transformed schistosomules were harvested and luciferase enzyme activity in their tissues was investigated . As shown in Figure 1A , luciferase activity was strongly reduced in schistosomules electroporated with dsLuc following introduction of mLuc . By contrast , luciferase activity was readily apparent in control schistosomules transformed with mLuc only . Specifically , there was a significant reduction in the luciferase activity in all treated groups of >95% in one day old schistosomules ( p<0 . 05 ) and >85% in two day old schistosomules ( p<0 . 05 ) . The strong RNAi effect was still detectable in schistosomules treated 12 days after cercarial transformation . The period between the dsRNA transfection and the mRNA transfection ( one or two days ) showed small , non-significant variations in the levels of knockdown of luciferase activity . Next , we investigated the effect of the co-transfection by electroporation of one day old schistosomules with dsLuc and Luc mRNA simultaneously . In similar fashion , we observed robust silencing , specifically 82% reduction in the luciferase activity ( p<0 . 05 ) ( Figure 1B ) . Finally , we transformed fourteen day old schistosomules with dsLuc , and one day later transfected them by electroporation with a plasmid DNA construct encoding luciferase driven by the actin 1 . 1 gene promoter of S . mansoni ( pLuc ) ( from ref [14] ) . We selected to transform two week old schistosomules because it was reported that actin expression increased dramatically by that time [47] . Consistently when luciferase activity was measured two days after the last electroporation , the control group transformed with pLuc only displayed strong levels of activity , indicating that the plasmid was able to drive the in vivo expression of the transgene . However , the dsLuc treated group showed a reduction of more than 80% in luciferase activity ( p<0 . 05 ) . Taken together these data indicate that dsRNA is able to induce a strong silencing response of a reporter mRNA in schistosomules irrespectively of the exogenous ( electroporated ) or endogenous ( expressed from plasmid ) origin of the transcript . Furthermore , these results indicate that we have developed a quick and efficient system to detect an active RNAi pathway that can be applied to evaluate the viability of RNAi in other parasites . We anticipated that the test system described above , knockdown of exogenous firefly luciferase activity with specific dsRNA , would facilitate determination of the presence of a functional RNAi pathway in less well studied species , and we selected F . hepatica to test this hypothesis . As a first component in the development of a tractable system for RNAi in less well studied flukes , we tested electroporation since it is the most proficient method for delivery of dsRNA in schistosomules of S . mansoni [20] . One day old juveniles of F . hepatica were transformed with 50 ng/µl mLuc by square wave electroporation ( a single 20 millisecond pulse of 125 volts ) , and three hours later were snap frozen at −80°C for subsequent analysis of luciferase activity . Significant amounts of luciferase activity ( >300 , 000 RLUs/sec/mg ) were detected in homogenates of the transformed flukes while untreated worms expectedly show neglectable activity ( p<0 . 001 ) . ( see Figure 2B , day 1 ) . No obvious detrimental effects due to the electroporation were observed , and within few minutes of the electroporation the worms recovered normal vitality and motility . Since mRNAs usually exhibit short half lives in vivo , we also examined luciferase activity at various times post-electroporation in 2 days old treated juveniles , as an indirect measure of mLuc stability . As shown in Figure 2A , the luciferase levels peaked in our study time points at 3 h post electroporation ( p<0 . 05 ) , and had begun to decline by 12 h and 24 h . We also examined if there were variations in succeptibility to electroporation , by perfoming the procedure at different times after excystment of metacercariae . NEJs ( 1 hour old ) or cultured larvae of F . hepatica one , two , three and 30 days old were transformed with 50 ng/µl mLuc by square wave electroporation ( a single 20 millisecond pulse of 125 volts ) , and three hours later were snap frozen at −80°C for subsequent analysis of luciferase activity . Figure 2B presents the luciferase activity in tissues of the transformed flukes measured at three hours after electroporation . Optimal luciferase activity was seen in three-day-old juveniles ( >400 , 000 RLUs/sec/mg ) , with elevated levels also detected in one- and two-day-old juveniles ( p<0 . 01 ) . Much lower , though nonetheless substantial , levels of activity were detected in the one-hour-old NEJs ( ∼25 , 000 RLUs/sec/mg ) and the 30-day-old juveniles ( ∼18 , 000 RLUs/sec/mg ) . The elevated levels in the younger flukes compared to the 30-day-old juveniles may reflect developmental differences in the translation of the mLuc or , trivially , may reflect decreased vitality in the larval flukes as the consequence of extended culture in vitro . Long term culture of larval F . hepatica is known to be challenging [48] . Collectively , these findings demonstrated that square wave electroporation could deliver efficiently exogenous nucleic acids into the tissues of cultured juvenile F . hepatica . Given the apparent success with these electroporation settings , we employed similar conditions in the experiments described below - NEJs of F . hepatica , 50 ng/µl of RNA , a single square pulse of 20 msec at 125V , and analysis of firefly luciferase activity three hours post-electroporation . The intra-parasite localization of the transfected molecule was investigated by electroporating two days old NEJs with mLuc , and detecting the presence of the mRNA by whole mount ( in toto ) hybridization 3 hours later . The antisense labeled probe produce a strong signal in the digestive tract of the treated worms , while no signal was detected in mock electroporated control worms ( Figure 2C ) . To confirm these results we electroporated two days old NEJs with a labeled siRNA used as transfection control in siRNA experiments in mammals . Three hours after electroporation treated worms showed a pale fluorescent signal in their parenchyma and a clear uptake in the digestive ceca , while no labeling was detected in the control worms ( Figure S1 ) . The signal persisted after 24 hs in live worms as depicted in Figure 2D . These data suggest that the digestive tract of the worm might be the principal site of uptake of the transfected molecules , although other entry pathways cannot be ruled out . A similar picture was obtained in sever day old S . mansoni schistosomules [20] . However , in this study the fluorescence pattern seen in the day 7 cultured schistosomes was distinct from that seen following electroporation of fresh ( 0 day ) parasites . Similarly , other reports indicate that the main targets of electroporation in S . mansoni and S . japonicum are tegumental and subtegumental cells [12] , [13] . The differences within schistosomes have been related to different maturation status , since it has been reported that schistosomula mouth remains closed until around day 7 after transformation [20] . In F . hepatica , the metacercarial gut is filled with secretory vesicles that are released during excystement , and the digestive tract of the juvenile remains a major source of secretions [49] . Consequently the digestive tract of the juveniles remains open from excystment on , and therefore this could be the entry path of the electroporated molecules , as indicated by the present data . Moreover , this suggests that electroporation could be an appropriate delivery method to knock down specific genes expressed in digestive tract . Because we observed that F . hepatica NEJ and juveniles could be productively transformed with mRNA by square wave electroporation , and because reporter firefly luciferase was active in these flukes , we proceeded to attempt to silence the expression of the exogenous reporter . In particular , one-day-old juveniles were transformed by electroporation with either dsLuc or dsMalE , the latter representing an irrelevant control dsRNA . Twenty fours hours later , these transformed flukes were subjected to a second electroporation procedure involving transformation with mLuc . A third group of flukes was electroporated with mLuc only , i . e . treatment with dsRNA , while a fourth group was not treated with either dsRNA or mLuc . As shown in Figure 3A , at 3 h after electroporation of mLuc , luciferase activity of ∼35 , 000 RLU/sec/mg was recorded in flukes treated only with mLuc . As expected , negligible luciferase activity was detected in the tissues of control flukes not exposed to either dsRNA or mRNA . By contrast , the luciferase activity in the juvenile flukes exposed to both dsLuc and mLuc was almost completed ablated ( 2000 RLUs/sec/mg; >95% reduction , p<0 . 001 ) . Unexpectedly , the juvenile worms treated with the irrelevant MalE dsRNA showed an increase in luciferase activity . We found no reasonable explanation for this observation . The direct measurement of luciferase activity by luminometry provides a demonstration of the gene silencing at a protein level . In addition to this , we employed RT-PCR to investigate the effect at mRNA levels . The mLuc was present at similar levels in the no dsRNA and the irrelevant control ( dsMalE ) groups , but substantially reduced in the flukes treated with dsLuc , indicating that the effect was specific ( Figure 3B ) . The findings demonstrated that a viable and efficient RNAi pathway existed in juveniles of F . hepatica . In this regard , they confirm the findings of McGonigle et al . ( 2007 ) who recently reported RNAi knock-down of the papain-like cysteine proteases cathepsin L and cathepsin B in juvenile F . hepatica by soaking [35] . Our electroporation protocol produces a detectable effect at the RNA level using less dsRNA than the soaking protocol used by McGonigle et al . However , since neither of these studies analyzed the required amount of dsRNA required for getting a detectable effect , is still not possible to determine the more efficient delivery method . In schistosomes comparative studies indicate that electroporation outperforms soaking as a delivery method [20] , a fact that still need to be addressed in Fasciola and other species and models . Having demonstrated the existence of a viable RNAi pathway in juveniles of F . hepatica through the knockdown of the exogenous reporter , firefly luciferase , we proceeded to investigate RNAi in this species by targeting an endogenous F . hepatica gene , and we selected leucine aminopeptidase as an interesting enzyme [46] . We electroporated 12 day-old-juveniles with gene specific dsRNA of F . hepatica leucine aminopeptidase ( FhLap ) , and dsLuc as an irrelevant control . No phenotypic effects were observed by light microscopy for these flukes maintained for 48 hours after electroporation ( not shown ) at which time the tissues of the worms were harvested . We could not detect LAP enzymatic activity in somatic extracts of both treated and control worms . However , the activity of the highly expressed cathepsin Ls was detectable at very low levels in both samples , suggesting that we might be under the limit of detection for LAP ( data not shown ) . Total RNA was extracted and employed as template for semi-quantitative RT-PCR analysis . The primers utilized targeted a region discrete from the target RNAi locus in the FhLap cDNA ( Figure 4 , panel A ) . This strategy was employed to obviate residual dsRNAs serving as spurious template for the RT-PCRs [50] . Thus the dsRNA spans residues 389–871 of the cDNA while the RT-PCR targets residues 1010–1437 . As shown in Figure 4B , there was a potent knock-down of mRNA encoding FhLap in the worms treated with dsFhLap but not in those treated with dsLuc . Moreover , no differences between groups of parasites were detected for the amplification of the housekeeping GAPDH gene ( internal control ) or the cathepsins L3 and B2 , predominantly expressed in juveniles [51] . The reduction of the mRNA levels of FhLAP , and the absence of variations in three other F . hepatica genes indicated that the silencing was specific for the target FhLap gene , excluding off-targeting effects . These findings demonstrated RNAi silencing of an endogenous gene at mRNA level , following on from our initial demonstration of knock-down of the exogenous reporter gene encoding firefly luciferase at mRNA and also at protein level . Because we are interested to transfer reverse genetics tools developed for Schistosoma to less well studied flukes or other helminths , we designed a reporter system for RNAi using an exogenous gene , and tested it in the liver fluke Fasciola hepatica . We demonstrated both the applicability of the system and consequently confirmed the existence of a viable RNAi pathway in this parasite . This straight-forward reporter system could provide investigators with a tool to test the presence of a functional RNAi pathway in other parasites that are by other means intractable . It is noteworthy that despite the success of the RNAi in Caenorhabditis elegans , the technique has serious limitations in other nematodes , both free living and parasitic [52] . Viney and Thompson recently postulated that either the delivery methods applied in parasitic nematodes were inapropiate , or they are defective for genes required to initate RNAi from external dsRNA [53] . While soaking is the delivery method most frequent chosen when testing for the presence of RNAi , some genes relevant for the uptake and spreading of the silencing phenomena might be missing . In effect , in Caenorhabditis briggsae the absence of the SID-2 gene is responsible for the failure to induce RNAi externally ( by soaking or feeding ) while is effective when microinjected or electroporated [54] . Furthermore , the draft genome of the filarial nematode Brugia malayi reveals the absence of sid-1 and sid-2 genes , indicating that spreading of RNAi would not occur in this parasite [55] . The procedure here described could provide a rapid and inexpensive method to help decide if this is the case in other parasitic helminths . In conclusion , the present investigation achieved three goals . First , using S . mansoni as model , we developed an efficient and quick system to test the presence and viability of an intact RNAi pathway in parasites in which the RNAi has no been tested yet and/or the long term culture conditions are not yet established . Second , we introduced genetic material by electroporation into F . hepatica demonstrating the feasibility of this route of transformation of this trematode . Third , we demonstrated the existence of a viable and functional RNAi pathway in F . hepatica by knocking down a reporter gene , and an endogenous gene , establishing the starting point for functional genomic studies in Fasciola . We consider that these findings will not only enhance investigation of gene function in Fasciola , including investigation of novel intervention targets , but they may likewise provide a path forward for genetic manipulation of even less studied trematodes .
|
Reverse genetics tools allow assessing the function of unknown genes . Their application for the study of neglected infectious diseases could lead eventually to the identification of relevant gene products to be used in diagnosis , or as drug targets or immunization candidates . Being technically more simple and less demanding than other reverse genetics tools such as transgenesis or knockouts , the suppression of gene activity mediated by double-stranded RNA has emerged as a powerful tool for the analysis of gene function . RNAi appeared as an obvious alternative to apply in complex biological systems where information is still scarce , a situation common to several infectious and parasitic diseases . However , several technical or practical difficulties have hampered the development of this technique in parasites to the expectations originally generated . We developed a simple method to test the presence of a viable RNAi pathway by silencing an exogenous reporter gene . The method was tested in F . hepatica , describing the conditions for transfection and confirming the existence of a viable RNAi pathway in this parasite . The experimental design created can be useful as a first approach in organisms where genetic analysis is still unavailable , providing a tool to unravel gene function and probably advancing new candidates relevant in pathobiology , prevention or treatment .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results/Discussion"
] |
[
"genetics",
"and",
"genomics/gene",
"expression",
"genetics",
"and",
"genomics/functional",
"genomics",
"infectious",
"diseases/neglected",
"tropical",
"diseases",
"infectious",
"diseases/helminth",
"infections",
"genetics",
"and",
"genomics/gene",
"function",
"biochemistry/transcription",
"and",
"translation",
"cell",
"biology/gene",
"expression"
] |
2008
|
Development of Functional Genomic Tools in Trematodes: RNA Interference and Luciferase Reporter Gene Activity in Fasciola hepatica
|
Thailand is currently experiencing one of its worst dengue outbreaks in decades . As in most countries where this disease is endemic , dengue control in Thailand is largely reliant on the use of insecticides targeting both immature and adult stages of the Aedes mosquito , with the organophosphate insecticide , temephos , being the insecticide of choice for attacking the mosquito larvae . Resistance to temephos was first detected in Aedes aegypti larvae in Thailand approximately 25 years ago but the mechanism responsible for this resistance has not been determined . Bioassays on Ae . aegypti larvae from Thailand detected temephos resistance ratios ranging from 3 . 5 fold in Chiang Mai to nearly 10 fold in Nakhon Sawan ( NS ) province . Synergist and biochemical assays suggested a role for increased carboxylesterase ( CCE ) activities in conferring temephos resistance in the NS population and microarray analysis revealed that the CCE gene , CCEae3a , was upregulated more than 60 fold in the NS population compared to the susceptible population . Upregulation of CCEae3a was shown to be partially due to gene duplication . Another CCE gene , CCEae6a , was also highly regulated in both comparisons . Sequencing and in silico structure prediction of CCEae3a showed that several amino acid polymorphisms in the NS population may also play a role in the increased resistance phenotype . Carboxylesterases have previously been implicated in conferring temephos resistance in Ae aegypti but the specific member ( s ) of this family responsible for this phenotype have not been identified . The identification of a strong candidate is an important step in the development of new molecular diagnostic tools for management of temephos resistant populations and thus improved control of dengue .
Aedes aegypti is a major vector of dengue fever and yellow fever viruses . Despite an effective vaccine , there are over 200 , 000 cases of yellow fever each year ( WHO source , 2012 ) . With no vaccine currently available for dengue , and no specific drug treatment , approximately 40% of the world's population is at risk of dengue fever and there may be as many as 390 million dengue infections per year [1] . Dengue is endemic in Thailand with the most severe manifestation of dengue , dengue haemorrhagic fever first reported in 1958 [2] . The number of dengue cases has been steadily increasing since 2009 with over 81 , 000 cases already reported in the first 7 months of 2013 and , predictions of between 100 , 000 and 120 , 000 cases for the whole year ( Department of Disease Control , Thailand Ministry of Public Health , http://www . ddc . moph . go . th/ ) . Maintaining Ae . aegypti populations at low levels is crucial for dengue control in Thailand [3] . Environmental management including educational campaigns to remove unnecessary sources of standing water , coupled with covering of permanent water storage vesicles , is recommended to help reduce Aedes populations [4] but this is supplemented by the use of chemical insecticides . In Thailand , adult mosquitoes are predominately targeted with pyrethroid insecticides [5] , mainly through the distribution of pyrethroid impregnated materials and the Ultra-Low-Volume ( ULV ) applications of pyrethroids [6] . Larval control primarily utilises the organophosphate insecticide , temephos , ( Department of Disease Control , Thailand Ministry of Public Health ) despite the known existence of temephos resistant populations of Ae . aegypti in many regions of Thailand [7] , [8] . An understanding of insecticide resistance mechanisms is important for the development of tools and practices that can improve resistance management and thereby the sustainability of control interventions . In many insect species , organophosphate and carbamate resistance is caused by amino acid substitutions in the target site , acetylcholinesterase ( ace-1 ) , which reduces the sensitivity of this enzyme to the insecticide . The most common ace-1 substitution in mosquitoes occurs at amino acid residue 119 where the wild type glycine is substituted to serine [9] . However , in Ae aegypti , the codon usage at Glycine 119 makes this substitution very unlikely to occur [10] . Indeed , despite numerous reports of temephos resistance in Ae aegypti populations across the tropics , including at least one report of insensitive AchE [11] , no target site mutations linked to organophosphate resistance have been detected to date . Organophosphate resistance can also be caused by elevated levels of esterase enzymes that can both act to sequester the insecticide , reducing the amount of active insecticide that reaches the target site [11] , or to increase the rate of turnover of insecticide , by amino acid substitutions in the coding sequences of one or more esterases [12] . Elevated CCE activity has been associated with temephos resistance in several populations of Ae aegypti [13] , [14] , [15] , [16] , [17] . A small number of studies [16] , [17] , [18] have used microarray based approaches to detect genes associated with the resistance phenotype . Although several transcripts of detoxification genes were found to be evelated in temephos resistant populations ( including CCEae3a , CYP6Z8 and CYP9M9 ) , a single clear candidate did not emerge from these studies . The current study provides evidence for elevated CCE activity in a temephos resistant population from Thailand and identifies a clear candidate gene that shows both elevated expression and amino acid polymorphisms in temephos resistant populations . Additional genes , potentially involved in temephos and/or permethrin resistance in Ae aegypti larvae are identified and discussed .
Mosquito eggs were collected from four sites of Thailand including Chiang Mai ( North , 18°47′25″N 98°59′4″E , 31st October 2011 ) , Nakhon Sawan ( central , site 1 : 15°20′45″N 100°29′41″E , 5th March 2012 , site 2: 15°52′52″N 100°18′9″E , 27th March 2012 ) and Phatthalung ( south , 7°37′6″N 100°4′24″E , 2th September 2011 ) ( Figure S1 ) . They were chosen based on previous reports of temephos resistance in these districts [8] , [19] , [20] . Aedes aegypti eggs from Phatthalung and Chiang Mai were collected using modified ovitraps by entomologists from the Department of Disease Control ( Ministry of Public Health , Thailand ) . Eggs from Nakhon Sawan sites were collected by entomologists from office of Disease Prevention & Control 8 ( DPC8 , Nakhon Sawan ) . The modified ovitraps consisted of a dark plastic cup with a piece of filter paper over the inner part of the cup and filled with tap water . They were placed in the resting sites of Ae aegypti such as under sinks , beds , cupboards or any cool , humid and dark areas in and around the house . Eggs were then sent to the Liverpool School of Tropical Medicine ( LSTM ) where they were hatched in distilled water and reared in standard insectary conditions ( temperature: 28+/−1°C; relative humidity: 75+/−5%; photoperiod: 12 hours day/night ) . An insecticide susceptible laboratory colony , New Orleans ( NO ) strain was used as control in the study . This population was originally collected in the namesake city located in Louisiana , United States . Standard WHO larval bioassays were conducted to detect the level of susceptibility to temephos [21] . Bioassays were done on late 3rd/early 4th instar larvae using a range of seven temephos ( Pestanal , analytic standard , diluted in ethanol ) concentrations . Concentrations of insecticides were chosen in order to cover larval mortality range ( 0–100% ) . Three replicates of 20 larvae were used for each concentration and 1 ml ethanol was added in control cups . Mortality was recorded after 24 hours of exposure . Larval bioassays using permethrin were also performed to look for any evidence of cross resistance between insecticide classes . Synergist bioassays were performed on the populations showing the highest temephos resistance levels using a cytochrome P450 inhibitor , piperonylbutoxide ( PBO ) at 0 . 3 ppm ( piperonylbutoxide 90% , Sigma Aldrich , Inc . , Italy ) , a glutathione S-transferase inhibitor , diethyl maleate ( DEM ) at 1 ppm ( diethyl maleate >97 . 0% ( GC ) , Sigma Aldrich Chemie GmbH , Austria ) and a carboxylesterase inhibitor , S , S , S-tributylphosphorotrithioate ( DEF ) at 0 . 5 ppm ( S . S . S-tributylphosphorotrithioate 98 . 1% , Chem service , Inc . , USA ) . Inhibitors were mixed with insecticide dilutions in ethanol and 1 ml of the mixture was added to 99 ml of water according to the protocol of [22] . Different concentrations of synergists were previously tested in order to establish appropriate sub-lethal concentrations [22] . PBO was also used as a synergist in permethrin bioassays . To determine the LC50s and confidence intervals data were analyzed using a Probit model on R software [23] . Activity levels of α esterases and β esterases were measured in the Nakhon Sawan 2 population ( NS2 ) , which showed the highest resistance ratio to temephos , and in Phatthalung , the population most susceptible to temephos and permethrin . Procedures were based on mosquito-specific biochemical assay protocols [24] , [25] , [26] . Briefly , 15 larvae from NS2 and Phatthalung were individually homogenized in 3 mL of 0 . 01 M potassium phosphate buffer ( KPO4 ) , ph 7 . 2 and 100 µl of each sample homogenate were then transferred by triplicate to a 96-well microtiter plate . Then , 100 µl of α/β naphthyl acetate ( 3 mM ) were added to each well , followed by 15 minute incubation at room temperature . Finally , 100 µl of dianizidine ( 4 mM ) were added , followed by 4 minute incubation , and then absorbance was read at a wavelength of 540 nm . Absorbance values where normalized by measuring protein content using a Bradford assay according to manufacturer's protocol ( Sigma , St Louis , MO ) . Data significance was compared using a Mann-Whitney test ( N = 15 ) . The most resistant population Nakhon Sawan 2 was chosen for the microarray experiment . Phatthalung was used as susceptible population because of its geographical proximity ( Figure S1 ) . Three groups of early 4th instar larvae ( 15 larvae each ) were used for total RNA extractions: Phatthalung ( P ) , Nakhon Sawan 2 unexposed ( NS 2 Unexp ) and Nakhon Sawan 2 larvae ( NS 2 Exp ) which survived a temephos bioassay inducing 60% mortality ( 24 hour exposure to 0 . 032 ppm temephos ) . Surviving larvae were left to recover in clean water for 24 hours after exposure to reduce the impact of short term gene induction on the transcriptomic profile . The Arcturus Picopure RNA Extraction Kit ( Arcturus , California , USA ) was used according to the manufacturer's protocol and 100 ng total RNA per biological replicate were amplified and labelled with Cy-5 and Cy-3 dyes with the ‘Two colors low input Quick Amp labeling kit’ ( Agilent technologies , Santa Clara , CA , USA ) according to manufacturer's instructions . Labelled cRNA were purified with the Qiagen RNeasy kit ( Qiagen , Hilden , Germany ) . Quantification and quality assessment of labeled cRNA were performed with the Nanodrop ND-1000 ( Thermo Scientific , DE , USA ) and the Agilent 2100 Bioanalyser ( Agilent Technologies ) . Microarray hybridizations were performed with the 15 k Agilent “Aedes microarray” ( ArrayExpress accession number A-MEXP-1966 ) , containing eight replicated arrays of 60-mers oligo-probes representing 14 , 204 different Ae . aegypti transcripts from AaegL1 . 2 Vectorbase annotation and several control probes . For each comparison , five hybridizations were performed including two dye-swaps in which the Cy3 and Cy5 labels were swapped between samples . After 17 h hybridization , non-specific probes were washed off with the Agilent microarray hybridization kit according to manufacturer's instructions . Slides were scanned immediately with an Agilent G2205B microarray scanner . Spot finding and signal quantification for both dye channels were performed using the Agilent Feature Extraction software ( Agilent Technologies ) . Data were then loaded into Genespring GX ( Agilent Technologies ) for normalization and statistical analyses . For each population comparison , only transcripts flagged ‘present or marginal’ in four of five hybridizations were used for further statistical analysis . Mean transcription ratios were then submitted to a one sample Student's t-test ( N = 3 ) against the baseline value of 1 ( equal transcription level in both populations ) with Benjamini and Hochberg's multiple testing correction . For each selected population , transcripts showing a >2 fold change in either direction and a t-test P-value lower than P<0 . 01 after multiple testing correction were considered significantly differentially transcribed compared to the susceptible population . Descriptions and GO-terms of transcript-IDs were extracted from VectorBase ( www . vectorbase . org ) using BIOMART and completed with Blast2GO software ( BioBam Bioinformatics S . L . ( Valencia , Spain ) ) . GO term Enrichment analysis was performed on the significant up-regulated genes found in both comparisons “NS2 exp vs P” and “NS2 Unexp vs P” using Blast2GO software and Fisher's exact test with FDR<0 . 05 according to [27] . All microarray data were uploaded to Arrayexpress ( E-MTAB-1934 , www . ebi . ac . uk/arrayexpress/ ) . Transcription levels of six genes ( four P450s , one CCE and one ABC transporter ) found significantly differentially transcribed in at least two comparisons were validated by reverse transcription followed by real-time quantitative PCR ( RT-qPCR ) as described in [28] . As a secondary control , the susceptible New-Orleans ( NO ) population was included . Two micrograms of total RNA per biological replicate were treated with DNAse I ( Invitrogen , Carlsbad , CA , USA ) and used for cDNA synthesis with superscript III and Oligo-dT20 primer ( Invitrogen ) according to manufacturer's instructions and resulting cDNAs were diluted 50 fold . Real time quantitative PCR reactions of 25 µL were performed on a MX3005P qPCR machine ( Agilent technologies , CA , USA ) using Brilliant III ultrafast SYBR green mastermix ( Agilent technologies , CA , USA ) , 0 . 3 mM of each primer and 5 µL of diluted cDNAs . A melt curve analysis was performed to check for the unique presence of the targeted PCR product . Quantification of transcription level was performed according to the ΔΔCt method taking into account PCR efficiency [29] and using two housekeeping genes for normalization: the ribosomal proteins L8 ( AAEL000987 ) and S7 ( AAEL009496 ) . Results were expressed as mean transcription ratio ( ±95% confidence intervals ) between Nakhon Sawan 2 and the susceptible populations New Orleans and Phatthalung . All primer sequences are included in supplementary table S5 . Three different groups of 4th instar larvae were used: P , NS2 unexposed and NS2 exposed mosquitoes . NS2 exposed mosquitoes were survivors of a temephos bioassay inducing more than 80% mortality after 24 hours . Genomic DNAs were extracted from 8 individual larvae per group using DNeasy Blood and Tissue Kit according to manufacturer's instructions ( Qiagen , Hilden , Germany ) and were treated with RNAse A ( Qiagen , Hilden , Germany ) to remove any RNA contaminants . DNA quantities were assessed on a Nanodrop ND-1000 spectrophotometer . Quantitative PCR reactions were performed as described above on CCEae3a gene ( same primers used above ) with AAEL000987 ( RPL8 ) and AAEL012167 ( Elongation factor ) ( see table S5 for primer sequences ) as housekeeping genes . The relative copy number fold-change was calculated using the 2−ΔΔCt method . To identify any amino acid polymorphisms that might be associated with temephos resistance , sequencing of CCEae3a cDNA sequence was performed on Nakhon Sawan 2 larvae which survived a concentration of temephos inducing 90% mortality and on unexposed Phatthalung larvae . Total RNAs from 10 individual larvae were extracted using Trizol according to the manufacturer's instructions ( Invitrogen , Carlsbad , USA ) and total RNA quantities were assessed using a Nanodrop ND-1000 ( Thermo Scientific ) . Genomic DNA contaminants were then digested using DNase I ( Invitrogen ) and total RNAs were reverse transcribed according to the same protocol used for qPCR validation . Primers were designed ( Table S1 ) to amplify the whole CCEae3a sequence available on Vectorbase ( AAEL005112-RA , www . vectorbase . org ) . PCR amplification was carried using Phusion High-Fidelity DNA Polymerase ( Thermo Scientific ) using the following conditions: Initial denaturation at 98°C for 30 seconds followed by 35 cycles of 10 sec denaturing at 98°C , 20 sec annealing at 66°C and one minute extension at 72°C . Last extension step 72°C last during 10 min . PCR products were visualized on a 1% agarose gel and purified using a GeneJET Gel Extraction Kit ( Fermentas , Vilnius , Lithuania ) . The PCR products were cloned into DH5 competent cells using pJET 1 . 2/blunt Cloning Vector kit ( Fermentas , Vilnius , Lithuania ) . Plasmids were extracted using GeneJET Plasmid Miniprep Kit , ( Fermentas ) and sequenced ( Macrogen , Amsterdam , the Netherlands ) using pJET primers and two internal primers ( Table S5 ) . The secondary structure and three-dimensional structure of the different polymorphic variants of CCEae3a were predicted by the Protein Homology/analogY Recognition Engine ( PHYRE2 ) ( Structural Bioinformatics Group , Imperial College , London ) . This method uses structural alignments of homologous proteins of similar three-dimensional structure in the structural classification of protein databases to obtain a structural equivalence of residues . The top 20 highest scoring matches of the query to known template structures are used to construct 3D model of the query .
The Phatthalung ( P ) populations showed the lowest LC50 to temephos and resistance ratios were calculated compared to this population , and according to the standard laboratory susceptible New Orleans ( NO ) . NS 2 showed the highest resistance to temephos ( RR at LC50 = 5 . 9 –9 . 85 fold ) followed by NS 1 ( RR at LC50 = 3 . 3–5 . 5 fold ) and CM ( RR at LC50 = 2 . 1–3 . 5 fold ) ( Table 1 ) . Larval bioassays using permethrin showed much higher LC50s in both NS1 and NS2 populations compared to P ( RR at LC50 = 29 . 1 and 31 fold respectively ) and intermediate LC50 in the CM population ( RR = 8 . 2 fold ) ( Table 1 ) . Although permethrin larval bioassays were not performed on a standard lab susceptible strain in this study , two previous studies have reported lab susceptible LC50 for permethrin as approximately 0 . 0007 ppm [8] , [30] which is similar to the 0 . 0005 value obtained for the P population in the current study . Synergist bioassays were performed on both NS 1 and NS 2 populations . The use of temephos + PBO or DEM had no significant effect on NS 1 and NS 2 compared to temephos treatment alone . However , the DEF treatment significantly improved the toxicity of temephos by 3 . 14 fold in NS 1 and 2 . 48 fold in NS 2 compared to temephos alone . Finally the use of PBO+permethrin in combination showed an improved efficacy by more than two fold in NS 2 larvae compared to permethrin alone . Comparison of constitutive detoxification enzyme activities between the susceptible population Phatthalung and the most insecticide-resistant population NS 2 revealed increased α- and β-carboxylesterase activities in NS 2 compared to P ( 2 . 9 fold and 3 . 8 fold with P<0 . 05 ) ( Figure 1 ) . By using a microarray approach , we detected 2484 transcripts significantly differentially regulated between NS2 Exp and Phatthalung , 2508 between NS2 Unexp and P and 0 between NS2 Exp and NS2 Unexp ( RC ) ( Absolute change >2 fold , corrected P-value<0 . 01 ) . Validation of microarray data on six selected genes by RT-qPCR revealed an acceptable correlation between transcription patterns obtained by the two techniques ( mean R2 = 0 . 92 ) except for CYP6Z9 for which transcription pattern among comparisons was not confirmed ( Table S1 ) . Between the comparisons “NS2 Exp vs P” and “NS2 Unexp vs P” , 2088 transcripts were commonly found differentially regulated , including 962 up- and 1126 down-regulated transcripts ( Figure 2 ) . Among these up-regulated transcripts , GO term Enrichment analysis revealed 8 GO terms over represented compared to the whole microarray ( FDR<0 . 05 ) , all linked with P450 activities ( Figure 3a ) . Within the 962 up regulated transcripts found in both comparisons ( Table S2 ) , 42 CYPs were detected , 18 of which belong to the CYP9J family ( Table S3 ) . Larvae from NS2 are resistant to both temephos and permethrin . In an attempt to prioritise genes putatively involved in temephos resistance we applied an additional layer of filtering to derive our candidate gene list . We specifically looked for genes whose fold change compared to the susceptible P population were higher in the NS2 surviving temephos exposure than in the unexposed NS2 vs P comparison . By using an arbitrary ratio threshold of 1 . 25 , the candidate list was reduced to 122 transcripts ( Table S4 ) . This threshold was chosen in order to be within the range of differential detection of the microarray technology ( in line with recommendations from Agilent Techonologies ) . These candidates are highlighted in the volcano plot ( Figure 4 ) which also shows all transcripts significantly upregulated in Nakhon Sawan Unexp compared to Phatthalung . Interestingly , among the most overtranscribed genes figured one carboxylesterase CCEae3a ( AAEL005112 ) which was overtranscribed around 60 fold in Nakhon Sawan Unexp compared to Phatthalung and 91 fold in Nakhon Sawan Exp compared to Phatthalung ( ratio RS/RC = 1 . 33 ) . Two other esterases were also found more upregulated in RS comparison compared to CS: CCEae6A ( AAEL015264-RA ) ( 29 fold upregulated in RS , 22 fold in CS ) and CCEglt1K AAEL006097-RA ( 4 . 2 fold in RS , 2 . 5 fold in CS ) . Four cytochrome P450s were also present in the candidate genes list: CYP6Z8 ( AAEL009131-RA ) , CYP9M9 ( AAEL001807-RA ) , CYP6AH1 ( AAEL007473-RA ) and CYP4H28 ( AAEL003380-RA ) . Multiple transcripts coding for cuticular proteins were also found significantly overtranscribed among the 122 transcripts , including 6 paralogous genes belonging to the CPLC group . Quantitative PCR showed a significantly higher CCEae3a gene copy number in NS 2 unexposed ( >165 fold , Pval<0 . 01 ) and NS2 Exposed ( >350 fold , Pval<0 . 01 ) compared to Phatthalung strain ( Figure S2 ) . Sequencing of the cDNA sequence of CCEae3a ( AAEL005112-RA ) revealed the presence of non synonymous mutations between the sequences from Vectorbase , Phatthalung and the resistant population NS2 . The derived amino acid sequence of NS2 had amino acid substitutions AAT positions 373 ( GAA to GAC , leading to the change of an aspartic acid to glutamic acid ) , 374 ( AAT to GAT , asparagine to glutamic acid ) , 538 ( CGA to CAA , arginine to glutamine ) and 541 ( GAA to GAC , glutamic acid to aspartic acid ) compared to Vectorbase and Phatthalung sequences ( Figure 5 ) . The models for Nakhon Sawan , Phatthalung , Vectorbase and mutated Vectorbase ( Vectorbase sequence with the NS mutations at the positions 373 , 374 , 538 and 541 ) sequences were generated using PHYRE2 web server in the intensive mode . For all of them , 99% of the residues were modelled at more than 90% confidence in the final model and the best ranked match was the carboxylesterase αE7 from the Australian sheep blowfly Lucilia cuprina ( LcαE7 ) with 34% identity . The in silico models enabled the polymorphic residues of the analysed variants ( E373D , N374D , R538Q and E541D ) to be localised and to identify those residues involved in the active site by homology with LcαE7 . The most interesting difference between resistant and susceptible forms was found more than 20 Å away from the polymorphic residues and involved residues that belong to the putative substrate-binding site ( Y283-G293 ) ( Figure 6 ) .
Previous studies have reported temephos resistant populations of Ae aegypti from Thailand [8] , [19] . The objective of the current work was to identify the mechanism ( s ) responsible for this resistance . Bioassays were conducted on four populations of Thai mosquitoes and a susceptible laboratory population . Larvae from the P population from the southern Phatthalung province were fully susceptible to temephos with a lower LC50 than the New Orleans laboratory strain . Full susceptibility to temephos was also reported in the neighbouring province of Songkhla in 2005 [19] . Two other populations , NS1 and CM , showed low levels of temephos resistance ( according to classifications in [31] ) and one population , NS2 , from central Thailand , showed medium levels of resistance , with RR from 6–10 fold . An earlier study also found the highest levels of temephos resistance in the Nakhon Sawan province [19] and the RRs obtained in the current study are similar to those reported from this province in a 2005 study , despite the use of different lab susceptible populations [8] . Although the current study did not directly assess the impact of the observed resistance on the field efficacy of temephos , earlier studies in Brazil clearly demonstrated an impact of resistance levels of similar magnitudes to the NS2 population on the duration of temephos efficacy in simulated field assays [32] . Hence it is likely that temephos resistance is compromising dengue control in central Thailand but , as noted by others [19] , insecticide resistance in Ae aegypti appears to be very focal ( note the marked differences in the Temephos LC50 between NS1 and NS2 , separated by a distance of 60 Kms ) . Permethrin resistance was also detected in Ae aegypti larvae from Chiang Mai and from both populations from Nakhon Sawan province . Again this agrees with earlier bioassays data from Thailand [8] . Pyrethroids are not directly applied as larvicides in Thailand but contamination of breeding sites may occur by the use of pyrethroids as aerial sprays to control dengue epidemics . Alternatively , the co-occurrence of both temephos and permethrin resistance in the same population may be caused by cross-resistance as was proposed following a temephos selection experiment in Cuba [33] . Possible mechanisms for this putative cross resistance are discussed below . The data from enzyme inhibitors suggests that temephos resistance in the Nakhon Sawan province is linked to carboxylesterase activities . Conversely , the cytochrome P450 inhibitor , PBO , had the biggest impact on permethrin resistance in the NS2 population . However , even after addition of PBO , NS2 remained moderately resistant to permethrin suggesting that pyrethroid target site resistance may be present in the population: two sodium channel mutations associated with permethrin resistance , V1016G and F1534C are known to be widespread in Thailand [34] , [35] , [36] , [37] . Further support for a key role for carboxylesterases in conferring temephos resistance is provided by biochemical assays using alpha- and beta-naphtylacetate as substrates . Significantly higher levels of esterase activity were detected in the NS2 population compared to the susceptible population from Southern Thailand ( P ) . Again , this mimics findings from other temephos resistant populations [14] , [15] . Although both changes in gene expression and allelic variation in individual CCE proteins has been associated with organophosphate resistance [38] , [39] the latter is typically associated with a decrease in esterase activity , as measured with general esterase substrates [40] , [41] , [42] . We therefore hypothesised that one or more up-regulated carboxylesterase genes were responsible for the temephos resistance and thus used a microarray platform to identify transcripts that were upregulated in the resistant NS2 population compared to the susceptible Phatthalung population . Phatthalung was used as a susceptible population , as opposed to a standard laboratory susceptible population , in an attempt to reduce the impact of extended laboratory colonisation and geographical differences on the transciptome data . It was therefore surprising to find over 2000 transcripts significantly differentially transcribed between the two Thai populations . In a three way comparison we compared both NS2 unexposed to insecticides and a subset of NS2 population that had survived temephos exposure and been sacrificed 24 hours after insecticide exposure with the Thai susceptible population . We did not observe any significant differences between the NS2 exposed and unexposed populations but we used these three data sets to filter our candidate list in two steps . Firstly we discarded genes that were only upregulated in the NS2 population in one of the comparisons ( Figure 2 ) focusing initially on the subset of 962 transcripts that were commonly upregulated in the NS2 exposed vs P and the NS2 unexposed vs P . Interestingly , this subset of transcripts contained a large number of cytochrome P450 genes . This was confirmed by the enrichment analysis which showed a clear enrichment of GO terms linked with P450 activities in the overtranscribed genes compared to the whole microarray . Over half of the upregulated P450s belonged to the CYP9J family ( Table S3 ) . CYP9Js have been widely implicated in pyrethroid resistance in Ae aegypti populations across the globe [27] , [43] , [44] , and several of these have been biochemically characterized and been shown to metabolize pyrethroids [45] . Further confirmation of the role of this P450 family in pyrethroid resistance comes from transgenic expression of CYP9J28 in Drosophila melanogaster which conferred an elevated level of resistance to pyrethroids [46] . To further refine our list of candidate genes responsible for temephos resistance , we hypothesised that genes putatively conferring this phenotype would exhibit a higher fold change differential in transcript levels in the NS2 exposed versus susceptible comparison than the NS2 unexposed vs susceptible . We therefore reduced our candidate list from 962 to 122 transcripts by dividing the fold changes in “NS2 Exp vs P” comparison by fold changes in “NS2 Unexp vs P” comparison and using an arbitrary cut off of >1 . 25 . Only four cytochrome P450s remained in this refined candidate list ( CYP6Z8 , CYP9M9 , CYP6AH1 , CYP4H28 ) , none of which belonged to the CYP9J family , perhaps indicating that the over expression of the CYP9J genes in NS2 contributes to the permethrin resistance phenotype but has a negligible role in conferring temephos resistance . CYP6Z8 has recently been shown to metabolize the 3-phenoxybenzoic alcohol ( PBAlc ) and 3-phenoxybenzaldehyde ( PBAld ) , common metabolites produced by carboxylesterases [47] , and it is possible that elevated levels of this enzyme is an important secondary resistance mechanism . Three carboxylesterase genes were present within final candidate list . One of these ( AAEL006097-RA ) encodes a putative glutactin which , although potentially catalytically active as it contains the catalytic triad and oxyanion hole , is not thought to be involved in xenobiotic detoxification . The two remaining carboxylesterases ( CCEae3a ( AAEL005112 ) and CCEae6A ( AAEL015264 ) ) belong to the alpha esterase clade , a group typically associated with dietary or xenobiotic detoxification functions . CCEae3a was overtranscribed more than 90 fold in NS2 exposed compared to Phatthalung and more than 60 fold in NS2 unexposed compared to P . To verify that this did not simply reflect an exceptionally low level expression in the southern Thai population , we also included the lab susceptible New Orleans in the qPCR . There was no significant difference in the expression of CCEae3A in the two susceptible populations ( Table S1 ) . CCEae6A was also highly over expressed in NS2 compared to the P population ( 29 fold in exposed , 22 fold in unexposed ) . Of these two alpha esterases , CCEae3a appears a particularly strong candidate for temephos resistance , as this gene is known to be overexpressed in temephos resistant populations from Martinique [16] , [48] and Brazil [17] . Interestingly , the copy number of CCEae3a was much higher in the NS2 resistant strain than the susceptible P strain , and also elevated in the subset of the NS2 strain surviving temephos exposure compared to the general NS2 population . This suggests that the overtranscription of CCEae3a may at least be partly due to gene amplification , similar to the mechanism observed in Culex pipiens [11] . In Martinique Island , both CYP6Z8 and CCEae3a were found upregulated together in pyrethroid and organophosphate resistant populations of Aedes aegypti [16] , [48] supporting the possible coordinated role of CYP6Z8 and CCEae3a in insecticide detoxification [47] . In addition to the over expression of CCEae3a cDNA sequence , several non-synonymous mutations were found between the sequences from Phatthalung compared to Nakon Sawan 2 . In silico structure predictions of CCEae3a , based on the carboxylesterase αE7 from the Australian sheep blowfly Lucilia cuprina ( LcαE7 ) [49] predicted that the polymorphic residues were not adjacent to the insecticide binding site . Nevertheless , the resistant variants lacked the hairpin loop between Y283 and G293 which was found in the susceptible population . It is possible that this loop displaces the F286 residue ( homolog to F309 in LcαE7 ) that seems to be essential in stabilizing OPs in the LcαE7 active site . Further work is needed however to determine whether the allelic variants differ in their enzymatic activity and if either or both forms are capable of sequestering and/or metabolising temephos . Temephos is one of the key insecticides for dengue control across the tropics but operationally significant levels of resistance are being increasingly reported [18] . Carboxylesterases have long been suspected to play a key role in mediating this resistance but to date no clear candidates had been identified . The identification of strong candidate genes has now laid the foundations for the development of molecular diagnostics to assess the correlation between the overexpression of these genes and temephos resistance across the distribution of Ae aegypti .
|
Temephos is the most important insecticide used in larviciding campaigns to reduce the risk of dengue transmission . This organophosphate insecticide has been in use for over 50 years and resistance to this chemical has been reported in Aedes aegypti populations from Latin America , the Caribbean and from Asia . In other insect species , organophosphate resistance is typically associated with mutations in the target site , acetylcholinesterase , that decrease the insect's sensitivity to the insecticide , or increases in the activity of one or more carboxylesterase enzymes , either by overproduction and/or amino acid substitutions , that reduce the amount of insecticide reaching the target site . Neither of these mechanisms has been previously characterised at the molecular level in dengue vectors . Here we identify an Ae aegypti carboxylesterase gene with expression levels and amino acid sequence polymorphisms correlating with temephos resistance in Thailand . This is a key step in the development of tools to manage resistance in this mosquito species .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"genome",
"expression",
"analysis",
"genetic",
"mutation",
"gene",
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2014
|
Identification of Carboxylesterase Genes Implicated in Temephos Resistance in the Dengue Vector Aedes aegypti
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Horizontal gene transfer ( HGT ) plays a major role in bacterial microevolution as evident from the rapid emergence and spread of antimicrobial drug resistance . Few studies have however addressed the population dynamics of newly imported genetic elements after HGT . Here , we show that newly acquired class-1 integrons from Salmonella enterica serovar Typhimurium and Acinetobacter baumannii , free of associated transposable elements , strongly reduce host fitness in Acinetobacter baylyi . Insertional inactivation of the integron intI1 restored fitness , demonstrating that the observed fitness costs were due to the presence of an active integrase . The biological cost of harboring class-1 integrons was rapidly reduced during serial transfers due to intI1 frameshift mutations leading to inactivated integrases . We use a mathematical model to explore the conditions where integrons with functional integrases are maintained and conclude that environmental fluctuations and episodic selection is necessary for the maintenance of functional integrases . Taken together , the presented data suggest a trade-off between the ability to capture gene cassettes and long-term stability of integrons and provide an explanation for the frequent observation of inactive integron-integrases in bacterial populations .
Horizontal gene transfer ( HGT ) enables bacteria to obtain alien genes and genetic elements from prokaryotic , archaeal , and eukaryotic organisms . This capacity for genetic exchange plays an important role in bacterial adaptive evolution , as exemplified by the rapid spread of antibiotic resistance determinants by HGT [1] , [2] . Most often , the fitness effects of novel genes in new hosts are selectively neutral or detrimental [3] , and prolonged persistence in the population requires compensatory evolution or associated linked selection [4] , [5] , [6] , [7] . Antibiotic resistance determinants are frequently associated with mobile and mobilizable genetic elements , and they tend to reduce host fitness when newly acquired as part of mobile DNA [4] , [5] , [8] , [9] . The magnitude of these fitness costs as well as the mode and speed of compensatory evolution are key parameters determining the frequency of resistance in bacterial populations following relaxed antibiotic selection ( i . e . following interventions on drug prescription levels ) [10] . From the perspective of horizontal dissemination of antibiotic resistance determinants , population dynamic studies are important to increase our insight on the evolution and reversibility of resistance [10] , [11] . Several studies have described compensatory evolution and host adaptation to self-replicating plasmids [for a selection see [4] , [5] , [8] , [9]] . However , only few studies have considered how bacteria adapt to the presence of chromosomally transferred genes and genetic elements . These studies have been limited to chromosomal allelic replacements [6] , [12] , transposons [13] , [14] and a report on conjugative transposons [15] . Integrons are a class of genetic elements frequently involved in antimicrobial resistance dissemination where population dynamic studies are currently absent . These genetic elements have the ability to capture and excise functional gene cassettes involved in host adaptation , often including antibiotic resistance traits [16] . Typically , an integron consists of an integrase gene ( intI ) encoding a site-specific recombinase responsible for the recruitment and excision of gene cassettes and a promoter ( PC ) for the expression of captured gene cassettes . Integrases capture gene cassettes through recombination between attI ( located downstream of PC ) and the gene cassette-borne recombination site attC present in a circular gene cassette . Inverse correlations exist between gene-cassette promoter ( PC ) strength and integrase activity [17] , [18] as well as expression levels [19] . Based on sequence similarity of the intI gene , five classes of “mobile integrons” have been described , for a review see [20] . Class-1 integrons are prevalent in Gram-negative clinical isolates , and harbor gene cassettes encoding resistance to the majority of clinically relevant antibiotics such as aminoglycosides , trimethoprim , and broad-spectrum β-lactams [20] , [21] . Structurally , class-1 integrons are relatively diverse , but they generally consist of a 5′-conserved segment ( 5′-CS ) including intI1 , attI1 , the variable regions where the gene cassettes are embedded , and a 3′-CS that includes a truncated qacE1 and sul1 [22] . Class-1 integrons are frequently linked to complete and incomplete transposons such as Tn402 [23] , and Tn21-like structures [24] . Due to the often incomplete nature of the transposable elements linked to clinical class-1 integrons these structures are generally thought to be defective in terms of transposition , and for these elements to move , transposition functions need to be provided in trans . However , in clinical isolates , these integron-containing transposons are frequently located on plasmids and thus can easily spread horizontally [25] , [26] . Integrons can be important factors for horizontal dissemination of novel and adaptive traits among bacteria because they facilitate “sampling” of the environmental gene-cassette-pool [27] , [28] . Moreover the ability to acquire novel cassettes , or shuffle the existing ones , has shown to be increased as a response to stress [29] . Integrons with non-functional integrases are however prevalent in bacterial populations [28] , [30] , suggesting that the ability to acquire gene cassettes does not necessarily provide a frequent selective advantage . Thus , whereas it is clear that selection for integron-encoded traits such as antibiotic resistance determine the frequency of class-1 integrons in bacterial populations , the selection for functional integrases remains unclear . Here we show that horizontally transferred class-1 integrons from Salmonella enterica serovar Typhimurium and Acinetobacter baumannii , free of associated transposable elements , strongly reduce host fitness in Acinetobacter baylyi . We demonstrate that these fitness costs are due to an active integrase IntI1 . These fitness costs were reduced during serial transfer experiments through mutational inactivation of the integrase gene , suggesting a trade-off between maintaining a functional integrase and stability of integrons in the population over time . Our results provide a rationale for why inactivated integron-integrases are frequently observed in clinical and environmental bacterial isolates . We use a mathematical model to explore the population dynamics of integrons with functional and non-functional integrases in competition with integron-free bacterial populations . We conclude that selection for pre-existing gene-cassettes acts synergistically with the ability to capture new ones [episodic selection [31]] in fluctuating environments .
The model organism A . baylyi ADP1 is a close relative to the nosocomial pathogen A . baumannii and is free of integrons [32] . We constructed a set of A . baylyi strains containing cloned diverse class-1 integrons from isolates of two A . baumannii ( clinical isolates ) and one S . enterica serovar Typhimurium ( isolated from pork ) . These strains allowed the investigation of the effects of newly acquired integrons on host fitness . The three integrons were inserted in an identical chromosomal locus ( ACIAD3309 ) [32] . Mixed culture competition experiments revealed that newly acquired class-1 integrons from A . baumannii ( IVS1 and IVS3 ) and S . enterica serovar Typhimurium ( IVS2 ) resulted in a statistically significant reduced relative fitness ( w ) of 0 . 93 ( p = 0 . 01** ) , 0 . 92 ( p = 0 . 02** ) , and 0 . 89 ( p<0 . 01** ) , respectively . The relative fitness of the ancestor was by default set to 1 . 0 . The neutrality of the insertion locus ( ACIAD3309 ) was confirmed using a pair of A . baylyi ADP1 strains that were isogenic except from the insertion of a selective/counter-selective marker pair in strain IVS4 ( A . baylyi ADP1 ACIAD3309::nptII sacB ) ( relative fitness w = 1 . 01 , not significantly different from 1 . 0 ( p = 0 . 2 ) ) . The results are summarized in Figure 1 . To verify that the relative fitness measurements were not hampered by the choice of selective antibiotic resistance markers all fitness measurements presented in this study were repeated with strain IVS4 as an integron-free competitor . The results from these experiments using sucrose ( sacB ) counter-selection were always congruent with kanamycin , spectinomycin , and spectinomycin/ampicillin selective platings , and the results from all parallel competition experiments were pooled before statistical analyzes . The class-1 integrons inserted into A . baylyi ADP1 differed in their gene cassette promoter sequences , located in the intI1 open reading frame . Sequence alignments of the three intI1 sequences inserted into A . baylyi revealed that the integrons with the highest ( IVS2 ) , and lowest ( IVS1 ) fitness costs contained cassette promoters identical to the recently described weak ( PCW ) and strong ( PCS ) promoters , respectively [17] . The difference in relative fitness between strains IVS1 and IVS2 was statistically significant in independent sample t-tests ( p = 0 . 03* ) , suggesting a correlation between integrase activity and the fitness cost of harboring an integron . The integron with the intermediate fitness cost ( strain IVS3 , w = 0 . 92±0 . 04 ) contained a hybrid PC promoter . In three integron-containing A . baylyi strains ( IVS1 , IVS2 , and IVS3 ) , the intI1 integrase genes were inactivated by insertions of either cat ( strain IVS1 intI1::cat ) or nptII sacB cassettes ( strains IVS2 intI1::nptII sacB and IVS3 intI1::nptII sacB ) . These intI1 knockout mutants displayed no significant reduction in relative fitness in mixed competition experiments with the ancestral A . baylyi ADP1 ( Figure 1 ) . To test the hypothesis that strains with inactivated integrases increased fitness when compared to their functional counterparts , independent sample t-tests were performed . For all pairs , the intI1 inactivation restored fitness completely: IVS1 vs . IVS1 intI1::cat ( p = 0 . 015** ) , IVS2 vs . IVS2 intI1::nptII sacB ( p<0 , 001** ) , and IVS3 vs . IVS3 intI1::nptII sacB ( p = 0 , 003** ) . These data further demonstrate that the initial fitness cost of integron-carriage was due to the presence of an active integrase . Expression of the integrase genes in the chromosomal insertion locus was verified by reverse transcription PCRs ( RT-PCR ) in IVS1 , IVS2 , and IVS3 . No transcripts were detected in strains IVS1 intI1::cat , IVS2 intI1::nptII sacB , and IVS3 intI1::nptII sacB ( Figure S1 ) . A total of 20 A . baylyi IVS1 cultures were subjected to daily 1∶100 dilutions in fresh LB medium . During the serial transfer experiments the evolving populations were screened for colonies of increased size on LB agar plates , a method regularly used to identify fitness compensated mutants [33] , [34] . Twice a week agar plates were visually inspected and the first colony of increased size appeared after 30 days in one of the populations . This colony was isolated and frozen down for further analyses . At day 42 we isolated two additional colonies from different populations . These isolates were analyzed and they all contained mutations in the intI1 . Complete integrons from these three evolved A . baylyi IVS1 genetic backgrounds were transferred back into the ancestral A . baylyi ADP1 strain , yielding strains IVS1EV-1 , IVS1EV-2 , and IVS1EV-3 . To test the hypothesis that the evolved integrons increased fitness , they were competed against the ancestral ADP1 . Mixed culture competition experiments revealed that fitness was completely restored in these strains ( Figure 1 ) . Independent sample t-tests further verified that the relative fitness of the each evolved integron was significantly different from its intI1-functional ancestor IVS1 , ( p = 0 , 001** , for all three comparisons ) . Subsequent characterizations of these three transformants by DNA sequencing revealed frameshift mutations close to the start codon of the intI1 open reading frame rendering the integrase non-functional ( Figure S2 ) . RT-PCR of evolved strain IVS1EV-1 yielded no transcript ( Figure S1 ) . We hypothesized that functional integrases are maintained by episodic selection provided by fluctuating environments [31] . To test this hypothesis in silico we parameterized a mathematical model with our own experimental data , and relevant parameters from the literature . Parameters related to resource utilization ( e and km ) were calibrated to yield population sizes close to what we observed in the laboratory . The MIC values were based on our own experiments , parameters on growth characteristics were derived from our own study ( fitness values ) combined with values from the literature . For a complete list of parameters used in these serial transfer simulations , see Table 1 . Fig . 2A shows the predicted population dynamics of strains harboring a newly acquired integron with a functional integrase with one ( I1 – blue line ) and two ( I2 – black line ) gene cassettes , the integron free susceptible wild type ( P – green line ) , and two fitness ameliorated integrase- mutants ( M1 - light blue and M2 - grey ) . The predicted in silico population dynamics , before “shift” in Figure 2A , mirrors our experimental data form the serial transfer cultures where integrase specific fitness compensating mutants were isolated after 30 and 42 days of serial transfers . These single mutants were selected on antibiotic free agar plates with approximately 100 colonies , suggesting an approximate frequency of 1/100 . Fluctuating environments are simulated by a probability of encountering antibiotic A for a period of 40 transfers , and then antibiotic B for the remaining time period , both at a 10% probability per transfer . The results shown in Fig . 2A are the median values for 100 simulations . Our simulations show that functional integrases are descending when only one antibiotic is present . However , the switch to a second antibiotic B allows the pre-existing two-gene cassette integron ( I2 ) to rapidly ascend to high frequency . During this ascent I1 and M1 are driven extinct . Without further environmental change , the mutated integrase M2 outcompetes its less fit counterpart I2 . As shown in Fig . 2B , persistence of integrons with functional integrases strongly depends on when the switch to antibiotic B occurs . To assess the robustness of the model predictions scenario in Fig . 2A we explored different parameter ranges for the gene cassette acquisition rate ( λ ) , mutation rate for inactivated integrase ( π ) , and the mutation rate for restoration of functional integrase ( θ ) . We performed 500 additional simulations where π and θ were varied over 10 values each , and λ over 5 levels ( ranges provided in Table 1 ) . As illustrated in Figure S3 the model predictions were robust for a wide range of these parameter combinations . Further , we explored the extreme values of the 95% CI of the relative fitness parameter V as experimentally determined ( w = 0 . 91 and 0 . 95 ) alone and in combinations with different parameter values . These values and the mean fitness value ( w = 0 . 93 ) for VI were tested when π , θ , and λ varied over a small range ( ± . 2 . 5% ) to assess changes in model predictions . Qualitatively all additional simulations ( n = 581 ) were consistent with the scenario presented in Figure 2A providing generality to the model predictions ( data not shown ) .
We show for the first time that newly acquired integrons can substantially reduce relative fitness of its new bacterial host . Following the insertion in a selectively neutral chromosomal locus , the three class-1 integrons from isolates of A . baumannii and S . enterica serovar Typhimurium reduced fitness in the A . baylyi recipient by 7–11% . For comparison , these fitness costs are in the range of mutations conferring antimicrobial resistance through modifications of housekeeping genes such as par/gyr mutations ( fluoroquinolone resistance ) in Streptococcus pneumoniae [35] , and some rpoB mutations in E . coli [36] . Direct insertional inactivation of the three intI1 alleles completely mitigated the initial fitness reductions , clearly suggesting that the fitness costs observed were due to the presence of a functional integrase gene ( intI1 ) . Non-functional integrase genes due to frameshift- and nonsense-mutations are frequently encountered in surveys [28] , [30] , [37] . We asked whether functional intI1 genes would be inactivated during experimental evolution . After 30–42 days of daily serial transfers we observed colonies of increased size on agar plates , representing putative fitness compensated mutants . Integrons from evolved isolates were subsequently introduced into the ancestral genetic A . baylyi ADP1 background , and in these strains they no longer reduced fitness of the host bacterium ( Figure 1 ) . Sequence analyses of the three intI1 genes revealed the presence of frameshift mutations in the first quarter of intI1 resulting in premature stop codons , rendering these integrases inactive ( Figure S2 ) . The emergence of non-functional intI1 genes during experimental evolution with mutational inactivation patterns identical to those reported from bacterial isolates of environmental and clinical origins [28] , [30] , [37] strongly suggests that integrase pseudogenes may ascend to high frequencies in bacterial populations by natural selection . It was recently demonstrated that intI expression is under the control of the SOS response through the presence of LexA binding sites in the integrase promoters ( including class-1 intI1 ) [29] , [37] . These authors proposed that LexA repression reduce the potential detrimental effects of intI expression , and that SOS induction allows expression of the integrase gene when new gene cassettes could provide a response to stressful and potentially lethal environmental conditions [29] , [37] . It was also suggested that integrase inactivation is correlated with absence of LexA regulation [37] , and that this is a key factor explaining the high proportion of pseudo-intI-genes found in integron-containing bacteria [28] , [30] , [37] . The experimental data reported here are the first to support both these hypotheses . The majority of Acinetobacter species , including our model organism A . baylyi and the clinically relevant A . baumannii all lack lexA homologues [38] , [39] . Thus , intI1 is most likely not under LexA repression in our model system , and the newly acquired integrons reduced fitness in A . baylyi , despite the presence of native LexA binding sites in two out of three integrons . The mutational inactivation of intI1 completely mitigated the fitness costs of integron carriage , and in the absence of repression the inactivation could very well mimic tight repression of integrase expression . The serial transfer experiments were performed in nutrient-rich LB medium , as opposed to minimal medium for the competition experiments . The emergence of fitness compensated A . baylyi with non-functional integrases during experimental evolution strongly suggests that the fitness costs of integron carriage are not limited to specific growth conditions . Consequently , the fitness restoration due to intI1 inactivation leads to stabilization of the cassette arrays in the bacterial population , and integron-borne antibiotic resistance determinants will not be reduced following relaxed selective antibiotic pressures . Previous reports indicate an inverse correlation between gene-cassette promoter ( PC ) strength and integrase activity [17] , [18] as well as expression levels [19] . From the results presented in these reports it could be hypothesized that a strong gene-cassette promoter would decrease the overall activity of the integrase gene , and that the cost of integron carriage should be reduced . Our results favor this hypothesis . However , the results should be interpreted with some caution since we achieved significance at the alpha level , but not when Bonferroni correction was applied . The newly acquired integron from a clinical A . baumannii strain ( ∼7% fitness cost ) contained a cassette promoter sequence identical to the “strong promoter” ( PCS ) whereas the cassette promoter of the integron from the S . enterica serovar Typhimurium strain ( ∼11% fitness cost ) displayed a “weak promoter” ( PCW ) , as reported by Jove et al . [17] . Moreover , the integrase sequence from S . enterica serovar Typhimurium revealed amino acids in positions 32 ( R ) , and 39 ( H ) consistent with the highest recombination activity demonstrated in [17] . Jove and co-workers suggested that increased expression of gene cassettes , leading to higher levels of resistance , would be selected for in environments with strong antibiotic selective pressures . Our results add complexity to that hypothesis insofar that the increased expression of gene-cassettes also could lead to reduced integrase activity , and thus stabilize functional integrons in non-selective environments . Two lines of evidence support that the mechanistic basis for the observed fitness effects of functional integrases is reduced genomic stability . First , IntI1 can catalyze recombination events between attI/attC sites and frequently encountered non-canonical sites in the genome , as demonstrated by Recchia and co-workers [40] . Secondly , purified IntI1 enzyme possesses all functions necessary for target recognition and recombination , as shown in in vitro strand transfer assays [41] , [42] . Consequently , when newly acquired and in the absence of tight regulation , expressed integrase would be able to form recombination junctions between the integron and sequence-regions elsewhere in the genome . Resolution of such single strand crossovers ultimately leads to potentially lethal deletions of the genomic region between the recombination sites either following replication or IntI1 activity , as demonstrated in co-integrate resolution experiments [40] . We hypothesized that environmental fluctuations and episodic selection [31] are key to the maintenance of functional integrases , and explored this in computer simulations . According to our hypothesis selection for pre-existing gene cassettes in integrons ( type-1 episodes ) acts synergistically with the ability to capture new cassettes that provide bacteria with a selective benefit in changing environments ( type-2 episodes ) . Type-1 episodes favoring pre-existing gene cassettes allow integrons to reach high frequencies in the population but during these conditions , due to the fitness cost of the active integrases , non-functional integrases rapidly ascend in the population . Type-2 episodes select for new gene cassettes acquired by the active integrase . Our simulations show that maintenance of functional integrases depends on the time between the different episodes ( i . e . the frequency of environmental change ) , as well as the continuous availability of new and adequate gene cassettes . Of course the selective episodes could be other favorable traits encoded by gene-cassettes , and are not limited to antibiotic resistance determinants . In conclusion , the presented data suggest that in the absence of intI1 repression , a fitness trade-off exists for the maintenance of integrons with functional integrases . The initial high fitness cost of the integrase can only be outweighed by selection for gene cassette dynamics .
Plasmid pTM4 is derived from the pGT41 [44] and was used for in vitro insertion of integrons into a chromosomal locus . pTM4 contains segments identical to upstream and downstream segments of the 5′-region of the chromosomal A . baylyi ACIAD3309 open reading frame for homologous recombination , interrupted by a SacI/Ecl136II restriction site , and was constructed as follows: The downstream segment ( 707 bp ) was PCR-amplified with primers ACIAD3309-down-F ( including a 5′-heterologous tail containing an Ecl136II/SacI site ) and ACIAD3309-down-R ( Table S2 , in Text S1 ) with Phusion DNA polymerase ( Finnzymes , Espoo , Finland ) according to the manufacturer's instructions but with 10% DMSO added , and inserted into the KspAI site of pGT41 , giving pTM1 . The upstream segment ( 785 bp ) was amplified with primers ACIAD3309-up-f and ACIAD3309-up-r ( with 5′-Ecl136II/SacI tail ) and inserted into the OliI site of pTM1 , giving pTM2 . From pTM2 , two unwanted segments were removed as follows: A 2 . 7 kbp insert containing an nptII ( kanamycin resistance ) gene was excised by cleavage with SacI ( has 2 sites in pTM2 ) and re-circularization of the large fragment , resulting in pTM3 which has the two segments for homologous recombination ligated immediately upstream and downstream of an Ecl136II/SacI restriction site . From pTM3 , the bla ( ampicillin resistance ) gene was truncated and rendered non-functional by cleavage with XmnI ( contains 2 sites in pTM3 ) and re-circularization of the large fragment , giving pTM4 . A . baylyi IVS1 was constructed as follows: The integron of A . baumannii Ab064 ( Table 2 ) including the 5′- and 3′-CS was PCR-amplified with Phusion polymerase using 5′-phosphorylated primers IntF2 and OrfRev3 ( Table S2 , in Text S1 ) and ligated to Ecl136II-cleaved ( blunt-ended linear ) pTM4 , respectively . The ligation assay was used as donor DNA to naturally transform ( see below ) A . baylyi ADP1 . Transformants were selected on medium containing kanamycin ( 25 µg/ml ) . One transformant was generated from a PCR product covalently joined to a vector molecule at both ends and that substituted the 5′-end of ACIAD3309 with the integron from A . baumannii Ab064 , and termed IVS1 . Co-integrates were excluded by screening for chloramphenicol sensibility , and the desired insertion was verified by PCR . The strains IVS2 and IVS3 were constructed as described for IVS1 with integrons of S . enterica serovar Typhimurium 490 and A . baumannii 47-42 ( Table 2 ) , respectively , using primers IntF2/OrfRev2 and employing corresponding selection and PCR controls . The three class-1 integrons differed in the variable regions ( Table 2 ) as well as in the integrase sequences ( different gene cassette promoters and SNPs ) . The integrase accession numbers are JX041889 ( A . baumannii Ab064 ) , AM991977 ( S . enterica serovar Typhimurium 490 ) , and JX259274 ( A . baumannii 47-42 ) . Strain IVS4 ( locus neutrality control ) was obtained by transformation of A . baylyi ADP1 by pTM2 ( kanamycin-resistant , sucrose-sensitive , verified by PCR ) . The intI1 gene of IVS1 was disrupted by natural transformation with HincII-linearized pACYC177-int-cat as substrate for natural transformation ( Table S1 , in Text S1 ) . This plasmid contains an internal fragment of the intI1 gene of A . baumannii AB064 with a cat ( chloramphenicol resistance ) gene inserted . The resulting strain was PCR-verified and termed IVS1 intI1::cat . The intI1 genes of IVS2 and IVS3 were insertion-inactivated in a corresponding manner by pACYC177-int-nptII-sacB , which contains the nptII sacB marker pair ( kanamycin resistance/sucrose susceptibility ) from pTM2 ( cloned as Ecl136II fragment ) instead of cat [45] , [46] . The resulting strains were verified phenotypically , and by PCR and termed IVS2 intI1::nptII sacB and IVS3 intI1::nptII sacB , respectively . Strain IVS1 with a class-1 integron from A . baumannii Ab064 was subjected to daily one hundred-fold dilutions in 10 ml LB broth in 20 independent parallels for 30–42 days . Aliquots were plated every third day on LB agar plates to screen for fitness-compensated mutants by increased colony size . Evolved integrons were transferred back into the ancestral A . baylyi ADP1 background by PCR-amplification including surrounding regions of homology using homologous transformation ( yielding strains IVS1EV-1 , IVS1EV-2 , and IVS1EV-3 ) ( Table 2 ) . Integron-containing and -free A . baylyi ADP-1 , otherwise isogenic , were subjected to mixed competition experiments as previously described [12] , [47] with the following modifications: Competing strains were pre-grown in S2 minimal media for 24 hours before diluting 1∶10 in NaCl ( 0 , 9% ) , and 150 µl of each competitor was transferred and mixed into 2 . 7 ml S2 medium supplied with 0 . 1% DNase ( to exclude natural transformation in the assays ) . Initial ( N0 ) and final densities ( N24 ) of competing strains were measured before the onset of competitions and after 24 hours by selective and non-selective plating . Selective traits exploited were antibiotic resistance markers or a counter-selective marker ( nptII or aadB , kanamycin resistance; aadA , spectinomycin resistance; blaOXA-30 ampicillin resistance; sacB , sucrose susceptibility ) . From these densities , the Malthusian parameter ( m ) of each competitor was determined using the equation m = ln ( N24/N0 ) . Relative fitness ( w ) was estimated as the ratios of each competitor's Malthusian parameter ( m1/m2 ) [47] . To avoid potential marker-bias m1 and m2 were estimated by selective plating on antibiotics ( kanamycin/spectinomycin/ampicillin ) in one genetic background followed by sucrose selection in the other . Results were always congruent for the antibiotics and concentrations chosen , and data from both selective regimes were pooled . Estimates of w were based on 12–24 parallel experiments for each competition experiment . Preparation of competent cells and transformation assays were performed as described previously [12] , [48] with some modifications . Briefly , competent cells were prepared by diluting an overnight culture of A . baylyi 1∶100 in fresh LB . The culture was incubated at 30°C with vigorous shaking until the cell titer reached 1×109 ml−1 . The cells were chilled on ice , pelleted by centrifugation at 5000×g and 4°C for 15 min , and re-suspended in LB supplemented with 20% glycerol . Aliquots were stored at −80°C until use . For transformation , competent cells were thawed on ice and diluted 1∶40 in LB medium containing the donor DNA . The assays were aerated for 90 min at 30°C and plated on selective media plates in appropriate dilutions . The plates were incubated at 30°C until visible colonies had formed ( 16–40 hours ) . The minimal inhibitory concentrations ( MICs ) of the donor , recipient and transformant strains were determined for sulfamethoxazole , kanamycin , streptomycin , spectinomycin , gentamicin , and ampicillin , by E-test according to the instructions of the manufacturer ( BioMeriux , France ) . Nucleic acids were isolated with QIAGEN Genomic/Plasmid DNA kits ( QIAGEN , Germany ) , according to the manufacturer's instructions . The transformation assay using A . baumannii 064 , S . enterica serovar Typhimurium 490 and A . baumannii 47-42 as donors , resulted in a number of transformants that were analyzed phenotypically ( MIC values , Table S3 , in Text S1 ) and genotypically . Primers IntF2/OrfRev3 and IntF2/OrfRev2 were used to amplify the entire integron region in both transformants and donor strains , giving approximate sizes of 4 kb , 5 kb , and 6 kb for A . baumannii 064 , S . thyphimurium 490 and A . baumannii 47-42 transformant strains , respectively . Primers 5CS′/3CS′ were used to verify the size of the variable regions in both donor and test strains; primers UpF/DownR as well as IntF2/3CS′ and 5CS′/OrfRev2/OrfRev3 were used to confirm the correct position of the aquired integrons in the ADP1 genome . Primers IntF2/OXA303R and IntF2/aacC1-OrfP-R were used to verify the position of the gene cassettes within an integron in the strains IVS2 and IVS3 , respectively . Primers aadBF/aadBR , OXA305F/OXA303R and aacC1-F2/aacC1-orfP-R ( Table S2 , in Text S1 ) were used for gene cassettes identification within the integrons . The unknown regions surrounding the integron in the donor were sequence determined by direct genomic DNA sequencing ( primer walking ) as described previously [12] with the following modifications: 20 µl sequencing reactions consisted of 4 µl BigDye v3 . 1 sequencing mix ( Applied Biosystems ) , 4 µl of the primer at a concentration 10 mM , 4 µl of a sequencing buffer , and ∼4 µg of the purified chromosomal DNA . The sequences of the integrons in the donor strain and transformants were determined by sequencing ( BigDye Chemistry ) of the PCR products obtained from the primers IntF2/OrfRev3 , or IntF2/OrfRev2 ( Table S2 , in Text S1 ) . The sequence of the integrase gene was determined by sequencing of the PCR products amplified with the primers IntF2/aadBR , IntF2/GCS1RevComp , and INCINTF/IntI1F . PCR products were purified by adding a mix of exonuclease 1 ( 0 . 2 U/µl PCR product ) ( New England Biolabs ) and shrimp alkaline phosphatase ( 0 . 01 U/µl PCR product ) ( Roche ) followed by 30 minutes incubation at 37°C and 5 minutes at 95°C in a PCR machine . The obtained sequences were analysed by the Sequencher v . 4 . 2 . 2 programme ( GeneCodes , USA ) and compared to previously published sequences ( GenBank ) . RNA was isolated using the Total RNA Isolation KIT ( Macherey-Nagel , Germany ) , and cDNA was synthesized using MonsterScript 1st-strand cDNA synthesis Kit ( Epicentre Biotechnologies , USA ) , both according to the manufacturer's instructions . The generated cDNA was amplified using primers INCINTF/IntI1F ( Table S2 , in Text S1 ) . To investigate the conditions that favor maintenance of integrons in bacterial populations , we used a mathematical model and numerical solutions , based on [31] [49] . This serial passage model included five populations . Populations I1 and I2 , contain functional integrases where I1 has captured a single cassette encoding resistance to antibiotic A , I2 has captured two gene cassettes and is resistant to both antibiotics A , and B . Populations I1 and I2 can acquire frameshift mutations in intI1 and turn into populations M1 and M2 with non-functional integrases , respectively . Population P is the antibiotic susceptible , integron-free wild type . The growth rates of I , M , and P populations are determined by the pharmaco-dynamic function developed by Regoes and co-workers [49] , where a Hill-function determines the growth rate or death rate ( negative growth rate ) of the populations in the presence of antibiotics [50] , [51] . Briefly , the growth rate depends on the concentration of resource ( R ) , antibiotics ( A and B ) , and antibiotic susceptibility ( MIC ) . In this model each population have two different growth rates; and . In the simulations is chosen if antibiotic A is present , and when B is present , such that . Thus , the model does not simulate events where both antibiotics are present . With these definitions the changes in the population densities during one serial transfer event of I , M , and P populations are given by the following equations:where e µg/ml is the conversion efficiency ( the resource concentration necessary to produce one new cell ) [52] , da and db are the decay rates of the antibiotics , π is the mutation rate for generating defective integrases , and θ is the mutation rate for restoring functionality of defective integrases . λ is the rate at which populations with functional integrases acquire gene cassettes . An illustration of the model with respect to π , θ , λ is given in Figure S4 . A list of parameter values is given in Table 2 . Following each simulated dilution ( 1∶100 ) 50 µg/ml of the resource was added , and the introductions of antibiotics were stochastic events . Each transfer was assigned a random value ( range 0 to 1 ) from a uniform distribution of numbers . When this value was above a defined probability of 10% , antibiotics were added at 2× ( antibiotic A ) and 10× ( antibiotic B ) the MIC concentration of the susceptible populations in order to ensure proper selective effects of the added antibiotics . To investigate the temporal effect of fluctuating environments on the population dynamics of integron containing populations the temporal switch from antibiotic A to B was set at days 20 , 40 , 50 , 60 , 70 , 75 , 80 , 100 . A total of 100 simulations were performed at each frequency . To qualitatively test the robustness of the model predictions 500 additional simulations were run for different combinations of θ ( 10 ) , π ( 10 ) , and λ ( 5 ) within the ranges provided in Table 2 . We also tested the model behavior where the parameters θ , π and λ were combined with a small variation around the original selected model parameter ( ±2 . 5% ) for three levels of the relative fitness of integron carriage parameter ( Vx ) . These levels of Vx included the extreme values from the 95% confidence intervals provided in the experimental measurements . For a numerical solution of the differential equations and to simulate the experimental conditions , the open source computer program R version 2 . 14 . 1 was used [53] . Dilutions as well as introduction of resource and antibiotics were determined by the events argument in the lsoda function from the deSolve package version 1 . 10-3 [54] . We assume that gene cassettes are available for the populations with functional integrase . Further , the resistance genes are assumed to be selectively neutral , as supported by the experiments conducted in this study . We model the use of two antibiotics to show the principle of a heterogeneous environment and the antibiotics are assumed to have no interactions . In these simulations a cut off was set at 1 CFU per ml where all growth and interactions were stopped . All populations were diluted 1∶100 every 24 hours . For simplicity , gene cassette reshuffling ( the order of resistance genes ) or loss of single gene cassettes was not considered . For each set of environmental variables the median population densities from 100 simulations were calculated for each time point and the logarithm of the densities plotted at 24-hour intervals until I2 population reaches 1 CFU/ml . Parameter estimation and statistical tests were performed in SPSS vs . 17 . In addition to significance at the alpha level ( 0 . 05* ) , multiple testing issues were addressed by Bonferroni corrections of significance levels ( indicated as ** throughout the text ) . IntI1 from A . baumannii Ab064: JX041889 . IntI1 from S . enterica serovar Typhimurium 490: AM991977 . IntI1 ( partial ) A . baumannii 47-42: JX259274 .
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Horizontal acquisition of mobile and mobilizable genetic elements plays a major role in the development of antimicrobial drug resistance in bacteria . Despite their causal role in drug treatment failure , there is only limited understanding of how horizontal acquisitions of these elements affect bacterial fitness . A prominent group of such genetic elements are the integrons . These genetic elements harbor an integrase-gene that allows the integron to respond to environmental changes by capture and excision of gene cassettes . Here , we have experimentally determined if horizontal acquisition of an integron affect host fitness . The data demonstrate that the initial costs are substantial . However , inactivation of the integrase gene occurred rapidly by spontaneous mutation alleviating the detrimental effect of the integron on bacterial fitness . The same fitness restoring effects was also shown by targeted inactivation of the integrase gene . The inactivation results in a negative trade-off between host adaptation and loss of the ability to capture new gene cassettes . Importantly , our results explain the frequent observation of inactive integrase genes in integrons found in bacteria of different origins . Finally , we use mathematical modeling to determine the conditions necessary for maintaining functional integrases .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"biology",
"microbiology",
"evolutionary",
"biology",
"population",
"biology",
"genetics",
"and",
"genomics"
] |
2012
|
A Trade-off between the Fitness Cost of Functional Integrases and Long-term Stability of Integrons
|
In eukaryotic cells , the internalization of extracellular cargo via the endocytic machinery is an important regulatory process required for many essential cellular functions . The role of cooperative protein-protein and protein-membrane interactions in the ubiquitous endocytic pathway in mammalian cells , namely the clathrin-dependent endocytosis , remains unresolved . We employ the Helfrich membrane Hamiltonian together with surface evolution methodology to address how the shapes and energetics of vesicular-bud formation in a planar membrane are stabilized by presence of the clathrin-coat assembly . Our results identify a unique dual role for the tubulating protein epsin: multiple epsins localized spatially and orientationally collectively play the role of a curvature inducing capsid; in addition , epsin serves the role of an adapter in binding the clathrin coat to the membrane . Our results also suggest an important role for the clathrin lattice , namely in the spatial- and orientational-templating of epsins . We suggest that there exists a critical size of the coat above which a vesicular bud with a constricted neck resembling a mature vesicle is stabilized . Based on the observed strong dependence of the vesicle diameter on the bending rigidity , we suggest that the variability in bending stiffness due to variations in membrane composition with cell type can explain the experimentally observed variability on the size of clathrin-coated vesicles , which typically range 50–100 nm . Our model also provides estimates for the number of epsins involved in stabilizing a coated vesicle , and without any direct fitting reproduces the experimentally observed shapes of vesicular intermediates as well as their probability distributions quantitatively , in wildtype as well as CLAP IgG injected neuronal cell experiments . We have presented a minimal mesoscale model which quantitatively explains several experimental observations on the process of vesicle nucleation induced by the clathrin-coated assembly prior to vesicle scission in clathrin dependent endocytosis .
The cellular process of endocytosis is important in the biological regulation of trafficking in cells , as well as impacts the technology of targeted drug delivery in nanomedicine [1] , [2] , [3] , [4] , [5] , [6] , [7] . In eukaryotic cells , the internalization of extracellular cargo via the endocytic machinery is an important regulatory process required for many essential cellular functions , including nutrient uptake and cell-cell communication . Several experimental [8] as well as theoretical [9] , [10] , [11] treatments have addressed mechanisms in endocytosis , yet the role of cooperative protein-protein and protein-membrane interactions in the ubiquitous endocytic pathway in mammalian cells , namely clathrin-dependent endocytosis ( CDE ) , remains unresolved . A sequence of molecular events in CDE is responsible for the recruitment of adaptor protein 2 ( AP-2 ) , accessory proteins such as epsin , AP180 , Eps15 , Dynamin , etc . , and the scaffolding protein clathrin to the plasma membrane [8] . The accessory proteins such as epsin are implicated in membrane bending [12] . Polymerization of clathrin triskelia in the presence of adaptor proteins such as AP-2 results in the clathrin coat formation , and tubulating proteins such as epsin interact with both the clathrin coat as well as the bilayer [13] to stabilize a clathrin-coated budding vesicle . The involvement of dynamin is believed to be in the vesicle scission step [8] . Even though actin is believed to play an important role in the endocytosis process in S . cerevisiae ( yeast ) , in mammalian cells , actin repression , at best , has a small effect on endocytosis [14] . We focus on the energetic stabilization of a budding vesicle induced by the clathrin-coat assembly . Recent work [15] demonstrates that the membrane invagination only begins in the presence of a growing clathrin coat [16] . Experiments performed by down-regulating AP-2 expression [17] , [18] as well as those involving the inhibition of epsin [19] either significantly decrease the number of clathrin-coated pits or alter the distribution of coated-intermediates involved in the vesicle-bud formation . Although the CDE in mammalian cells remains a complex regulatory process , we believe that a critical and self-consistent set of experiments is now emerging which warrants the formulation of physically-based models to quantitatively describe the bioenergetics of protein-induced vesicle formation in CDE [20] . Even though models directly addressing CDE in the experimental ( cellular ) context have not been proposed , Oster et al . have addressed yeast endocytosis driven by actin [9] , [21] . Moreover , Kohyama et al . [22] have shown that model two component membranes bud in response to induced spontaneous curvature or the line tension between the two components of the membrane and Frese et al . have investigated the effect of protein shape and crowding on domain formation and curvature in biological membranes [23] . A recent mini-review examining the current experimental trend by Lundmark and Carlsson on driving membrane curvature in clathrin-dependent and clathrin-independent endocytosis is also available [24] . We formulate a minimal model , by restricting our focus to three proteins in the clathrin-coat assembly ( Fig . 1 ) : clathrin , epsin and AP-2 , and their role in the stabilization of a budding vesicle on the cell membrane . Mammalian cells have a diverse set of proteins which often serve as surrogates and participate in compensatory mechanisms . In this regard , our choice for the ingredients for the minimal model represents roles for the scaffolding proteins ( clathrin ) , curvature inducing proteins ( epsin ) and the adaptor proteins ( AP-2 ) . Recent experiments [15] , [25] have reported characteristics of nucleation and growth of clathrin coat: the initiation was observed to occur randomly , but only within subdomains devoid of cytoskeletal elements . In BSC1 cell lines , such domains appear to be 400 nm in diameter surrounded by a rim of a 200 nm “dead zone” . Notably , the nucleation of clathrin coats was observed only in the 400 nm region [25] with the following salient properties: ( a ) in the growth phase , the addition of clathrin proceeds at a steady rate of about one triskelion every 2 s , ( 6s-old coats have 10–20 clathrins ) . ( b ) Two fates are possible for a growing coat; they either transform into a vesicle ( in 32 s the structure resembles a coated vesicle , 50–100 nm in diameter depending on cell type ) , or they abort containing about 10–40 triskelia , which suggests that the coat sizes are bounded . While we do not consider the process of nucleation and growth of clathrin , based on the above observations , we study the process of one maturing vesicle in the presence of an assembled clathrin coat of a finite size in a membrane patch free of cytoskeletal elements and subject to a pinned boundary condition at the patch boundary . For our model cell membrane patch not fortified by cytoskeleton , we employ a typical value of bending rigidity of our κ = 20kBT derived from literature [26] , [27]; ( we also explore the effect of varying κ ) . In this respect , we describe a mean-field model which characterizes the membrane patch as a homogeneous phase with effective ( bulk-like ) properties . Our model is also mean-field in the sense that it applies to just one vesicular intermediate and the effect of neighboring coats is not included . As noted earlier , our model does not account for the mechanism of clathrin coat nucleation or that of vesicle scission . Clathrin triskelia and AP-2 ( in a ratio of 1∶1 ) polymerize to form a coat [28] and the stabilizing interactions in the clathrin coat assembly can be quantified using the free energy of the polymerization process . Based on in vitro equilibrium data of clathrin cage formation , Nossal [29] estimated the energetics of a fully-closed clathrin/AP-2 basket relative to a dissolved coat to be ≈−20 kBT . The inclusion of epsin in the clathrin-coat accounts for −23 kBT of energy per bound epsin: the ENTH domain of epsin binds to the PtdIns ( 4 , 5 ) P2 ( or PIP2 ) lipid head groups on the membrane with a binding energy of −14 kBT per bound epsin [12] and the CLAP domain of epsin interacts with clathrin/AP-2 with an energy of −9 kBT [30] . The ENTH interactions with the membrane require the presence of PIP2 , which constitutes about 1% of the total phospholipids on the cell membrane [31] . To produce a coated vesicle d = 50 nm diameter , ( based on the empirical scaling relationship , the number of triskelia involved ∼0 . 031d7/4 is 29 [29] ) , the area of the clathrin coat required is πd2 = 7850 nm2 . Considering the area per lipid head-group to be 0 . 65 nm2 , the number of PIP2 molecules in the membrane spanning the area of the coat is 1% of ( 7850/0 . 65 ) = 185 . Hence , we note that the ratio of ENTH binding sites ( which correspond to the PIP2 on membrane ) to the CLAP binding sites ( which correspond to the triskelia ) is 185/29≈6 , and hence as the clathrin coat grows , we expect sufficient number of the corresponding PIP2 binding sites to be present for the ENTH domain of epsin to bind . For this reason , we are justified in not explicitly considering PIP2 as a necessary/limiting species in our minimal model .
Field-theoretic approaches are popular for studying energetic and entropic contributions in continuum field-based mesoscale models [32] , [33] and several successful applications of such mesoscale models for gaining mechanistic insight into cell-membrane mediated processes are available [3] , [9] , [21] , [34] , [35] , [36] . Here , to model membrane response in CDE , we solve the membrane equations in a curvilinear manifold by assuming an underlying axis-symmetry using the surface evolution formalism outlined by Seifert et al . [37] . We derive the equations governing membrane shapes of minimum energy under imposed curvature fields assuming that curvature fields are additive and that protein insertion does not cause spatial heterogeneities in physical properties of membrane such as bending rigidity and interfacial frame tension . Parameterizing the membrane shape by the angle , where s is the arc-length along the contour , we obtain and , where prime indicates the derivative with respect to arc-length s , ( Fig . 2 ) . As described by Safran [38] , for topologically invariant membrane shape transformations , the contribution of the Gaussian curvature term to the membrane deformation energy is a constant . Hence , we describe the membrane energy , E using the Helfrich formulation [39] . By including only one of the two principal curvatures , namely the mean curvature: ( 1 ) Here , H is the mean curvature of the membrane , H0 is the imposed ( or intrinsic ) curvature of the membrane due to curvature-inducing proteins and is a function of arc-length s , is the membrane interfacial frame tension and A is the total membrane area . We express curvature H and the area element dA in terms of . Minimization of this energy functional with respect to leads to ( see Text S1 ) : ( 2 ) Here , is a Lagrange multiplier introduced to satisfy the constraint ( which defines R ) . We also impose the boundary condition at R = R0 ( or at s = s1 ) corresponding to the pinning of the membrane by the cytoskeleton at the boundary of the membrane patch . In addition , due to the axis-symmetry , at R = 0 , . Since the total arc-length s1 is not known a priori , one additional closure equation is specified , ( see Text S1 ) : . We solve the above system of boundary valued differential equations numerically by the shooting and marching technique [40] , ( see Text S1 ) , yielding membrane profiles for a specified spontaneous curvature function , and pinned at R = R0; in this work , we employ R0 = 500 nm . We also compute the curvature deformation energy of the membrane defined by: ( 3 ) We present our results for the case when interfacial frame tension σ is zero . Results obtained for non-zero σ ( not shown ) are found to be similar to the σ = 0 case . We also note that in prior work , we showed that the entropic term |TΔS| at T = 300K is small , i . e . ∼5% of the membrane bending energy for κ = 20 kBT [41] . This result justifies the basis for neglecting thermal fluctuations ( such an assumption was also employed by Oster et al . for their model for endocytosis in yeast [9] ) and is valid except in cases where the vesicle neck region becomes narrow ( i . e . same order of magnitude as the bilayer thickness ) . The situation of a narrow vesicle neck is very pertinent to vesicle scission , where even the continuum treatment of the membrane is subject to approximations and a molecular treatment is necessary as described by Lipowsky et . al , recently [42] . For a given membrane profile , the area of the coat Aa ( s0 ) is computed using the relationship , ( 4 ) where , the neck-radius R ( s0 ) is the radius at s0 , which marks the coat boundary .
In our model , the dominant factor contributing to the intrinsic curvature H0 in the region where the membrane binds to the clathrin coat is the presence of epsins , bound at the CLAP-binding sites on the coat . In a recent study , [11] , we modeled the spontaneous curvature induced by one epsin as a Gaussian function: ( 5 ) That is , for the nature of epsin-induced curvature , we have assumed a form that has a spatial decay . Such a choice of spatially-varying intrinsic curvature function is motivated by recent molecular simulations [35] , [36] , [43] , [44] . We have also employed such models in our earlier work [11] , [45] . Similarly , for integral membrane proteins , a local curvature model has been proposed by Goulian et al . [46] , Oster et al . [47] , and Lubensky et al . [48] . Hence there is a bank of such phenomenological curvature models in use in the literature . In vitro , Ford et al . [12] observed tubulation of vesicles upon addition of epsin; the observed tubule diameter of 20 nm enables us to estimate C0 = 0 . 1 nm−1 . Using the surface-evolution approach , we calculate the curvature deformation energy of the membrane , Ec ( defined in Eq . ( 3 ) ) when a single epsin interacts with the membrane , i . e . through the curvature function in Eq . ( 5 ) . Since the energy Ec is stabilized by the negative interaction energy of the ENTH domain of epsin with the membrane ( Er ) , we iteratively determine the value of b in Eq . ( 5 ) such that Ec≈|Er|; using Er = −14kBT [12] , we obtain b = 8 . 3 nm for κ = 20 kBT . The periodicity of clathrin lattice , ( from cryo-EM studies [49] , the average distance between adjacent vertices of the hexagons in the clathrin cage is 18 . 5 nm ) , ensures that epsins are templated to maintain both spatial as well as bond-orientational ordering [50] . Hence , within our axis-symmetric membrane model , we translate the patterning of epsins on the clathrin coat to an intrinsic curvature function H0 of the form: ( 6 ) Here , the index i runs over the number of concentric shells of epsins on the coat separated by a distance of 18 . 5 nm , the underlying periodicity of the clathrin lattice . Hence , relative to a central epsin bound to the coat at R = 0 and s0 , 1 = 0 , successive shells of epsins are located at s0 , 2 = 18 . 5 nm , s0 , 3 = 37 nm , s0 , 4 = 55 . 5 nm , etc . until we reach the periphery of the coat of a prescribed extent ( or area Aa ) ; the H0 function is depicted in Fig . S5 and the schematic location of the shells is also depicted in Fig . 2 . We note that the coat boundary is prescribed by the value of s0 for the outermost shell and the neck-radius R ( s0 ) is the radius at this value of s0 , as described earlier . In Fig . 3a , we depict energy minimized membrane deformation profiles for different values of the clathrin coat area Aa ( defined in Eq . ( 4 ) ) obtained using the surface evolution method and subject to the epsin curvature fields described by Eq . ( 6 ) ; we find that above a critical value of the coat area , the membrane profile develops overhangs , ( also evident from the behavior of the neck-radius in Fig . 3b ) , which when the coat area Aa approaches 6500 nm2 , transforms to a mature spherical vesicular bud with a narrow neck . We emphasize the generality of this result , i . e . , that there exists a critical coat area above which the membrane deformation develops an over-hang and a constricted neck , by confirming this observed trend using a conceptually simplified “capsid model” in which H0 ( s ) = 0 . 08 nm−1 if s<s0 and H0 ( s ) = 0 if s≥s0 , s0 is the length of the clathrin coat , as described in Text S2 and Fig . S1 . In Fig . 3a , we estimate the number of epsins , Nepsins , i in each shell i as: ( 7 ) where , R is in nm , and 18 . 5 ( nm ) represents the triskelial spacing underlying the clathrin lattice; R ( s ) is depicted in Fig . S3 . The total number of epsins is obtained by summing over the number of shells , which for the mature vesicular bud is estimated to be 23 , see ( a ) in Fig . 3a . Our results for the epsin shell model assumed a value for the bending rigidity of κ = 20 kBT reported in the literature [26] , [27] . However , membrane bending rigidity depends upon multiple factors: membrane lipid and protein composition , anchoring of lipid with cytoskeleton , etc . Hence , a broad range of bending rigidity , 10–400 kBT has been reported in the literature: in particular , there is consensus that cytoskeleton-free membranes have rigidity in the range of 20 kBT and cytoskeleton-fortified membranes can be as stiff as 400 kBT . For this reason , it has indeed been postulated that apparent bending rigidity of the membrane depends on the relevant length scale and lies between 20 kBT ( membrane patches below 100 nm ) and 500 kBT ( membrane patches of 1 µm ) [26] . Hence , we have further explored the effect of varying κ in the range κ = 10–50 kBT on the mechanism of epsin-induced vesicular bud formation . In Fig . 4 , we plot the membrane profiles for a mature vesicle for different values of κ . We note that , in varying κ , we also self-consistently determined the value of b ( the range of epsin curvature ) as outlined earlier: the dependence of b on membrane bending rigidity is shown in Fig . S4 . For each value of κ , we varied the number of shells i in Eq . ( 6 ) to solve for the membrane profiles and determined the number of shells necessary for obtaining a mature vesicle; Nepsins and the diameter of the vesicular bud , d , were also computed as depicted in Fig . 4 . The membrane profiles in Fig . 4 suggest that the epsin-shell model is still viable in orchestrating a mature vesicular bud for different values of membrane bending stiffness . However , we note that there is a strong dependence of the bud diameter on the bending rigidity , which suggests that the variations of in the size of the vesicle in CDE across cell types could be due to changes in the effective bending rigidity of the membrane . The computed deformation energy Ec ( defined in Eq . ( 3 ) ) for the capsid model is plotted in Fig . S2 and is seen to increase linearly with increasing coat area , Aa; we find that the energy Ec required to form a mature spherical bud of diameter 50 nm is estimated to be 25κ = 500 kBT . The estimate is very close to 8πκ , which is the deformation energy of a spherical vesicle of diameter d for which H0 = 4/d ( and constant in space ) . The energy Ec required to deform the membrane can be offset by stabilizing interactions between the proteins in the clathrin coat assembly and between the coat proteins and the membrane . As described in the introduction , the free energy of the clathrin-coat assembly , Ea was estimated by Nossal [29] to be ≈−20 kBT , i . e . , |Ec|≫|Ea| . This implies that the curvature induction in the presence of a clathrin-coat is energetically unfavorable in the absence of additional stabilizing interactions . Indeed , as reported in cell-experiments [25] , not all growing clathrin coats result in vesiculation events and a commitment step possibly accounting for additional stabilizing interactions ( Er which includes those interactions that preferentially stabilize state 2 over state 1 in Fig . 1 ) is necessary . As noted in earlier 1 , inclusion of epsin in the clathrin-coat accounts for εepsin = −23 kBT per bound epsin and hence , within our model , we consider Er ( Aa ) = Nepsins ( Aa ) ×εepsin . Thus , for a given extent of the coat characterized by its area Aa , the total free energy change of the membrane and clathrin-coat assembly in the curved state ( state 2 , see Fig . 1 ) relative to the planar state ( state 1 , see Fig . 1 ) is given by: Et ( Aa ) = Ec ( Aa ) +Ea ( Aa ) +Er ( Aa ) . Recently , Jakobsson et al . [19] studied the role of epsin in synaptic vesicle endocytosis by inhibiting the interactions of epsin with clathrin using a CLAP antibody and those of epsin with PIP2 on membrane using an ENTH antibody . By microinjecting the CLAP antibody into neuronal cells , they observed that while the total extent of clathrin coated regions in the periactive zone on the plasma membrane remained the same , the observed fractions of the coated regions in different stages of coated-vesicle budding prior to scission were altered in a dramatic fashion , ( see Fig . 5b ) : in the control wildtype ( WT ) cells , coated structures resembling a mature vesicular bud are more probable in comparison to planar structures and early intermediates; however , upon addition of CLAP , the early intermediates are stabilized and become more probable at the expense of the number of mature vesicular buds [19] . By computing Ec and Er for different values of Aa in the capsid model , we determine the energetics of the clathrin coated vesicular bud Et versus coat area , Aa for the capsid model ( see Fig . S6 ) . Number of epsins in WT ( control ) cell = 21: this number differs slightly from 23 , the estimate for the epsin shell model , because R ( s0 ) for the capsid model is slightly different from that for the shell model . We also computed probability of observing different coated-intermediates of vesicular structures as P∝exp ( −Et ( Aa ) /kBT ) as depicted in Fig . 5a . The predicted distribution of vesicular intermediates ( Fig . 5a ) closely matches the experimental distribution reported by Jakobsson et al . [19] ( see Fig . 5b ) . For modeling the clathrin-coated vesiculation in CLAP IgG injected cells , we compute the number of epsins as Nepsins ( CLAP cells ) = Nepsins ( WT cells ) *Aa ( vesicles in CLAP injected cells ) /Aa ( in WT cells ) = 33 . The ratio of the respective areas ( = 1 . 6 ) is determined based on the experimental observations of increase in the size of the coated intermediates in CLAP injected cells relative to WT cells [19] . Remarkably , with Nepsins = 33 and εepsin = −14 kBT ( reduced from −23 kBT due to the abrogation of the CLAP-clathrin/AP-2 interaction ) , we find not only that Et ( Aa ) increases monotonically with Aa ( a reversal in trend , see Fig . S6 ) but also the probability P∝exp ( −Et ( Aa ) /kBT ) quantitatively matches the experimentally observed distribution in CLAP IgG injected cells , ( compare Figs . 5a and 5b ) . We note that even though Nepsins increase in the CLAP IgG injected cells relative to wildtype , the size of the bud likely increases due to a lack of templating of epsins; arguably , there is lack of bond-orientational order as the CLAP domains of epsin can no-longer bind the periodic clathrin lattice . Corroborating this view , many extended coated structures ( cisternae ) also appear in the experiments with CLAP IgG injected cells [19] . Furthermore , according to the predictions of our model , disrupting the epsin-membrane interaction ( i . e . , by targeting the ENTH domain of epsin ) completely abrogates Er and should make the coated vesicular bud highly unfavorable . Indeed , consistent with this view , in cells microinjected with ENTH antibodies the extent of clathrin-coated structures decreased by over 90% [19] . Regarding the comparison in Fig . 5 , we re-iterate that the fraction ( or histogram ) is proportional to exponential of the energy . Hence a small error in energy ( of the order of kBT which is 0 . 6 kcal/mol at T = 300 K ) can lead to a large change in the fraction [exp ( 0 . 6 ) ≈factor of 2] . Hence , an order of magnitude agreement in histograms between theory and experiment in the trends of the intermediate shapes implies that the energetics agree even more closely .
In conclusion , we have presented a minimal mesoscale model which we believe imposes the correct spatial as well as thermodynamic constraints , and quantitatively explains several experimental observations on the process of vesicle nucleation induced by the clathrin-coated assembly prior to vesicle scission in CDE . We re-iterate that the input to our model is the membrane bending rigidity , spacing between epsins bound to the clathrin coat , and the curvature-field imposed by each bound epsin , which have all been determined using independent biophysical experiments . For these choices of input , our calculations then yield the membrane profiles for different sizes of the clathrin coat . Based on the number of shells of epsins accommodated on the clathrin coat ( which depends on the size of the coat ) , and the circumference of each shell ( which depends on the coat/membrane deformation ) , the number of epsins is calculated . Thus , the number of epsins , the membrane profile , and the deformation energy are outputs of our model . While our model does not include nucleation of the clathrin coat or scission of a mature coated vesicular-bud , our results identify a unique dual role for the tubulating protein epsin: multiple epsins localized spatially and orientationally collectively play the central role of a curvature inducing capsid; in addition , epsin serves the role as an adapter in binding the clathrin coat to the membrane . Our results also suggest an important role for the clathrin lattice , namely in the spatial- and orientational-templating of epsins for providing the appropriate curvature field for vesicle budding . We suggest that there exists a critical size ( area ) of the coat above which a vesicular bud with a constricted neck resembling a mature vesicle is stabilized . Based on the strong dependence of the vesicle diameter on the bending rigidity , we suggest that the variability in bending stiffness due variations in membrane composition with cell type can explain the experimentally observed variability on the size of clathrin-coated vesicles , which typically range 50–100 nm . Apart from providing a mechanistic description of the budding process in CDE , our model provides estimates for the number of epsins involved in stabilizing a coated vesicle , and without any direct fitting , reproduces the experimentally observed shapes of vesicular intermediates as well as their probability distributions quantitatively in wildtype as well as CLAP IgG injected neuronal cell experiments . We consider such an agreement to be a strong validation for the basis of our model . These model predictions can further be tested by engineering mutations in epsin , clathrin , and AP-2 all of which are predicted to influence the distribution of coated structures . The framework of our approach is generalizable to vesicle nucleation in clathrin-independent endocytosis . Indeed , based on our results we can speculate that alternative mechanisms ( such as receptor clustering ) which can provide a hexatic bond-orientational templating of epsins on the membrane can facilitate vesicle-bud formation independent of CDE [11] . Future modeling work will address spatial distribution of curvature inducting proteins on vesicle nucleation [20] .
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Cell membranes and membrane-based organelles actively mediate several intracellular signaling and trafficking decisions . A growing number of applications rely on cooperative interactions between molecular assemblies and membranes . Yet , the studies of membrane-based and membrane-mediated signaling are not considered core aspects of systems biology . While a coherent and complete description of cell membrane-mediated signaling is not always possible by experimental methods , multiscale modeling and simulation approaches can provide valuable insights at microscopic and mesoscopic scales . Here , we present a quantitative model for describing how cell-membrane topologies are actively mediated and manipulated by intracellular protein assemblies . Specifically , the model describes a crucial step in the intracellular endocytic trafficking mechanisms , i . e . , active transport mechanisms mediated through budding of the cell membrane orchestrated by protein-interaction networks . The proposed theory and modeling approach is expected to create avenues for many novel applications in systems biology , pharmacology , and nanobiotechnology . The particular application to endocytosis explored here can help discern pathological cellular trafficking fates of receptors implicated in a variety of biomedical conditions such as cancer , as well as impact the technology of targeted drug delivery in nanomedicine .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"biophysics/theory",
"and",
"simulation",
"cell",
"biology/membranes",
"and",
"sorting",
"computational",
"biology",
"biophysics",
"physics/interdisciplinary",
"physics"
] |
2010
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Minimal Mesoscale Model for Protein-Mediated Vesiculation in Clathrin-Dependent Endocytosis
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Purpura fulminans is a deadly complication of Neisseria meningitidis infections due to extensive thrombosis of microvessels . Although a Disseminated Intra-vascular Coagulation syndrome ( DIC ) is frequently observed during Gram negative sepsis , it is rarely associated with extensive thrombosis like those observed during meningococcemia , suggesting that the meningococcus induces a specific dysregulation of coagulation . Another specific feature of N . meningitidis pathogenesis is its ability to colonize microvessels endothelial cells via type IV pili . Importantly , endothelial cells are key in controlling the coagulation cascade through the activation of the potent anticoagulant Protein C ( PC ) thanks to two endothelial cell receptors among which the Endothelial Protein C Receptor ( EPCR ) . Considering that congenital or acquired deficiencies of PC are associated with purpura fulminans , we hypothesized that a defect in the activation of PC following meningococcal adhesion to microvessels is responsible for the thrombotic events observed during meningococcemia . Here we showed that the adhesion of N . meningitidis on endothelial cells results in a rapid and intense decrease of EPCR expression by inducing its cleavage in a process know as shedding . Using siRNA experiments and CRISPR/Cas9 genome edition we identified ADAM10 ( A Disintegrin And Metalloproteinase-10 ) as the protease responsible for this shedding . Surprisingly , ADAM17 , the only EPCR sheddase described so far , was not involved in this process . Finally , we showed that this ADAM10-mediated shedding of EPCR induced by the meningococcal interaction with endothelial cells was responsible for an impaired activation of Protein C . This work unveils for the first time a direct link between meningococcal adhesion to endothelial cells and a severe dysregulation of coagulation , and potentially identifies new therapeutic targets for meningococcal purpura fulminans .
Neisseria meningitidis is a natural host of the human nasopharynx . For still unknown reasons , it can invade the bloodstream , causing a severe disease with an annual incidence of about 1 case per 100 000 inhabitants in developed countries . During meningococcemia , patients usually present cutaneous purpuric lesions [1] . These are the consequence of dermis microvessels thrombosis that induce capillary congestion and red blood cells extravasation . In about 25% of patients , these lesions evolve to an extended skin necrosis associated with a severe septic shock , a syndrome referred to as purpura fulminans ( PF ) [2–4] . Other organs can be affected by thrombosis and necrosis such as kidneys , heart and adrenal glands [5–7] . Despite highly active antimicrobials and intensive care , PF is still associated with a high mortality rate . Moreover , in surviving patients , PF lesions often require surgical debridement and limb amputations [8 , 9] . Deciphering PF pathogenesis is then a real need to develop new specific therapeutics to limit extensive thrombosis . As for a Gram-negative sepsis , a Disseminated Intravascular Coagulation ( DIC ) syndrome is commonly observed during meningococcemia . This complication is due to high levels of circulating endotoxin and pro-inflammatory cytokines which trigger the coagulation cascade that ends with thrombin activation and subsequent fibrinogen cleavage . However , PF remains very unusual in Gram negative sepsis , whereas meningococcemia is usually complicated by mild to severe thrombotic events . This suggests that an additional dysregulation of coagulation occurs during this specific infection . Interestingly , apart from meningococcemia , PF also arise from any severe acquired or congenital deficit in the anti-coagulant protein C ( PC ) . PC is a non-active zymogen produced by the liver that is activated by the endothelial cells following the generation of thrombin by the coagulation cascade . Thrombin bound on thrombomodulin , located on the surface of endothelial cells , cleaves PC in activated PC ( aPC ) . Activated PC subsequently inactivates the coagulation cascade factors V and VIII , creating an endothelial-based negative feed-back on coagulation activation . The fixation of PC on the Endothelial Protein C Receptor ( EPCR ) accelerates the rate of aPC generation and has notably been proven to be critical during sepsis [10–14] . Importantly , a reduced endothelial expression of EPCR has been described at the site of meningococcal purpuric lesions [15] . This reflects a local impairment of PC activation which is likely to favor the formation of thrombosis . However , the molecular mechanism involved in this localized reduction of the EPCR endothelial expression remained unknown . In addition to its pro-thrombotic nature , another specificity of meningococcemia is the ability of the bacteria to adhere on endothelial cells and to colonize microvessels [16–19] . In particular , this endothelial colonization has been demonstrated in skin biopsies from purpuric lesions [17 , 19 , 20] . This adhesion is the consequence of the interaction of the bacterial type IV pilus ( T4P ) with its cellular receptor CD147 [21] . T4P is a fiber composed of a major pilin PilE and of minor pilins designated as PilV , PilX and ComP [22] . Alongside its role in bacterial adhesion , T4P also triggers endothelial cells signaling . For instance , the T4P-induced a biased activation of the β2-adrenergic receptor that weakens the endothelial tight junctions and , at the level of the blood-brain barrier , opens the paracellular route to the meninges [23–25] . In this work , we hypothesized that the meningococcal interaction with endothelial cells is linked to the decrease of EPCR expression described at the site of purpuric lesions , leading to a subsequent impairment of PC activation , which is known to be key in thrombosis development . Using an in vitro approach , we demonstrate that meningococcus induces an EPCR shedding as a consequence of T4P-dependent adhesion . We also demonstrate that ADAM-10 ( A Disintegrin and Metalloprotease 10 ) is the membranous protease responsible for this EPCR shedding , identifying for the first time a new sheddase for this receptor . Finally , we show that this ADAM10-dependant EPCR shedding induced by meningoccal interaction with endothelial cell is responsible for an impairment of PC activation .
As mentioned above , EPCR endothelial expression is reduced in purpuric lesions observed in children suffering from severe meningococcemia [15] . These cutaneous lesions correspond to location where bacteria colonize the microvessels [17 , 19] , suggesting that the reduced expression of EPCR is the consequence of the meningococcal-endothelial cell interaction . To test this hypothesis , we infected primary Human Dermal Microvessels Endothelial Cells ( HDMEC ) with the adhesive piliated wild type ( WT ) meningococcal strain 2C4 . 3 and assessed the membranous expression of EPCR using a FACS analysis without cell permeabilization . As shown Fig 1A , the infection induced a rapid and dramatic reduction of EPCR membranous expression . This started as early as 2 hours after the infection of the monolayer . On the other hand , 4 hours of infection had no effect on thrombomodulin expression which remained identical to that of non-infected cells ( Fig 1B ) . We then aimed at determining if this decrease in EPCR expression was a direct consequence of meningococcus adhesion onto endothelial cells . As shown Fig 2A , after 4 hours of infection the expression of membranous EPCR was dramatically reduced on colonized cells , while neighbouring non-colonized cells still expressed this receptor . Furthermore , the infection of a cell monolayer with a non-adhesive non-piliated isogenic derivatives ( ΔpilE ) had no effect on EPCR expression which remained identical to that of non-infected cells ( Fig 2B ) . Altogether these results reveal a direct link between meningococcal adhesion on endothelial cells and the decrease of EPCR membranous expression . As already mentioned the mean by which virulent capsulated meningococci interact with endothelial cells is via their T4P . To demonstrate that the reduction of endothelial EPCR expression was a specific consequence of T4P-mediated adhesion , we used a previously described isogenic derivative of the WT strain which is non-piliated ( ΔpilE ) and non-capsulated ( ΔsiaD ) but expresses Opa adhesins that enable its adhesion on endothelial cells ( Opa+ΔpilEΔsiaD strain—see Material and Methods ) . An isogenic piliated non-capsulated Opa-expressing strain ( Opa+ΔsiaD ) was used as a control to rule out a possible role of the capsule . As shown Fig 3 , EPCR expression after infection with the Opa+ΔpilEΔsiaD strain was comparable to that of non-infected cells . On the other hand , both the WT and the piliated non-capsulated strain ( Opa+ΔsiaD ) induced a decrease of EPCR expression . Altogether these data demonstrate that meningococcus T4P-dependent adhesion is responsible for a rapid and intense reduction of EPCR expression . As shown Fig 1A , the decrease of EPCR expression starts as early as 2 hours after infection of an endothelial cells monolayer . Such a rapid decrease of membranous EPCR suggested that the protein has been proteolytically removed from the cellular membrane . EPCR is indeed known to be a target of ADAM-17 , a membranous protease that , upon its activation , cleaves the extracellular domain of the receptor [26] . To confirm the shedding hypothesis of the EPCR , we quantified the presence of soluble forms resulting from the cleavage of the receptor ( sEPCR ) in the supernatant of HDMEC cells infected for 4 hours with either a WT or a non piliated ( ΔpilE ) strain . As shown Fig 4A , sEPCR concentration in the supernatant of WT-infected cells was increased when compared to that of non-infected or ΔpilE-infected cells . To further support this result , we studied the membranous expression of EPCR after 4 hours of infection with a WT strain in the presence of 25 μM of TAPI-0 , a drug known to inhibit all metalloproteases of the ADAM family , including ADAM17 . We first checked that this drug had no effect on N . meningitidis growth and adhesion on endothelial cells ( S2 Fig ) . As shown Fig 4B , membranous expression of EPCR after infection in the presence of 25 μM of TAPI-0 was almost identical to that of non-infected cells . Moreover , TAPI-0 treatment inhibited the release of soluble forms of EPCR in the supernatant of WT-infected cells ( Fig 4C ) . Altogether these data demonstrate that meningococcal interaction with endothelial cells induces the shedding of EPCR most likely via the activation of an ADAM-family protease . We showed that an inhibitor of the whole ADAM family prevented the meningococcal-induced EPCR shedding . To date , the only sheddase described as being able to cleave the EPCR is ADAM17 [26] . To investigate the role of ADAM17 in the N . meningitidis-induced EPCR shedding , we first inhibited the expression of ADAM17 by transfecting HDMEC cells with a small interfering RNA ( siRNA ) against ADAM17 or a control siRNA . As shown Fig 5A , ADAM17 expression was reduced by ~70% in ADAM17-siRNA-transfected cells ( blue ) when compared to that of control-siRNA-transfected cells ( green ) . Yet , EPCR shedding induced by N . meningitidis 4 hours after infection was identical in ADAM17-siRNA or control-siRNA transfected cells ( Fig 5B ) . To confirm this result , we engineered an ADAM17-negative endothelial cell line using a Crispr/Cas9 technology in the human endothelial cerebral microvessel cell line hCMEC/D3 as described in the material and methods section . The resulting cell line was indeed ADAM17-negative as assessed by a FACS analysis ( Fig 5C ) . The infection of these cells by a WT meningococcus strain induced a strong decrease in the expression of EPCR comparable to that observed with the parental hCMEC/D3 control cells ( Fig 5D ) . Moreover , soluble forms of EPCR were still released upon infection ( Fig 5E ) . These data clearly demonstrate that meningococcus-induced shedding of ECPR is ADAM17-independent . Considering the effect of TAPI-0 , a pan-ADAM inhibitor , we next aimed at finding another member of the ADAM family responsible for the EPCR shedding . This family of proteins includes 21 members among which 13 have a predicted or proven proteolytic activity [27] . The sequence comparison of the full proteases or metalloprotease domains showed a segregation of ADAM17 and ADAM10 which are close relatives and are quite distinct from the other members of this family [27 , 28] . Furthermore , both have been described as sharing several common targets [29] . Therefore , we investigated the role of ADAM10 during meningococcal infection . We first used GI254023X , which is known to specifically inhibit ADAM10 [30] . This drug had no effect on N . meningitidis adhesion and growth on cells ( S2 Fig ) . As shown Fig 6A , the EPCR shedding induced by meningococcus infection was significantly reduced in GI254023X-treated HDMEC cells compared to that of DMSO-treated cells . We then used a siRNA approach . The siRNA treatment was associated with a decrease of ~70% of ADAM10 membranous expression ( Fig 6B ) . We also verified that ADAM17 expression was not modified in the cells treated with the siRNA against ADAM10 ( supplemental data S3 Fig and S4 Fig ) . EPCR shedding induced by meningococcal adhesion was dramatically reduced in ADAM10-siRNA-transfected cells compared to that of control-siRNA-transfected cells ( Fig 6C ) . To confirm the above results , and since the siRNA knock-down was not complete and was associated with variability in experiments , we engineered an ADAM10-negative-hCMEC/D3 cell line using a Crispr/Cas9 genome edition as described in the material and methods section . The absence of expression of ADAM10 in the resulting cell line was demonstrated by a FACS analysis ( Fig 6D ) . The infection of the ADAM10-negative-hCMEC/D3 cells by the WT meningococcal strain was associated with no change of membranous EPCR expression ( Fig 6E ) . Moreover , there was no release of soluble forms of EPCR in the supernatant of ADAM10-negative-hCMEC/D3 upon infection ( Fig 6F ) . To confirm that ADAM10-negative-hCMEC/D3 cells were still able to shed EPCR after stimulation , we used phorbol 12-myristate 13-acetate ( PMA , 1 μM ) a known inducer of Protein Kinase C signaling and subsequent ADAM17-mediated EPCR cleavage [26] . As expected PMA treatment had no effect on ADAM17-negative-hCMEC/D3 , but efficiently induced an EPCR shedding in ADAM10-negative-hCMEC/D3 ( Fig 6G ) . Altogether the above results demonstrate that meningoccal adhesion on endothelial cells induces an ADAM10-dependent shedding of EPCR . PC activation is critical to ensure the control of the coagulation cascade in vivo . PC is activated by endothelial cells following the binding of thrombin on thrombomodulin and PC on EPCR . We demonstrated that meningococcal interaction with endothelial cells induces a profound decrease of EPCR expression through an ADAM10-mediated shedding of this receptor . We then aimed at determining the impact of meningococcal adhesion on the ability of endothelial cells to activate PC . We infected HDMEC for 4 hours with either a WT or a ΔpilE non-adhesive isogenic derivative . Following infection , cell monolayers were incubated with thrombin and PC for 30 minutes and the amount of aPC generated was determined using an enzymatic method . As shown Fig 7A , infection with the adherent WT strain reduced by 50% the production of aPC , whereas the non-adherent ΔpilE strain had no effect . To confirm that this defect in aPC generation was consistent with the loss of EPCR , we incubated cells with a blocking antibody against EPCR that prevents PC interaction with its receptor , limiting the generation of aPC . The reduction of aPC following infection of a monolayer of endothelial cells with the WT strain was comparable to that observed after a complete blocking of PC-EPCR interaction with an antibody ( Fig 7A ) . To demonstrate that this impairment of aPC generation was the consequence of the ADAM10-mediated shedding of EPCR , we first used the pan-ADAM inhibitor TAPI-0 . This drug restored the endothelial cells ability to generate aPC when infected with a WT meningococcus ( Fig 7B ) . Furthermore , the infection of the ADAM10-negative-hCMEC/D3 cell line by a WT strain had no effect on the generation of aPC , comparatively to that of the ADAM17-negative-hCMEC/D3 cell line ( Fig 7C ) . These data demonstrate that meningococcal adhesion on endothelial cells impairs the activation of PC by inducing an ADAM10-mediated shedding of EPCR . Beyond its prominent anti-coagulant function , activated Protein C also initiates important endothelial signaling through its receptor . Indeed , aPC fixed on EPCR is able to cleave the Protease Activated Receptor PAR-1 at a non-canonical site . Once cleaved by aPC , the extracellular domain of the PAR-1 receptor acts as a ligand and induces anti-inflammatory and anti-apoptotic responses [31–35] . aPC also initiates a barrier-protective response of the endothelial monolayer , that involves the transactivation of the sphingosine-1-phosphate receptor ( S1P ) , and an increased activity of Rac1 and a decrease activity of RhoA small GTPases . This prevents the formation of actin-myosin stress fibers and reduces the vascular leakage induced by thrombin or pathogens . This protective effect of aPC relies on the presence of EPCR . [34 , 36–38] . To investigate the effect of the meningococcal-induced EPCR shedding on the barrier-protective signaling of aPC , we used the iCELLigence System that continuously monitors the electrical impedance across the endothelial monolayer and enables a real-time analysis of the barrier function and integrity . Confluent monolayers of HDMEC cells grown on ACEA Plates L8 were infected with N . meningitidis or left uninfected . After 4 hours , the cell culture medium was replaced by fresh medium and activated protein C was added ( 50 and 100 nM ) for 2 hours . Thrombin ( 1 nM ) was added to induce the barrier disruption . As expected , in non-infected cells , aPC provided a protection against thrombin ( Fig 8A and 8C ) . On the other hand , meningococci-infected monolayers were not protected against thrombin-induced disruption ( Fig 8B and 8C ) . These data demonstrate that meningococcal-induced EPCR shedding abrogates the aPC/EPCR/PAR-1 protective signaling .
The activation of coagulation is a common response to pathogens invasion . However , a particular pro-thrombotic activity is observed during N . meningitidis infections . Indeed , most meningococal infections are associated with cutaneous purpuric lesions which are the clinical consequence of dermal microvessels thrombosis . In severe forms , the latter extend and evolve toward a dreadful purpura fulminans syndrome with extensive necrosis . Here , we demonstrate that meningococcal adhesion on endothelial cells induces a rapid and intense proteolytic cleavage of the EPCR from the endothelial membrane . This result is consistent with previous studies showing a reduced EPCR endothelial staining in purpuric lesions [15 , 39] . In addition , we demonstrate that this cleavage has functional consequences by impairing the generation of aPC by endothelial cells . In vivo studies clearly demonstrate that EPCR is key for the generation of sufficient aPC and for the subsequent control of the coagulation cascade after a pro-thrombotic trigger . PC activation following a thrombin challenge is reduced by 95% in an EPCR deficient mice [40] and by 92% in mice expressing a mutated EPCR unable to bind PC [14] . Similarly , in baboons , the blockade of PC-EPCR interaction with an antibody results in a 87% decrease of aPC generation following the infusion of thrombin [12] . As a consequence , in all the above-referenced animals models , any thrombotic challenge–such as a Gram negative systemic infection—induces the extensive thrombosis of several organs and death [11 , 12 , 14 , 40 , 41] . As severe meningococcemia is associated with high levels of circulating endotoxin and pro-inflammatory cytokines that trigger an important activation of the coagulation cascade , the decrease of aPC induced by the bacterial colonization of skin microvessels is a likely explanation of cutaneous purpura and purpura fulminans . Beyond the decrease of aPC generation , the loss of EPCR is also highly detrimental . Indeed , EPCR is key to the non-anticoagulant properties of aPC . aPC fixed on endothelial EPCR is able to cleave the Protease Activated Receptor PAR-1 at a non-canonical site , initiating barrier-protective , cytoprotective and anti-inflammatory signalings [31–38] . We demonstrate here that meningococcal adhesion on endothelial cells and the subsequent EPCR shedding inhibit the barrier protective effect of the aPC/EPCR/PAR-1 signaling . As this signaling counteracts endothelial barrier disruption during sepsis , it is tempting to speculate that its impairment could participate in refractory septic shock , capillary leakage and blood-brain barrier crossing that are common during meningococcemia . Thus , it arises from our data that meningococcus affects both the anticoagulant properties of aPC by limiting its activation , but also its broad barrier-protective , cytoprotective and anti-inflammatory properties by preventing its interaction with EPCR , cleaved upon meningococcal adhesion . We provide clear evidences that EPCR is cleaved by ADAM10 upon meningococcal adhesion , with no involvement of ADAM17 . To date , only ADAM17 was known as shedding EPCR [26] . However , it is now clear that ADAM17 and ADAM10 share common targets and that one protease can be activated independently from the other , depending on the initial trigger [29 , 42] . Our work gives a new insight into EPCR biology , and the respective roles of ADAM17 and ADAM10 in pathological contexts involving EPCR remains to be clarified . It is also interesting to point out that ADAM10 has several other targets whose shedding may contribute to N . meningitidis pathogenesis . This protease is notably able to cleave endothelial VE-Cadherin , a key component of endothelial intercellular junctions and a critical determinant of barrier integrity [43–45] . Interestingly , Staphylococcus aureus alpha-hemolysin ( Hla ) has been shown to bind to and to activate ADAM10 [46] . This activation induces VE-Cadherin cleavage responsible for a capillary leakage in a mouse model of Hla intoxination [47] . Therefore , it is likely that ADAM10 activation upon N . meningitidis vascular colonization induces VE-cadherin cleavage and participates to the capillary leakage and eventually to the crossing of the blood-brain barrier by the bacteria . Type IV pilus is the critical determinant of the meningococcal interaction with endothelial cells . It is essential to the initial adhesion on cells and to subsequent vascular colonization [21] . We demonstrated that pilus interaction with endothelial cells is also required for ADAM10 activation and EPCR shedding . Our data bring out a new cellular response triggered by meningococcal pilus . ADAM10 has a prominent role in several physiological and pathological pathways such as Notch signaling , embryonic development , tumor resistance to anticancerous agents or protection against Alzeihemer’s disease [48] . As such , its has been extensively studied but many questions remain unanswered to date . The mechanism by which it switches form an inactive state to an active state by a change of conformation has been described very recently [49] . But the precise cellular mechanisms that triggers this changing of conformation are still not understood . Therefore , further work is needed to decipher the mechanism by which ADAM10 is activated following pilus-mediated adhesion of N . meningitidis . This work , by identifying for the first time a specific dysregulation of the coagulation induced by N . meningitidis , opens therapeutics perspectives . Indeed , human recombinant aPC was a licensed drug , approved in 2001 with a broad indication of severe septic shocks . It was intended to improve survival due to the multiple effects of aPC [50] . After a promising preliminary study , the drug failed to confirm efficiency in several subsequent controlled trials [51 , 52] and was sometimes associated with an increased risk of serious bleeding . The drug was subsequently withdrawn from the market in 2011 but the real benefit of the drug is still a matter of debate [52–55] . However , it should be pointed out that recombinant aPC has never been evaluated in the specific context of meningococcal purpura fulminans , in contrast to extensive data with the non-activated zymogen [15 , 56–65] . Furthermore , the clinical endpoint of the aPC trials was sepsis-induced mortality and not thrombosis sequelae . Our data raises the question whether patients suffering from meningococcal invasive infection could benefit from an aPC treatment to prevent or limit thrombosis and thrombosis sequelae . In summary , N . meningitis endothelial colonization mediated by type IV pili triggers ADAM10 activation and subsequent EPCR shedding that impairs the generation of aPC . All three events could participate—and synergize—in meningococcal induced vascular injury , capillary leakage , fluid loss , and the bacterial crossing of the blood-brain barrier . Despite active antimicrobials and continuous improvements in intensive care , meningococcus purpura fulminans is still associated with a poor outcome and severe thrombosis sequelae such as amputations . Therefore , our work set up a basis for the development of new therapeutics based on ADAM10 inhibition and/or aPC supplementation alongside with antibiotics and traditional intensive care .
Human Dermal Microvessels Endothelial Cells ( HDMEC ) were purchased from Promocell and grown in Endothelial Cell Medium MV ( Promocell ) containing 5% Fetal Calf Serum ( FCS ) and endothelial cell growth supplements at 37°C/5%CO2 . Human Cerebral Microvessels Endothelial Cells ( hCMEC/D3 ) were a gift from P . O . Couraud at Institut Cochin , Paris , France , and were grown in Endothelial Cell Basal Medium-2 ( Lonza ) supplemented with 5% of FCS , 1 . 4 μM hydrocortisone ( Lonza ) , 5 μg/ml ascorbic acid ( Lonza ) , 1 ng/ml b-FGF ( Lonza ) , at 37°C in 5% CO2 . A piliated capsulated serogroup C N . meningitidis clinical strain 2c4 . 3 was used in this study [66] . An isogenic non-piliated ΔpilE derivative of strain 2c4 . 3 was previously described [66 , 67] In order to test the specific role of T4P we used a previously described isogenic derivative of strain 2c4 . 3 which is non piliated ( ΔpilE ) , and non capsulated ( ΔsiaD ) [23] . This strain is unable of pilus-mediated adhesion but can interact with cells via Opa proteins , a family of outermembrane proteins that mediate adhesion through their interaction with CEACAMs ( Carcino Embryonic Antigen-related Cell Adhesion Molecule ) receptors [68] . It should be pointed out that this interaction between Opa and CEACAMs occurs only if bacteria are non-capsulated . As the Opa-mediated adhesion on endothelial cells is weaker than that induced by T4P , cells were infected with a multiplicity of infection ( MOI ) twenty times higher than that used when a WT piliated strain was used for infection . Under such conditions , meningococcal interaction with endothelial cells is similar to that observed with the conditions used when infecting with a WT strain . Strains were grown on gonococcal-broth agar with Kellogg’s supplements at 37°C/5%CO2 . Before infection bacteria were grown in cell medium for 2 hours to reach the growth exponential phase . Cells were infected with a Multiplicity Of Infection ( MOI ) of 25 bacteria per cell unless specified . When needed , Tapi-0 ( 25μM , Merck Millipore ) or GI254023X ( 1 μM , Bio-Techne ) were added 1h before infection . After infection , cells were washed twice in Phosphate-Buffered-Saline ( PBS ) , trypsinized , fixed in 4% PFA for 10 minutes , washed twice in PBS and kept at 4°C until staining . Cells were stained for 1 hour with the appropriate primary antibody at 4°C in PBS/Bovine Serum Albumine ( BSA ) 1% and washed twice in PBS-BSA before FACS analysis . The following antibodies were used: EPCR ( clone RCR-252 , PE-conjugated , BD Biosciences ) , Thrombomodulin ( clone 1A4 , PE-conjugated , BD Bioscience ) , ADAM17 ( clone 111633 , PE-conjugated , R&D Systems ) , and ADAM10 ( clone 163003 , PE-conjugated , R&D Systems ) . Isotype controls were chosen accordingly . Data were acquired using a BD LSR Fortessa instrument ( BD Biosciences ) and analyzed using the FlowJo Software version 10 . A minimum of 5 000 cells were acquired for each experiment . For immunofuorescence assays , cells were grown on glass coverslips coated with 5 μg/cm2 of rat tail collagen type I ( Corning ) until confluence . After infection cells were fixed in 4% PFA for 5 minutes , washed twice in PBS , and incubated 15 minutes in PBS/BSA 1% . EPCR staining was performed first with no cell permeabilization ( polyclonal goat antibody from R&D Systems ) . Cells were then washed twice in PBS and permeabilized with Triton X-100 ( 0 . 1% Sigma ) . VE-Cadherin staining was performed after permeabilization ( polyclonal rabbit antibody from eBioscience ) . After staining cells were washed twice in PBS and incubated with secondary antibodies ( Alexa-fluor conjugated , invitrogen ) and DAPI ( Invitrogen ) . After additional washings , coverslips were mounted in mowiol ( Biovalley ) . Images acquisition was performed on a laser-scanning confocal microscope ( Leica TCS SP5 ) . Images were collected and processed using the Leica Application Suite AF lite ( Leica ) software . After infection , the supernatants were collected and centrifuged at 4000 rpm for 10 minutes at 4°C . Soluble EPCR was dosed using an ELISA commercial kit ( R&D Systems ) . For each experiment , a standard curve was generated using the highly purified human EPCR provided with the kit , from 0 . 313 ng/mL to 20 ng/mL following the manufacturer’s instructions . Each point was dosed in duplicate . Data were analysed using a 4-Parameters Logistic Regression and the online software www . elisaanalysis . com . The limit of detection and the limit of linearity using this kit is then of 0 . 313 ng/mL . All the cell supernatants had a sEPCR concentration superior to 0 . 313 ng/mL . The specificity of the capture antibody from the kit was tested using a whole cell lysate of Human Dermal Microvessels Endothelial Cells ( 10 μg/lane ) and a Western-Blot analysis ( antibody concentration: 0 . 5 μg/mL ) . The capture antibody detected a unique band of approximately 48 kDa which is the molecular weight of the glycosylated EPCR . Ambion Silencer Select siRNA targeting ADAM17 , ADAM10 or control siRNA were purchased from ThermoFisher Scientific ( references S13720 , S1006 and 4390843 respectively ) . HDMEC cells were transfected with siRNA ( 2 μM ) using AMAXA transfection system ( Lonza ) , Human Umbilical Vein Endothelial Cells ( HUVEC ) transfection kit ( Lonza ) and program HUVEC-OLD , according to manufacturer’s protocol . Cells were infected 3 days after transfection . CRISPR/Cas9-mediated genome editions were conducted using Dr . Feng Zhang Lab CRISPR tools and protocols ( http://www . genome-engineering . org and Cong et al [69] ) . The following sequences were used as target and cloned into pSpCas9 ( BB ) -2A-GFP ( PX458 ) plasmid , a gift from Feng Zhang ( Addgene #48138 ) : ADAM17 ( GTCGCGGCGCCAGCACGAA ) , ADAM10 ( CGTCTAGATTTCCATGCCCA ) . The efficiency of each construct to cleave target sequences was first analyzed in HEK cells by a T7 Endonuclease I assay using the Gene Art Genomic Cleavage Detection kit ( ThermoFisher Scientific ) according to the manufacturer’s protocol . hCMEC/D3 cells were transfected with the constructed plasmids ( 1 μg/100μL ) using AMAXA transfection system ( Lonza ) , HUVEC transfection kit ( Lonza ) and program HUVEC-OLD . The day after , cells were trypsinized and suspended in cold HBSS/FCS ( 2% ) . GFP-positive cells were selected using an Aria III cell sorter ( BD Bioscience ) . The whole GFP-positive populations were grown for two weeks and ADAM17 or ADAM10 negative cells were then selected using the Aria III cell sorter . The whole negative populations were grown , amplified and used for the experiments . The absence of ADAM17 or ADAM10 expression was also verified during each experiment . The protocol was adapted from a previously published paper [10] . Cells were grown in 1 . 9 cm2 wells until confluence . After infection , the monolayer was washed twice with Hank’s Balanced Salt Solution ( HBSS ) supplemented with 0 . 1%BSA , 3 mM CaCl2 and 0 . 6 mM MgCl2 before a 5-minutes incubation with 200 nM Protein C ( Enzyme Research Laboratories ) in a total volume of 200 μL of the same buffer at 37°c under gentle agitation . Activated thrombin ( 10 nM , Diagnostica Stago ) was added to initiate the reaction . After 30 min , 150 μL of the supernatant was collected and antithrombin ( 1 . 8 μM , Aclotine from Laboratoire de Fractionnement et des Biotechnologies—LFB , Courtaboeuf , France ) and heparin ( 90 mU/mL , from Sanofi-Aventis , France ) were added to stop the reaction . The activity of aPC was determined using 200 μM of CS-21-66 ( Hyphen Biomed ) as chromogenic substrate and a Mithras LB940 plaque reader ( Berthold Technologies ) . All aPC determinations were done in duplicate . Endothelial cell permeability was measured in real time using the iCELLigence System ( Acea Biosciences ) that determines the changes in transendothelial resistance by electric-substrate impedance sensing ( ECIS ) . HDMEC ( 50 000 / well ) were seeded in E-Plate L8 wells containing gold electrodes ( Acea Biosciences ) . The Cell Index ( CI ) that reflects the ECIS was monitored every 20 minutes for 48 hours until complete stabilization . Endothelial cells were infected for 4 hours or left non-infected . After infection , the cell medium was removed to get rid of soluble EPCR and replaced by fresh medium without heparin . Cells were then treated for 2 hours with Activated Protein C ( 50 nM or 100 nM , Lilly ) or left untreated . After 2 hours , the CI were normalized and recorded every minute . Thrombin ( 1 nM , Diagnostica Stago ) was added in each well . For comparisons , the maximal loss of CI induced by thrombin in cells not treated by aPC was set to 100% .
|
Neisseria meningitidis ( meningococcus ) is responsible for a severe syndrome called purpura fulminans in which the coagulation system is totally dysregulated , leading to an extensive occlusion of blood microvessels . The pathogenesis of this syndrome is still not understood . Here we show that the meningococcus , when adhering on the apical surface of endothelial cells , induces the activation of membranous protease named ADAM-10 , which in turn hydrolyses a cellular receptor called EPCR . The latter is key for the activation of a circulating potent anticoagulant , the Protein C ( PC ) . PC activation is then impaired following meningococcal adhesion on endothelial cells . This work unveils for the first time a specific dysregulation of coagulation induced by the meningococcus and potentially identifies new therapeutic targets for meningococcal purpura fulminans .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"medicine",
"and",
"health",
"sciences",
"immune",
"cells",
"pathology",
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"laboratory",
"medicine",
"gene",
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"endothelial",
"cells",
"pathogens",
"antigen-presenting",
"cells",
"immunology",
"microbiology",
"cardiovascular",
"medicine",
"thrombosis",
"epithelial",
"cells",
"bacterial",
"diseases",
"physiological",
"processes",
"bacteria",
"bacterial",
"pathogens",
"small",
"interfering",
"rnas",
"infectious",
"diseases",
"coagulation",
"disorders",
"neisseria",
"molting",
"thrombin",
"animal",
"cells",
"neisseria",
"meningitidis",
"medical",
"microbiology",
"proteins",
"gene",
"expression",
"microbial",
"pathogens",
"biological",
"tissue",
"meningococcal",
"disease",
"hematology",
"biochemistry",
"rna",
"blood",
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"cell",
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"vascular",
"medicine",
"organisms"
] |
2018
|
An ADAM-10 dependent EPCR shedding links meningococcal interaction with endothelial cells to purpura fulminans
|
Sequence database searches require accurate estimation of the statistical significance of scores . Optimal local sequence alignment scores follow Gumbel distributions , but determining an important parameter of the distribution ( λ ) requires time-consuming computational simulation . Moreover , optimal alignment scores are less powerful than probabilistic scores that integrate over alignment uncertainty ( “Forward” scores ) , but the expected distribution of Forward scores remains unknown . Here , I conjecture that both expected score distributions have simple , predictable forms when full probabilistic modeling methods are used . For a probabilistic model of local sequence alignment , optimal alignment bit scores ( “Viterbi” scores ) are Gumbel-distributed with constant λ = log 2 , and the high scoring tail of Forward scores is exponential with the same constant λ . Simulation studies support these conjectures over a wide range of profile/sequence comparisons , using 9 , 318 profile-hidden Markov models from the Pfam database . This enables efficient and accurate determination of expectation values ( E-values ) for both Viterbi and Forward scores for probabilistic local alignments .
Sequence similarity searching was advanced by the introduction of probabilistic modeling methods , such as profile hidden Markov models ( profile HMMs ) and pair-HMMs [1] . When parameters are probabilities rather than arbitrary scores , they are more readily optimized by objective mathematical criteria . This enables building more complex , biologically realistic models with large numbers of parameters . For example , profile HMMs use position-specific insertion/deletion probabilities in place of the arbitrary , position-invariant gap costs of more traditional approaches such as BLAST or PSI-BLAST [2] , and this allows profile HMMs to model the fact that indels occur more frequently in some parts of a protein more than others ( e . g . , in surface loops as opposed to buried core ) [3] . More sophisticated scoring models are desirable but not sufficient . It is also necessary to be able to determine the statistical significance of a score efficiently and accurately [4] , [5] . One reason that the BLAST suite of programs [2] , [6] is so useful is that BLAST introduced a robust theory for evaluating the statistical significance of local alignment scores , widely known as Karlin/Altschul statistics [7]–[9] . Although the scoring technology in HMM-based profile search and profile/profile search methods is generally an improvement over BLAST and PSI-BLAST [10] , [11] , some problems in determining statistical significance of homology search scores have impeded the development and adoption of these or other more complex models and methods [12] . There are two main problems . The first problem is that Karlin/Altschul statistics only rigorously apply to scores of optimal ungapped alignments using simple position-independent scoring systems . In this case , alignment scores follow a Gumbel distribution with slope parameter λ and location parameter K [7] , and both parameters are readily calculated for any given scoring system [7] , [13] . In the more relevant case of optimal gapped local alignments , although scores empirically still follow a Gumbel distribution for a useful range of gap costs [14] , the key Gumbel λ parameter must be estimated by expensive computational simulation for each new scoring system [9] . Much effort aims to find better ways of determining λ [15]–[24] . For traditional pairwise comparison methods ( e . g . BLAST ) , using computational simulations to determine λ is not a major limitation . BLAST precalculates Karlin/Altschul parameters K and λ for the small number of general scoring systems in common use [2] . However , for position-specific profile scoring models like PSI-BLAST or profile HMMs , each query specifies a customized scoring system , requiring its own K and λ . PSI-BLAST avoids using simulations to determine λ by restricting its profiles to fixed position-invariant gap costs , and assuming ( backed by empirical results ) that the λ of a PSI-BLAST profile is equal to the λ of the pairwise scoring system with the same gap costs and the most similar relative entropy ( average score ) per aligned residue pair [2] . For models with position-specific gap penalties , though , such as the HMMER profile HMMs used by protein domain databases like Pfam [25] and SMART [26] , each model still requires a relatively expensive “calibration” by simulation before accurate E-values can be obtained . This lack of computational efficiency particularly hampers the use of profile HMMs in iterative database searches , where each iteration produces another model that needs calibration . The second problem is that in terms of probabilistic inference , an optimal alignment score is not the score we should be calculating in a homology search . The quantity we want to calculate is the total log likelihood ratio for the target sequence ( s ) given an evolutionary model and a null hypothesis , independent of any particular alignment . The alignment is uncertain , a so-called “nuisance variable” in the inference that one wants to marginalize ( integrate out ) . In closely related sequences , when the alignment is well determined , the optimal alignment score will approximate the total log likelihood ratio , but the more uncertain the alignment , the more the optimal alignment score and the total log-likelihood ratio differ , so remote homology detection ( where alignments are most uncertain ) is most affected by the approximation . Benchmarks of profile HMM sensitivity and specificity have shown that “Viterbi” scores ( optimal alignment ) are significantly outperformed by “Forward” scores ( total log likelihood ratios , summed over all alignments ) [27] . However , Karlin/Altschul statistics do not apply to Forward scores , and are not expected to [28] . The distribution that Forward scores follow had been unknown [28] , [29] . Forward score distributions have been empirically fitted to various fat-tailed distributions [29] , but with unsatisfactory accuracy . Here I test two conjectures about the expected distributions of scores for full probabilistic models: that optimal gapped alignment scores ( Viterbi scores ) follow Gumbel distributions with a constant λ ( just as in the ungapped alignment case ) and that the expected distribution of total log likelihood ratio scores ( Forward scores ) asymptotes to an exponential tail with the same constant λ . I use simulations to show that these conjectures hold for all the models in the current Pfam database ( 9318 profile HMMs ) . In achieving these results , I modified the architecture and parameterization of profile HMMs used by HMMER [30] .
Let us start with a definition of Viterbi and Forward scores in terms of probabilistic inference . We have a query ( either a single sequence or a multiple alignment ) , and we want to ask if a target sequence x is homologous to our query or not . To set up a hypothesis test , we specify “homology to the query” as a hypothesis ( call it H ) to be compared to ( at least ) one alternative hypothesis , that x is an unrelated sequence ( call this hypothesis R , random ) . To apply probabilistic inference , both hypotheses are specified as full probabilistic models , which means that they describe probability distributions P ( x|H ) and P ( x|R ) , such that and over all possible target sequences x = x1…xL of length L = 1…∞ . H and R would typically be generative stochastic models such as hidden Markov models ( HMMs ) or stochastic context-free grammars ( SCFGs ) [1] . ( Note that this does explicitly define a homology search , not merely a similarity search [31] . ) Typically , model H will generate target residues aligned to ( homologous to ) residues in the query , along with deletions and insertions relative to the query , so its scoring model depends on an alignment of the query to the target . That is , model H directly expresses a joint probability distribution P ( x , π|H ) , where π represents a particular alignment . To obtain the probability P ( x|H ) , we marginalize the unknown nuisance variable π; that is , we sum over all possible alignments , . A model might require the complete query and target sequences to be aligned and homologous – a global alignment model . Because biological sequences often only share homologous domains , it is more useful for H to permit any subsequence i…j of the query to align to any subsequence k…l of the target , while treating the remainders of the sequences as nonhomologous – this defines a local sequence alignment model . The simplest random model R is a one-state HMM that generates sequences with each residue drawn from a background frequency distribution . This is the usual independent , identically distributed background model used when calculating standard log-odds scoring matrices , plus a geometric length distribution . In this case , there is only one possible alignment to the target sequence , and P ( x|R ) is obtained directly . The likelihoods of H and R can be used to define at least two different log likelihood ratio scores for a target sequence x . The Viterbi score V is the score of the optimal alignment π̅: The Forward score F is obtained from the total likelihood of model H , a sum over all possible alignments: The logarithms may be taken to any base z . By convention , HMMER reports scores in units of bits , log base z = 2 . Because both scores are log likelihood ratios , I will be careful to refer to Viterbi versus Forward scores , or to optimal alignment scores versus “total log likelihood ratio” scores . The names Viterbi and Forward refer to the standard dynamic programming algorithms used to calculate these scores in the specific case of HMMs [1] . Other probabilistic models have differently named algorithms ( CYK and Inside for stochastic context-free grammars for RNA analysis , for example [1] , [32] ) , but here I will use the shorthand V and F to represent optimal alignment scores and total log likelihood ratio scores in general . Traditional search algorithms report optimal alignment scores , so the Viterbi score is the probabilistic analog of traditional methods . However , from a probabilistic inference standpoint , the Forward score is what we want , because we are after the probability that sequence x is a homologue of the query – that is , the posterior probability of model H given data x , P ( H|x ) [33] , [34] . The posterior is a sigmoid function of F:where ρ is a constant offset , the prior log odds ratio . Forward scores are not generally used in traditional sequence comparison , because they only make sense if individual alignments have probabilities P ( x , π|H ) that can be meaningfully summed . Forward scores cannot be calculated directly for arbitrary ( nonprobabilistic ) scoring systems , except by using approaches based on renormalization and partition functions , where the arbitrary scores are assumed to be unnormalized log probabilities [28] , [35]–[38] ) . Local optimal alignment scores of random sequences ( V scores ) are expected to follow Karlin/Altschul statistics [7] , [14] , a special case of a Gumbel distribution ( a type I extreme value distribution ) [39]:where μ and λ are location and scale parameters . Karlin/Altschul statistics give a specific dependence of μ on query and target sequence lengths N and L , , with parameter K essentially representing the fraction of the NL residue alignment lattice that is available for initiating independent local alignments . I will use the more general Gumbel notation ( in terms of μ , λ ) as opposed to the more usual Karlin/Altschul notation ( in terms of KNL , λ ) for reasons that will become clear when I consider how score distributions depend on target sequence length . In contrast to optimal alignment scores , the distribution of Forward scores is unknown . It has appeared “fat-tailed” relative to the high-scoring exponential tail of the Gumbel distribution of Viterbi scores [28] , [29] . I made the following two conjectures about V and F scores , in the case of full probabilistic models of local sequence alignment: These conjectures are based on three main lines of argument , two of which depend heavily on the work of Bundschuh and his collaborators . First , for Viterbi scores , Bundschuh's “central conjecture” about the distribution of optimal gapped local alignment scores states that λ for the Gumbel distribution is the unique positive solution of in the limit of infinite length comparisons [22] , [23] . There is a strong analogy to the case of ungapped local alignments with additive pairwise residue scores σab , where λ is the unique positive solution of [13] . When the residue scores σab are explicitly probabilistic log-odds scores ( in some arbitrary logarithm base z ) then simple algebra shows that λ for ungapped alignment scores is log z . Likewise Bundschuh's central conjecture would be satisfied by λ = log z for full probabilistic models of local alignment , when indels are included as part of the probability model rather than scored with arbitrary penalties . Second , for Forward scores , Milosavljević proved in his “algorithmic significance” method that an upper bound for the distribution P ( F>t ) of log likelihood ratios F for full probabilistic models is an exponential e−t log z [40] , [41] . Although this is not a tight bound , it suggests the high-scoring tail cannot be fatter than exponential , and that if it were exponential , it must have λ≥log z . Third , for Forward scores , Yu , Bundschuh , and Hwa argued by a different approach that the high-scoring tail P ( F>t ) for scores for probabilistic sequence alignment is likely to be approximated by e−t log z , i . e . again , an exponential tail with λ = log z [42] . However , they only used this result as an intermediate in a derivation showing that the scores of a new “hybrid” scoring system for local alignment would probably be Gumbel-distributed with λ = log z . They stated their approximation in the context of a full probabilistic model of global alignment , not local , and then used that result to derive a further approximation for the expected distributions of scores for a nonprobabilistic model of local alignment . However , I believe their approximation only relies on the model being fully probabilistic , not whether it is of global or local alignment . Additionally , one expects the high-scoring tail of Forward scores to approximate the high-scoring tail of Viterbi scores ( so Gumbel-distributed Viterbi scores and exponential-tailed Forward scores would have the same λ ) , because for the highest scoring sequences , the optimal alignment should contain most of the probability mass . In practice , however , the simulation-calibrated λ values for bit scores of Gumbel distributions fitted to Viterbi scores of HMMER2 multihit local alignment models for 9318 Pfam 22 . 0 models have a mean of 0 . 6677 , with a standard deviation of 0 . 051 ( ±8% ) , and a range of 0 . 517 to 1 . 337 . Though the mean is suggestively close to the conjectured log2 = 0 . 6931 , the variation is unacceptably broad , well outside traditional tolerance for useful λ estimates ( which is typically considered to be ≤3% error [20] ) . Similarly , another popular profile HMM software package , SAM [3] , [43] , has used λ = log z in the past , but switched to simulated-calibrated λ values because they gave better statistical significance estimates [29] . Either something is wrong with the conjectures , or something is not quite right with profile HMMs of local alignment . I modified HMMER's profile HMM architecture in several details , with the main goal of achieving a uniform query entry/exit distribution in local alignments . A uniform query entry/exit distribution means that for a query profile of N positions 1…N , each choice of local alignment to a core model subsequence i…j ( leaving query prefix 1…i−1 and suffix j+1…N unaligned ) has the same probability: , since there are possible choices of i…j . This assumption is implicit in the traditional Smith/Waterman alignment scoring system [44] , which scores identically ( zero ) for any choice of entry i and exit j , therefore corresponding to an implicit assumption of a uniform query fragment distribution ( albeit unnormalized ) . HMMER's previous entry/exit distribution , in contrast , was ad hoc and non-uniform , causing scores to be biased by the local alignment's position in the query model . I guessed that a uniform entry/exit distribution might result in simpler , more statistically homogeneous expected score distributions that might asymptotically approach conjectured predictions faster than for nonuniform entry/exit distributions . Besides HMMER's previous model , several other probabilistic local alignment models in the literature also imply nonuniform entry/exit distributions . For example , simple pair-HMMs for pairwise local sequence alignment imply a non-uniform ( geometric ) distribution over local alignment length , because they use a single residue alignment state with a self-loop and an exit probability [1] . In standard profile HMMs , I see no way to specify a uniform entry/exit distribution when delete states are present , at least not while maintaining a fully probabilistic model . The generative probabilistic model of local alignment that I intend to use in HMMER3 is illustrated in Figure 1 . Figure 1A shows the core model , which is a standard profile HMM essentially following the original formulation of Krogh et al . [3] . This is a model of global alignment to the original query ( a multiple alignment or single sequence ) . The parameters in the core model ( M and I residue emissions , and M , D , and I state transitions ) are estimated from counts of residues and indels in the query . Details of model construction and parameter estimation in the core model follow previous work on profile HMMs , and are not particularly relevant to the results reported here except as noted . Figure 1B shows the search profile , which adds extra states and state transitions to the core model to describe various kinds of alignment modes , including local versus glocal and unihit versus multihit . For locality with respect to a query segment , there are transitions from the begin state to any match state , and exits from any match or delete state to the end . For locality with respect to a target sequence segment , the search profile generates flanking unannotated segments of the target using N and C states . For a “multihit” mode , to generate multiple consistent alignments to the same query in one target sequence ( either multiple domains of the same type , or separate pieces of one alignment ) , the model may cycle from E to the J state , generate an unannotated segment in J , and cycle back to B . The N , C , and J states are all assumed to emit residues with the same background frequencies as in null model R , so their log-odds emission scores are zero . This is essentially the same as the HMMER2 “Plan 7” profile architecture , but as it cannot be parameterized to achieve a uniform entry/exit distribution , the following step was taken . Figure 1C shows the implicit probabilistic model . To achieve a uniform entry/exit distribution , we imagine replicating all N ( N+1 ) /2 possible chunks i…j of the model , and assigning an entry probability of 2/N ( N+1 ) and exit probability of 1 . 0 to each of these fragments . Except for these entry/exit probabilities , all other emission and transition probabilities are the same as in the search profile . Now we have a probabilistic model with a uniform entry/exit distribution , but the model is enormous . Dynamic programming on the implicit probabilistic model would be costly . A key observation is that dynamic programming on the search profile with entry probabilities set to 2/N ( N+1 ) and exits to 1 . 0 is provably equivalent to doing dynamic programming on the implicit probabilistic model . Two conditions are sufficient to make this so: first , that there is a one-to-one correspondence between the sets of possible state paths in the two models , and second , that any given state path is assigned identical probability by either model . ( The state transition schemes in the search profile and the implicit probabilistic model were carefully designed to fulfill these conditions . ) Therefore dynamic programming on one model to find either the optimal state path or the sum over all state paths must give the same answer as the other model would . This holds so long as the probability of entering at i is independent of exit point j , which is true for a uniform entry distribution . Therefore , the search profile is not probabilistic per se . It is a dynamic programming construct that calculates correct probabilities for the implicit probabilistic model . It uses entry probabilities of 2/N ( N+1 ) and exit probabilities of 1 . 0 that are properly normalized with respect to the state diagram for the implicit probabilistic model , not the state diagram for the search profile . The N , C , J state transitions , plus the self-loop transition in the null hypothesis HMM R , comprise the target length model , so-called because this parameterization largely controls the expected length of the target sequence . For simplicity , the target length model is expressed in terms of three parameters p , q , and r . p is the self-loop transition probability for N , C , and J , so it controls the length of unannotated segments; parameterizing these states identically corresponds to an assumption that prefixes , suffixes , and intervening unannotated regions have identical length distributions . q is the E→J transition probability of looping around for another pass through the core model , controlling the expected number of homologous domains per target sequence ( q = 0 puts the model in a unihit mode , and q>0 is a multihit mode; I will only use q = 0 . 5 here ) . r is the self-loop transition for null model R's single HMM state , controlling the length distribution generated by R . How should the three target length model parameters be set ? I will discuss the rationale in more detail in a later section , in the context of illustrative simulation results . For now I will just state that , q = 0 , and in unihit modes , and , q = 0 . 5 , and in multihit modes . That is , these model parameters are recalculated for each target , according to its length L: both H and R are conditional on L . With these choices , models H and R will both generate approximately the same mean target sequence length L . Previously HMMER2 used ( and the same q = 0 or q = 0 . 5 choice of unihit versus multihit mode ) , independent of target sequence length . Recalculating part of the scoring system based on each target sequence's length is an unusual step , but the reason to condition the hypothesis test ( both models H and R ) on target length L will become apparent . Traditional sequence similarity search methods distinguish local , global , and glocal alignments , applying different alignment algorithms , while using the same scoring system . ( A glocal alignment , also known as a semi-global alignment [45] , is global with respect to the query 1…N , and local with respect to a subsequence k…m of the target; glocal alignment is useful , for example , when a profile HMM models a protein structural domain that may occur one or more times somewhere in a longer , multidomain protein sequence . ) Additionally , local and glocal algorithms may allow only one aligned region per target sequence ( a unihit alignment ) , or they may allow a combination of one or more aligned regions ( a multihit alignment ) . The Smith/Waterman alignment algorithm [44] is a unihit algorithm , for example , whereas BLAST is multihit , implementing “sum statistics” to allow multiple consistent hits to contribute to a target's score [8] . In a probabilistic inference framework , these distinctions are not in the algorithm , but in the parameterization and architecture of the model H . A full ( generative ) probabilistic model H must always explicitly model the complete target sequence x1…xL , not just part ( s ) of it . This is why the HMMER model includes additional states and transitions that account for unannotated residues in the target sequence , and transitions allowing a model to loop back and generate one or more consistent alignments to the core model in the same target . Thus , an alignment π to a probabilistic model is always complete ( and in some sense “global” ) in that every residue xi in the target is assigned to a state in the model . The HMM algorithms used to score and align target sequences ( Viterbi and Forward ) are always the same , regardless of the configuration of the model . In HMMER , searches can be configured in any choice of local , glocal , or global combined with a choice of unihit or multihit , a total of six different standard alignment modes , by reparameterizing the entry/exit distribution and the target length distribution . I only explore local alignment modes in this paper , and I generally concentrate on multihit rather than unihit mode because multihit mode is more powerful . Viterbi bit scores are predicted to be Gumbel distributed with parametric λ = log 2 . To test this prediction on many different profile HMMs , I estimated ( represents a maximum likelihood estimate fitted to a finite sample of scores , as distinguished from the parametric true λ ) for 9 , 318 different profile HMMs built from Pfam 22 . 0 seed alignments , by collecting multihit local Viterbi score distributions for n = 105 i . i . d . random sequences of length 400 generated with the same residue frequencies as the null model R . Figure 2 shows the results of maximum likelihood fitting these scores to Gumbel distributions . The 9 , 318 estimates are tightly clustered with mean 0 . 6928 , consistent with the conjecture that λ = log 2 = 0 . 6931 . As examples , the top right of Figure 2 shows the score distributions for two typical Pfam models , for deep simulations with a 1000-fold larger sample size ( 108 random sequences ) . As “typical” models , I chose RRM_1 and Caudal_act from Pfam 22 . 0 . The RRM_1 model is the RNA recognition motif , a ∼72 residue domain , chosen because it is one of the Pfam domains I am most familiar with . The Caudal_act domain is the activation domain of the Caudal-like homeobox transcription factors , chosen because it is literally typical for Pfam , being closest to the median of Pfam seed alignments in three different characteristics: number of seed sequences ( Pfam 22 . 0 median = 9; Caudal_act = 9 ) , model length ( Pfam median = 147; Caudal_act = 147 ) , and average pairwise identity ( Pfam median 36% , Caudal_act = 37% ) . Both observed distributions show good agreement to the predicted Gumbel of λ = log 2 . I examined outliers in to look for models for which the conjectured λ = log 2 fails . If the 9318 trials were all truly Gumbel distributed with λ = log 2 , ratios ( parametric over maximum likelihood estimate ) should be normally distributed around a mean of 1 . 0 with standard deviation 0 . 0025 ( , [46] ) , so in 9318 trials , values should range from about 0 . 687 to 0 . 700 ( ±3 . 7 s . d . ) . The observed ratios do show a mean close to 1 . 0 ( 1 . 0008 ) , but an s . d . of 0 . 0167 ( six-fold higher than expected ) , and the 's range from 0 . 5828 to 0 . 8368 . This suggests source ( s ) of variation beyond expected noise of fitting finite samples , and that both low and high outliers are more frequent than expected . The bottom right of Figure 2 shows multihit local Viterbi score distributions for the most extreme high and low outliers , Sulfakinin and DUF851 , for deep simulations ( 108 random L = 400 sequences ) . In both cases , a similar is reproduced in the second ( and deeper ) simulation , more evidence that these outlying values are not the result of expected statistical variation in estimation . The low outlier DUF851 ( and all other low outliers I examined ) actually fits better visually to the conjectured λ = log 2 than to the maximum likelihood fitted . Low outliers are invariably models where the sequences in the seed alignment are highly identical . This discretizes the model's alignment scores ( emission probabilities all converge to 1 . 0 for all consensus residues , regardless of residue type or model position ) leading to a non-smooth score distribution ( a stairstep-like effect is often seen , corresponding to local alignments of increasing discrete lengths 1 , 2 , 3… ) , and this stairstep gets misfit by maximum likelihood estimation . Low information content models ( parameterized by entropy weighting , described later ) do not show such outliers ( not shown ) . Thus , for low outliers , the error is attributed to artifacts of maximum likelihood fitting . The high outlier Sulfakinin ( and all other high outliers I examined ) does show a higher λ ( steeper slope ) than the conjectured log 2 . A distinctive feature of Sulfakinin compared to other Pfam models is that it is tiny , just N = 9 consensus positions long . All other high outliers examined were short models . Finite-length sequence comparisons are expected to show an “edge effect” that increases the apparent λ relative to an asymptotic theoretical prediction , and finite-length artifacts are maximal for the shortest query models [20] . A method for compensating for “edge effect” is discussed later in the paper . The Forward score distribution is predicted to converge to an exponential with λ = log 2 , with the approximation holding above some score threshold τ: Figure 3 shows the results of maximum likelihood fitted for exponential tails , for multihit local Forward scores of n = 500 , 000 i . i . d . random sequences of length 400 , as a function of fitted tail mass , for 9 , 318 Pfam 22 . 0 models . We expect a tradeoff between fitted tail mass and accuracy . Convergence to λ = log 2 is expected to occur as fitted tail mass decreases ( e . g . as threshold τ increases ) , but as τ increases , the number of fitted samples decreases , so the accuracy of fitting decreases . This tradeoff is seen in the data , with mean estimates closely approaching log 2 for tail masses of ≤0 . 001 or so . A tail mass of 0 . 001 was chosen as a reasonable tail mass for further characterization of Forward exponential tails . The top right of Figure 3 shows score distributions and expected λ = log 2 exponential distribution of the 0 . 001 tail for deep ( n = 108 ) simulations for the “typical” RRM_1 and Caudal_act Pfam models , showing that these fits are visually satisfactory . In this case , the survey of 9 , 318 models has limited power to detect significant outliers . Even with n = 500 , 000 scores , the 0 . 001 tail contains only 500 points , so estimates will exhibit substantial stochastic variation . is expected to be normally distributed with mean 1 . 0 and standard deviation 0 . 045 ( , [46] ) , and the absolute values are expected to range from about 0 . 590 to 0 . 840 ( ±3 . 7 s . d . ) . At the chosen tail mass of 0 . 001 , observed ratios have mean 0 . 9935 and s . d . 0 . 0473 , with absolute values ranging from 0 . 5949 to 1 . 0116 . The variance of the estimates is consistent with expected estimation error on the low side , but there appears to be a higher than expected frequency of large values . The lower right of Figure 3 shows score distributions of deep simulations for the most extreme low and high outliers , Ribosomal_L12 and XYPPX , and their expected exponential tails . In both cases ( and in other cases examined ) , deeper simulations change the estimates , bringing them closer to log 2 , suggesting expected statistical estimation error is responsible some of the discrepancies . However , for some models , including these two , still remain significantly different from log2; Ribosomal_L12 remains −11 s . d . and XYPPX +25 s . d . away from the expected 1 . 0 for ratios for exponential tails containing 105 scores . Some low outliers exhibit the same high-identity , discretized-scores , stairstepping-distribution artifact observed with the Viterbi low outliers ( DUF851 for example; not shown ) , but this explanation does not seem reasonable for Ribosomal_L12 , where the observed score distribution appears smooth . The Ribosomal_L12 discrepancy ( = 0 . 6688 differs from log 2 by 3 . 5% ) is small and can be neglected in practice , but it is worth noting theoretically , because the Milosavljević result suggests that λ<log 2 should not occur . The most obvious thing that is unusual about the Ribosomal_L12 seed alignment is that it has strongly biased residue composition . The high outlier XYPPX ( and some other high outliers examined ) remains a high estimate in the deeper simulation ( the observed 0 . 7519 is lower than the 0 . 8413 estimated in the smaller survey , but still +25 s . d . of expected given 105 scores in the deeper tail ) . As with the Viterbi scores , XYPPX and these other high outliers are unusually small models ( XYPPX is N = 5 consensus residues ) , and likely to be attributable to finite-length edge effect . So far , all target sequences have been a typical length of L = 400 residues . However , proteins range in length from a few residues to tens of thousands . One must be able to predict how the expected score distribution depends on target sequence length . For expected Gumbel distributions of traditional optimal local alignment scores , Karlin-Altschul statistics predicts that the location parameter μ scales as with query length N and target length L , and that the λ parameter ( aside from finite-length edge effects ) is independent of target length . That is , for each two-fold increase in target sequence length , the expected score distribution shifts by one bit . For the old target length model parameterization in HMMER2 ( in the target length model , such that all unannotated residues assigned to N , C , J states score zero , an explicit model of Smith/Waterman's implicit assumptions ) , the Gumbel distributions for multihit local Viterbi scores follow the specific target length dependence predicted by Karlin-Altschul statistics , as shown in the top left of Figure 4 for two typical models . Over a range of target sequence lengths from 25 to 25 , 600 residues in steps of two-fold , observed score distributions are spaced in steps of one bit . However , from a probabilistic inference standpoint , seeing the expected score increase with increasing target sequence length raises a red flag . The posterior probability P ( H|x ) should not increase as the length of a random target sequence increases . If anything , it should decrease . The more data are available ( the longer the target ) , inference should become more accurate , and the more certain we should be that a random sequence was generated by hypothesis R , not hypothesis H . This concern becomes a practical issue when multihit local Forward score distributions are examined for models using the HMMER2 target length model , as shown in the top right of Figure 4 . These score distributions shift unpredictably , and by more than one bit per target length doubling . In absence of theory describing this length dependence , one would have to empirically determine a different exponential tail location parameter τ for a range of different target lengths in order to assign accurate E-values to multihit local Forward scores . Although I show later that τ is not hard to estimate , this is not desirable . ( Unihit local Forward scores do scale by one bit per target length doubling; data not shown . ) A simple argument about the target length model appears to suffice to explain this behavior . Consider the length distribution generated by models H and R , given the length model parameters p , q , and r . The probability that model R generates a target sequence of length L is a geometric density:and the expected length generated by model R is: If we assume the length distribution of H is dominated by the N , C , J states and the target length model , and that the core model contributes negligible length ( an assumption that will be most true for local alignment modes and long L ) , then the probability that model H generates a sequence of length L is a sum of Pascal distributions:where the index j counts over the number of times we start a J segment . The expected length generated by model H can be derived from this , using the expectations for Pascal and binomial distributions: Intuitively , this follows from the fact that the expected number of times that we include a J segment is . Thus , counting the two segments emitted by the N and C states , the total number of unannotated segments is , each of which follows an independent geometric distribution with expected length . We can then approximate the component of the log-odds Forward score that is attributable to target length modeling alone: ( 1 ) In the case of unihit modes ( q = 0 ) , this becomes: ( 2 ) So , when p = r ( HMMER2's old parameterization ) , for unihit Forward scores , Equation 2 predicts that the target length model's score contribution will increase as log ( L+1 ) , essentially the same scaling for unihit local Forward scores that Karlin/Altschul statistics predicts for Viterbi ( optimal alignment ) scores . However , with p = r , for multihit local Forward scores , Equation 1 predicts that the length model's score contribution will scale as log ( L+1 ) at small L , but will increase more rapidly at larger L . Qualitatively , this appears to be the behavior observed in Figure 4 ( upper right ) . Intuitively , the problem is that under a target length model with p = r , model H favors longer sequences than model R , because there are at least two states ( N , C ) generating unannotated segments ( plus additional contribution from J states in multihit mode ) . The longer the target sequence , the more H is favored , simply because it generates longer sequences with higher probability than R . One way to “fix” this behavior would be to set p such that model H generates the same expected target length as model R . For example , in a unihit model , we might set , so that the N and C states each generate a mean length of 175 , adding up to the same “typical protein” mean length 350 that R generates . But setting any constant p and r still has problems , because the length model then becomes informative - target sequences of length ∼350 get higher scores than shorter or longer sequences - and this creates a nonlinear dependence of scores on log L . In general we probably want target length modeling to be uninformative , because target sequence lengths are unpredictable . For example , the target sequence may be a fragment , or a huge multidomain protein . How can we set an uninformative target length model ? One way to do this is to make the parameterization of models H and R conditional on the length of the target sequence L . That is , as each new target sequence is examined , model M and R are set on the fly to generate sequences of mean length L: Under this scheme , according to Equation 1 , the length model is predicted to contribute a nearly constant score , independent of target sequence length L . Empirically , using this scheme , expected score distributions indeed do become essentially target length independent ( Figure 4 , bottom ) over a wide range of lengths L , both for Viterbi and for Forward scoring , and whether the model is configured for unihit or multihit alignment modes . Target length independence is an important result . It not only means that single choices of location parameters μ and τ work for all lengths L; it also means that simulations that determine μ and τ can be done for a small L , further decreasing computational cost . For the expected Gumbel distribution of local Viterbi scores , the location parameter μ can be determined by a maximum likelihood Gumbel fit [46] to a small simulation . When λ is known , n = 200 Viterbi scores of random sequences of L = 100 suffices to determine μ with a standard deviation of 0 . 1 bits . This estimation error is within tolerance . We would accept estimated E-values within about two-fold error , corresponding to an accuracy of μ of ±1 bit; so if we want less than one estimate in 10 , 000 to deviate by that much , we want a standard deviation of <0 . 25 or so . The time required for this simulation is essentially negligible for most purposes . For n = 200 sequences of length L = 100 and the “typical” Pfam model Caudal_act , it takes about 40 milliseconds to estimate μ . It is more difficult to efficiently determine the location parameter τ , the base of the exponential tail of expected Forward scores . Few samples fall in the small probability mass of the tail . To obtain 200 high-scoring samples in a 0 . 1% exponential tail , we would still need to score 200 , 000 simulated random sequences , largely obviating any advantage of knowing λ . After unsuccessfully exploring several alternative approaches , I adopted the following ad hoc method . A Gumbel distribution of unknown λ is fitted to n = 200 Forward scores of random sequences; the Gumbel μ and λ from this simulation are used to predict the score threshold t at which P ( F>t ) = 0 . 04 ( the 4% tail ) ; this t is then taken to be τ for the location of the base of the high-scoring 4% Forward score tail . 4% was carefully chosen . Because Forward scores are not Gumbel distributed , and appear fat-tailed with respect to a maximum likelihood fitted Gumbel of unknown λ , the true tail mass P ( F>t ) is systematically underestimated by a Gumbel fit . On the other hand , because the Forward survival curve approaches its exponential asymptote of λ = log z from above , if we did accurately estimate P ( F>τ ) at low score thresholds and used that to locate the base of our exponential tail , that exponential tail would overestimate ( be above ) the tail probability mass at higher scores . The choice of 4% was optimized by trial and error as a point at which these opposing systematic errors are well balanced; the fitted exponential tail deliberately underestimates P ( F>t ) at lower scores where the Forward distribution still appears fat-tailed , in order to become accurate in the highest-scoring tail ( P ( F>t ) <0 . 001 or so ) where the Forward distribution has converged to an exponential . Using n = 200 Forward scores of random sequences of L = 100 suffices to determine τ with a standard deviation of 0 . 2 bits , and costs 330 msec for the “typical” Caudal_act model . For Karlin/Altschul statistics , the apparent λ for finite-length comparisons is known to increase for smaller sequences and weaker ( lower relative entropy ) scoring systems . Intuitively , finite length edge effect arises because the number of places that an alignment can start while still achieving a given length is less than NL , and achieving the highest scores requires the longest alignments ( so the higher scoring alignments have fewer start points available ) , and weaker average scores per position require longer alignments to reach a given total score; thus higher-scoring alignments “see” a smaller search space than lower-scoring alignments , so the probability of higher-scoring alignments is lower – the tail of the distribution falls off faster – than the asymptotic λ predicts . Edge effect has significant impact on BLAST's statistics and substantial effort has been made to correct for it [20] . In most of the results in Figures 2–4 , edge effect is not particularly apparent . However , these models have high relative entropy per position ( about 1 . 8 bits per match state emission distribution , compared to about 0 . 7 bits per aligned residue pair for BLAST's default BLOSUM62 substitution scores ) . High relative entropy per position results from the standard multinomial estimation procedures used for parameterizing the core profile HMM [3] , [47] , but has been shown to compromise the sensitivity of profile HMMs [27] , [43] . We have confirmed previous observations that even an ad hoc method to reduce the relative entropy per position ( “entropy-weighting”; [43] ) greatly improves search sensitivity in HMMER [27] , although , puzzlingly , the same effect was not seen by PSI-BLAST's authors [48] . Empirically , on a benchmark of structural homologs [49] , an optimal target relative entropy using entropy-weighting is about 0 . 6 bits per match state [27] . When entropy-weighted HMMER models are used , the apparent λ's for both Viterbi and Forward scores deviate slightly upwards from the conjectured λ = log z . Consistent with an edge effect interpretation [20] , the magnitude of this deviation is inversely proportional both to the length of the query N and to the average relative entropy per match state emission distribution; on the other hand , the effect does not appear to depend as strongly on the target length L ( data not shown ) . Two different approaches have been developed for correcting for edge effect . One approach is to use corrected query and target sequence lengths N′ = N−ℓ , L′ = L−ℓ , where ℓ is the expected length of an alignment [9] . Another approach is to apply a small correction to λ , using , where λ is the true ( asymptotic ) value , and α is empirically determined but clearly related to the inverse of the relative entropy per position [20] . I experimented with setting an edge-corrected target length model such that the flanking nonhomology states generate L′ = L−ℓ residues for various schemes of determining an appropriate average local alignment length ℓ , but without satisfactory results . The expected alignment length length ℓ has a complicated dependence on the model , the alignment score , and the query and target lengths . In particular , my schemes tended to break down severely in the small target sequence length regime L≃ℓ . Applying a correction to λ proved more successful . I estimate , where h is the average relative entropy per match state emission distribution , and the 1 . 44 factor was empirically determined from slopes of lines fitted to λ versus plots for models of varying h . Thus for typical Pfam models ( N∼140 ) parameterized with standard profile HMM multinomial/Dirichlet maximum a posteriori estimation ( h∼1 . 8 ) the correction is small ( 0 . 6931+0 . 0057 ) , but for short and/or entropy-weighted models the edge effect correction has non-negligible effect . This is only an empirically derived correction . It appears to suffice in practice , but there is clearly more going on here . A more satisfying and theoretically grounded accounting for edge effects in probabilistic local alignment is needed . In summary , the overall procedure for estimating the expected score distributions is to assume λ = log2 , determine an edge-corrected effective lambda for a query model of length N and relative entropy per match state emission h , and run two small simulations ( L = 100 , n = 200 ) to determine location parameters μ and τ for the Viterbi score Gumbel distribution and the Forward score exponential tail . Because I added ad hoc steps ( the edge effect correction and the methods for determining μ and τ ) on top of the conjectures about λ , one now wants to know , when the complete procedure is put together , how accurate are the resulting E-values for profile HMM searches ? Figure 5 shows the results of searching 9 , 318 Pfam 22 . 0 models ( either parameterized by the standard approach , or using entropy-weighting to yield lower information content models ) , against three different databases of 105 random sequences , of lengths L = 100 , 400 , and 1600 , collecting multihit local Viterbi and Forward scores , and plotting predicted E-value for the top 1000 scoring hits versus rank . If E-value estimation were perfect , we expect these points to disperse around a straight line of slope 1 ( the E-value of the top hit should be 1 , the E-value of the 10th ranked hit should be 10 , and so on ) . As expected , the mean predicted E-values are indeed tightly dispersed around a straight line of slope 1 . Each mean is derived from 9 , 318 trials , so we expect the outlying minimum E-value for the top-ranking score to be on the order of 1/9318 , or about 1×10−4 . The minimum predicted E-values for each of the six searches ( Forward vs . Viterbi , three choices of length ) range from 2 . 2×10−4 down to 3 . 7×10−6 , basically within expectation ( the 3 . 7×10−6 is significantly low , but just barely so; P = 0 . 03 to occur by chance in 9 , 318 trials ) . Some small systematic deviations from expectation can be seen on close examination , the most significant of which is in the Viterbi scores of entropy-weighted models for long ( L = 400 and L = 1600 ) target sequences: this is where the apparent “edge effect” of low information content models is having its greatest impact . Though statistically significant errors in E-value accuracy remain , for practical purposes they are tolerably small . Moreover , they are almost invariably in the conservative direction . That is , we would rather slightly underestimate λ than overestimate it . If we underestimate λ , we overestimate E-values and miss some true positive homologs without compromising our false positive rate . A design goal of HMMER is to accurately estimate and control false positive rates in large-scale automated analyses .
The most immediate benefits from this work are that for profile HMM searches , the statistical significance of both Viterbi and Forward scores can be calculated efficiently without expensive simulation . This enables substantial accelerations in the use of Viterbi scores , and more importantly , it opens the way to a broader use of more powerful Forward scores . Although I have done the simulations in the specific context of HMMER , the local alignment model is not specific to HMMER . It is a generalized probabilistic local alignment model with a uniform entry/exit distribution . Because position-independent substitution matrix scores and gap costs are just a special case of position-specific profile scores , the same model can be used to parameterize standard Smith/Waterman local alignments [44] probabilistically . From a computational standpoint , optimal ( Viterbi ) local alignment for profile HMMs is essentially identical to Smith/Waterman alignment , with the same O ( NL ) computational complexity , and the Forward algorithm is a minor modification of Viterbi ( replacing max operations with sums ) . Existing profile HMM implementations are two orders of magnitude slower than BLAST , but this is only because they are still using full dynamic programming ( so running times are comparable to other unaccelerated Smith/Waterman implementations ) . There is no reason why the same heuristics that BLAST uses to accelerate Smith/Waterman cannot be applied to accelerate profile HMM searches . Similarly , existing nonprobabilistic sequence alignment methods , including BLAST , can be modified ( with the addition of a few transition parameters ) to accomodate the probabilistic parameterization described here . The same conjectures are also expected to hold for local alignment scores for probability models of more than just linear sequence alignment . For example , our preliminary results indicate that local alignment scores for profile stochastic-context free grammars ( SCFGs; models of RNA structure and sequence ) obey the same conjectures for both CYK and Inside scores ( analogous to local Viterbi and Forward scores ) ( DL Kolbe and SRE , unpublished results ) , which should help in efficiently and accurately calculating E-values for profile SCFG searches for structural RNAs [32] , [50] . However , at least three important points limit any conclusions I can try to draw about how widely the conjectures might hold . First , the same conjectures ought to hold for glocal and global alignment models . Nothing in the conjectures' rationale required the probabilistic models H and R to be configured in any particular way . However , based on previous work on glocal and global alignment scores , it is unlikely that these score distributions are going to exhibit a λ = log z simple exponential tail for biologically relevant model and sequence lengths [45] , [51] . Indeed , in preliminary experiments I have observed glocal score distributions converging to λ = log z Gumbels for Viterbi scores and e−t log z exponential tails for Forward scores only for the smallest HMMs , the largest target sequences , and the most extreme tails E<<1 . This may suggest that the conjectures hold only asymptotically , with glocal or global alignment score distributions converging slower than local score distributions . Second , if any probabilistic local alignment model H should work , why would the prototype HMMER3 profile HMM architecture and parameterization be necessary to obtain these results , compared to HMMER2's local alignment scores ? This again indicates that score distributions are more sensitive to details of model parameterization than the conjectures' generality would suggest . I believe the uniform local entry/exit distribution to be the important difference , again possibly because this makes score distributions reach asymptotic behaviors more quickly . However , I have not dissected the two implementations and tested specific differences one at a time , because it is not feasible to emulate HMMER2 in HMMER3's implementation ( and vice versa ) . Moreover , perhaps inconsistent with my thinking , the other popular profile HMM software package , SAM , uses a nonprobabilistic strategy of scoring zero for local entry/exit by analogy to Smith/Waterman , which ought to produce an implicit uniform entry/exit distribution , but the SAM implementors have gone away from assuming a fixed λ ( using Milosavljević's algorithmic significance test ) and now use simulation-calibrated E-values instead [29] . Third , it is trivial to produce an example of a probabilistic model H that gives expected score distributions deviating strongly from the conjectures: set H = R , and all log odds ratio scores become zero ( and thus λ = ∞ ) . The conjectures must break down as the relative entropy between H and R approaches zero . These issues show the main limitation of the simulation-based approach I have taken . Proper understanding of the regimes in which the conjectures break down requires a mathematical analysis , not simulations limited to a particular problem domain . Such analysis would be desirable , and it could lead in fruitful new directions . For example , the fact that HMMER3 glocal score distributions do appear to asymptote towards the conjectures ( albeit not for a practical range of tail probability mass nor query and target lengths ) seems promising . A general approach for estimating statistical significance of global or glocal gapped alignment scores , under traditional ( arbitrary ) scoring systems , largely remains elusive , despite significant effort and progress [45] , [51] . Perhaps – though this is only a guess – such problems could become more amenable to mathematical analysis under the simplifying constraints imposed by a fully probabilistic scoring system . For example , the troublesome “log-linear transition” of traditional alignment scores [52] never occurs; the expected score of extending a full probabilistic alignment by an additional residue is always nonpositive . Another problem that will need more attention is finite length effects . The finite length edge effect described for BLAST scores [20] is not the only finite length effect that can impact score distributions . Another is that there is a maximum score threshold ( i . e . , the score of a global , ungapped , 100% identical alignment ) beyond which the probability of a higher score is just zero , so expected distributions will deviate down as they approach this maximum score threshold . In typical sequence alignments , where both the query and the target are on the order of hundreds of residues , this effect is negligible . In profile HMMs , however , where some Pfam models are quite short ( as small as N = 5 ) , a maximum score effect appears to be in play , especially for unihit mode models with low information content ( entropy-weighted ) parameters . Fortunately , any such errors will be in the conservative direction , compromising sensitivity instead of specificity ( HMMER would overestimate E-values for such models ) . This work was partly inspired by the work of Yu and Hwa , who described a “hybrid” ( or “semi-probabilistic” ) scoring method that gives Gumbel-distributed scores with λ = log z [28] , [53] . Hybrid scoring essentially amounts to taking the maximum score of the cells in the Forward dynamic programming matrix . In HMMER3 , I also observe Gumbel-distributed hybrid scores with λ = log z ( data not shown ) . The three scoring systems appear to differ in their susceptibility to finite length effects that increase in low information content models . The distribution of Forward scores seems more robust than Viterbi scores ( this is seen in Figures 4 and 5 ) , and in preliminary experiments , hybrid scores appear to be even more robust ( data not shown ) . This might account for why they turned to hybrid scores rather than standard Viterbi or Forward scores to achieve what they dubbed “universal statistics” ( meaning constant λ ) . I have taken care to distinguish Viterbi from Forward scores , and local from glocal or global alignment modes , all of which are just choices in the same full probabilistic modeling framework . Some prior work has conflated probabilistic modeling and Forward scoring , referring to Forward scores as “probabilistic alignment scores” and arguing that probabilistic alignment scores do not follow Gumbel distributions as opposed to traditional alignment scores [28] , but Viterbi scores are also probabilistic . Other prior work has argued that HMMER scores do not follow expected Gumbel statistics [49] , but HMMER2's default mode is multihit glocal , not local ( local alignment requires a command line option ) . As it happens , HMMER2 does fit a left-censored Gumbel as a best-effort approximation of the glocal score distribution , and because this is known to be inaccurate , it attempts to focus the fit to achieve highest accuracy at the critical E∼1 region where accurate significance estimation is important; this means that HMMER2 multihit glocal ( default ) mode E-values are overestimated for E<<1 , underestimated for E>>1 , and most accurate in the E∼1 region , which others have observed empirically [45] . Although most homology search methods are based on local alignment , our previous internal HMMER2 benchmarks and benchmarks of other methods [45] have suggested that glocal alignment is more sensitive and specific when conserved protein domains can be defined a priori ( as in protein domain databases like Pfam , SMART , and CDD [25] , [26] , [54] ) . On the other hand , even with predefined domain boundaries , occasional cases of conserved subdomains and truncated database sequences make it unwise to rely solely on glocal searches . For these reasons , HMMER2 has defaulted to glocal mode , and Pfam search servers report an ad hoc merge of glocal and local search results . We have wanted to find a way around the need to run two searches to trade off the better statistics and robustness to unusual cases of local mode versus the better average sensitivity of glocal mode . Following results of Karplus and coworkers [43] , we have recently observed that much of our previously observed difference between local and glocal mode power results from local alignments being much more sensitive to the information content of the query . When we introduce parameterization methods for controlling the model's average information content per position ( such as “entropy weighting” [43] ) , sensitivity benchmarks of HMMER local and glocal modes become comparable [27] . I am not so concerned any more that local alignment mode will be sacrificing significant search power relative to glocal mode , and I am currently planning for HMMER3 to default to local . Whether HMMER3 will implement glocal alignment mode and glocal E-value statistics remains undecided . It is important to distinguish generative probabilistic models of local alignment from other “probabilistic” local alignment methods that apply renormalization and partition functions to interpret traditional arbitrary scores as unnormalized log-odds probabilities [28] , [35]–[38] . In a generative model , λ is explicitly log z , where z is the base of the log used to convert probability parameters to log odds scores . In renormalization-based approaches , the original arbitrary scores and their distribution are unchanged , so determining distribution parameters like λ is no simpler than in BLAST or Smith/Waterman – essentially , in a renormalization approach , one must still determine the unknown implicit probabilistic basis of the arbitrary scoring system , which means determining λ [13] . A limitation of this work is that I have only examined scores of independent , identically distributed ( i . i . d . ) random sequences with a single typical amino acid composition . Real sequences often have biased residue composition , repetitive regions , and other heterogeneities that can produce spurious high-scoring aligments , requiring additional methods to compensate [29] , [48] , [55] . It will be necessary to confirm previous observations that the same sorts of methods apply to Forward scores , not just to optimal alignment scores [29] . Additionally , the probabilistic inference framework admits an interesting alternative , which is to develop better explicit probabilistic models of nonhomologs ( hypothesis R ) , not just of homologs ( hypothesis H ) . From a purist Bayesian perspective [33] , [34] , [56] , one might question why we need E-values and classical statistical significance tests at all . Shouldn't a posterior probability be sufficient ? It would be , if model H were an accurate model of the sequence space of remote homologs we want to detect . However , query sequence ( s ) are rarely an unbiased sample from the desired space of homologs . Our model H usually represents a narrow clade of known query sequences , not the broader space of homologs we want to detect . Presented with a remote homolog , the model may correctly assign it a low posterior probability ( it doesn't look like it belongs to the same sequence space as our query sequences ) , but nonetheless , it may have a higher score than one expected by chance . A purist would say that this just shows that our model is inaccurately parameterized for our problem . This is certainly true , but better parameterization requires evolutionary models that can extrapolate what remote homologs will look like , and this has proven to be a difficult problem . Most current probabilistic evolutionary models neglect important inhomogeneities in the evolutionary process , like heterotachy ( rate variation between branches ) , and have so far proven in our hands to be insufficient in schemes for increasing profile HMM sensitivity ( Alex Coventry and SRE , unpublished results ) . E-values and classical statistical significance testing are of immediate utility , while development of more useful probabilistic evolutionary models remains a focus for the future .
The HMMER3 prototype source code ( together with Easel , a code library that HMMER depends on ) is freely available at http://selab . janelia . org/publications/#Eddy08 under the terms of the open source GNU General Public License . This source tarball includes a 00README file with detailed command-line scripts for reproducing the results in the figures . The Pfam database is freely available at http://pfam . janelia . org . The simulation results are generated by the hmmsim program , which takes a profile HMM as input , generates and scores n random i . i . d . sequences , and outputs scores , statistics , and input files for the freely available GRACE graph plotting program ( http://plasma-gate . weizmann . ac . il/Grace/ ) . Maximum likelihood fitting of Gumbel and exponential distributions is implemented in the gumbel and exponential modules of Easel , respectively , following methods in [46] . In HMMER3's implementation , the local entry/exit distribution is in fact not completely uniform , for the following reason . Imagine ( as an extreme illustration ) a profile HMM with a “consensus” match state Mk that is never reached , because the ( M , D ) k−1→Mk transition probabilities are zero , and imagine that this “dead” match state generates a residue that is for some reason never seen in homologs . If the local alignment model imposed a uniform entry/exit distribution , allowing an entry transition straight into the dead Mk state , then local alignments can contain the impossible residue . To avoid this , HMMER ad hoc weights the local entry probabilities into states Mk by the probability that each Mk is used in sequences generated from the model . Because by default HMMER assigns consensus match states to alignment columns that contain ≥50% residues as opposed to gap characters , the usage of each match state is generally similar and high , so the effect of this weighting is normally small ( less than two-fold difference between any pair of entry positions k ) . It was necessary to implement HMMER3 dynamic programming routines as floating point calculations . In the target length model , a ratio like approaches 1 . 0 for large L , and roundoff/truncation error becomes an issue . The precision of HMMER2's internal scaled integer log-odds scores ( in units of 0 . 001 bits ) proved insufficient . All computational times mentioned in the paper are measured for a single execution thread on a 3 . 2 GHz Intel Xeon ( Dempsey ) CPU , using prototype HMMER3 code compiled with the GNU C compiler ( gcc ) version 3 . 4 . 5 with a -O2 optimization level , running a Red Hat Enterprise Linux AS release 4 operating system .
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Sequence database searches are a fundamental tool of molecular biology , enabling researchers to identify related sequences in other organisms , which often provides invaluable clues to the function and evolutionary history of genes . The power of database searches to detect more and more remote evolutionary relationships – essentially , to look back deeper in time – has improved steadily , with the adoption of more complex and realistic models . However , database searches require not just a realistic scoring model , but also the ability to distinguish good scores from bad ones – the ability to calculate the statistical significance of scores . For many models and scoring schemes , accurate statistical significance calculations have either involved expensive computational simulations , or not been feasible at all . Here , I introduce a probabilistic model of local sequence alignment that has readily predictable score statistics for position-specific profile scoring systems , and not just for traditional optimal alignment scores , but also for more powerful log-likelihood ratio scores derived in a full probabilistic inference framework . These results remove one of the main obstacles that have impeded the use of more powerful and biologically realistic statistical inference methods in sequence homology searches .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"mathematics/statistics",
"computational",
"biology/protein",
"homology",
"detection"
] |
2008
|
A Probabilistic Model of Local Sequence Alignment That Simplifies Statistical Significance Estimation
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Self-incompatibility has been considered by geneticists a model system for reproductive biology and balancing selection , but our understanding of the genetic basis and evolution of this molecular lock-and-key system has remained limited by the extreme level of sequence divergence among haplotypes , resulting in a lack of appropriate genomic sequences . In this study , we report and analyze the full sequence of eleven distinct haplotypes of the self-incompatibility locus ( S-locus ) in two closely related Arabidopsis species , obtained from individual BAC libraries . We use this extensive dataset to highlight sharply contrasted patterns of molecular evolution of each of the two genes controlling self-incompatibility themselves , as well as of the genomic region surrounding them . We find strong collinearity of the flanking regions among haplotypes on each side of the S-locus together with high levels of sequence similarity . In contrast , the S-locus region itself shows spectacularly deep gene genealogies , high variability in size and gene organization , as well as complete absence of sequence similarity in intergenic sequences and striking accumulation of transposable elements . Of particular interest , we demonstrate that dominant and recessive S-haplotypes experience sharply contrasted patterns of molecular evolution . Indeed , dominant haplotypes exhibit larger size and a much higher density of transposable elements , being matched only by that in the centromere . Overall , these properties highlight that the S-locus presents many striking similarities with other regions involved in the determination of mating-types , such as sex chromosomes in animals or in plants , or the mating-type locus in fungi and green algae .
Sexual reproduction entails the combination of genetic material from different individuals to produce offspring . Yet in many species mating is not entirely random , being only possible between individuals with either distinct sexes or distinct mating-types [1] . Sexes or mating-types are typically determined by very distinctive genomic tracts known as sex chromosomes in animals [2] , [3] and plants [4] , [5] , sex-determining loci in honeybees [6] , mating-type loci in green algae [7] , [8] and fungi [9]–[12] or self-incompatibility ( SI ) loci in plants [1] . In spite of the wide diversity of organisms and types of molecular and genetic systems involved , these genomic regions typically share several common features . In particular , the genes that directly determine the sexes or the mating-types are often tightly linked , sometimes with a large genomic region containing many genes , in which recombination is suppressed . Such regions can include most of a chromosome ( e . g . the male-determining region of mammalian Y chromosomes ) . Recombination suppression in these genomic regions is typically accompanied by a variety of degeneration signatures [13] , [10] , [2] , [14] such as low efficacy of natural selection , low gene density and accumulation of repeated DNA such as transposable elements ( TEs ) . At present , a comprehensive understanding of the forces driving evolution of these genomic regions is still missing [15] . In particular , two sets of issues remain unanswered . First , the process by which recombination is suppressed and the shape of the transition between recombining and non-recombining regions is not known . In sex chromosomes of mammals and those of the plant Silene latifolia , the level of X-Y divergence increases with increasing distance from the boundary with the recombining ( pseudo-autosomal ) region . Recombination suppression is therefore thought to have occurred in successive and discrete steps [3] , [14] , [16]–[20] , possibly involving large chromosomal inversions . Second , the factors determining the size of the non-recombining region remain poorly understood . In mammals , the size of the Y chromosome is 37% that of the X [3] , [14] , while in Silene latifolia it is 150% that of the X [5] . Homomorphic self-incompatibility ( SI ) is a highly relevant genetic system to address these issues . SI functions to prevent self-fertilization in hermaphroditic plants [21] . While relatively widespread ( being present in at least 94 flowering plant families [22] ) , homomorphic SI has been described at the molecular level in only a handful of taxa ( reviewed in [23] , [24] ) . The genetics of SI involves a single genomic region or a small number of regions . All of the few incompatibility loci that have been characterized at the molecular level contain at least two genes , one expressed in pistils and the other in anthers for sporophytic SI; in gametophytic SI systems , the pollen-S gene is expressed in pollen , and there are sometimes multiple genes [25] . These genes encode proteins that physically interact in a haplotype-specific manner , ultimately allowing normal cross-pollen germination and/or growth when proteins are produced by haplotypes carrying different specificities , but preventing it when pollen and pistils express cognate specificities , in particular avoiding self-fertilization . Evolutionary properties of the genes controlling SI have been studied in several taxa , including the Brassicaceae , Solanaceae and Papaveraceae species [26] , [27] . In accordance with negative frequency-dependent selection theory [28] , these genes show remarkable evolutionary features . First , the S-locus typically has very high haplotype diversity , with up to >100 distinct specificities in natural populations within species ( see [29] for a review ) . Second , because they are maintained within species for extended periods of time , these haplotypes show high nucleotide divergence among specificities within species [30] and trans-specific polymorphism between closely related species [31] . Third , to maintain specific recognition , the pollen and pistil genes are expected to be in strong linkage disequilibrium and hence to constitute co-adapted haplotypic combinations [32] . Indeed , recombination between the two component genes would disrupt specific recognition , leading to self-compatible haplotypes [33] , [34] . Several studies in different SI systems confirmed that recombination among haplotypes in the S-locus is highly infrequent [35] , [33] , [36] , [34] , [37] , [30] , and consequently that pollen and pistil genes are expected to follow the same evolutionary history . Fourth , in species whose SI system is sporophytic [21] , complex dominance relationships have been described among S-haplotypes controlling both pollen and pistil phenotypes [38] . Sporophytic SI has been described at the molecular level in a single family , the Brassicaceae . In both Brassica and Arabidopsis , the dominance relationships among haplotypes are partly related to their phylogenetic distance , with roughly four different classes in A . lyrata , corresponding to four phylogenetic groups [39] and two dominance classes in Brassica corresponding to two phylogenetic groups [40] , [41] . In line with theoretical expectations [42] , [43] , dominant and recessive S-haplotypes appear to experience contrasted evolutionary dynamics [30] . In particular , recessive haplotypes generally occur at higher frequency and may form homozygotes . Since molecular polymorphism has been reported among gene copies within a given S-allele [30] , homozygote combinations may allow recombination between these highly similar genes copies . Because of linkage to the targets of negative frequency-dependent selection , the surrounding genomic region is also expected to show deeper coalescence than the genomic background , and hence high sequence divergence among haplotypes [44] . The physical extent of this genomic region is potentially large , in inverse proportion to the extent of local recombination restriction within the S-locus . Analysis of the S-locus in different species belonging to different SI systems confirmed that this genomic region is indeed highly heteromorphic in terms of sequence similarity among haplotypes [45]–[49] . However detailed analyses of the patterns of molecular evolution in the S-locus region are lacking because full sequences of the region are available for just a handful of haplotypes and for a few taxa belonging to different SI systems . In the best documented SI system , that of the Brassicaceae , twelve S-haplotypes have been sequenced in the cultivated species of the Brassica genus [50]–[53] , [46] , [54] , [55] . However , many of these sequences lack the flanking regions , hence preventing comparative analysis . In addition , three haplotypes of the S-locus were sequenced in A . thaliana , one of which is a recombinant haplotype between two of the three main haplogroups currently segregating in the species [56] , [57] , [49] . However , although the breakdown of SI is arguably recent in A . thaliana [58] , the three available sequences encode non-functional haplotypes and may have decayed substantially , especially in light of the rapid genomic changes that occurred since the split with A . lyrata [59] . Only five haplotypes from natural populations have been sequenced in Brassicaceae with functional SI , all from A . lyrata [60]–[62] . Additionally , two haplotypes with truncated SCR sequence , consequently carrying non-functional specificities , were also reported and sequenced in this species [62] . Here , we obtained full sequences for a sample of 11 S-haplotypes from natural populations of A . halleri and A . lyrata , distributed across the four phylogenetic classes described in these species . We first used these data to determine accurately the boundaries of the non-recombining S-locus region and evaluated its extent , by studying the breakdown of sequence similarity and changes in inter-haplotype phylogenetic patterns at the interface between the flanking regions and the S-locus . We then investigated patterns of variation among haplotypes in the genomic distance between SCR and SRK , in their relative orientation and in the occurrence of additional ORFs or pseudogenes . We also compared the complement of transposable elements across haplotypes and asked whether the different evolutionary processes acting on dominant and recessive haplotypes had left different molecular signatures . Finally , we took advantage of the complete haplotypic combinations of the two component genes SCR and SRK in A . lyrata and A . halleri to investigate their pattern of co-divergence in natural populations .
To determine the precise location of the boundaries of the non-recombining S-locus region , we compared sequences from twelve S-locus haplotypes ( additionally including the reference haplotype Al13 from the A . lyrata full genome sequence [59] ) using the VISTA software [64] , looking for a transition in the levels of sequence similarity among haplotypes . As shown in Figure 1 and Figure S1 , the sequence conservation among different haplotypes is fairly high in flanking regions on both sides of the S-locus , but plummets sharply between about 300 bp upstream of the start codon of the U-box gene on one side and near the stop codon of ARK3 on the other side . Hence , we define the S-locus as this region of very low similarity lying between these two breakpoints . Synteny is remarkably well conserved outside the S-locus region , except for the presence or absence of some transposable elements in intergenic regions ( which were removed from the reference sequence in Figure 1 for clarity ) . High sequence similarity among haplotypes and high collinearity of flanking genes in the region outside of the S-locus suggest that recombination among haplotypes does occur outside the region delimited by these breakpoints . Additional evidence comes from the observation that elevated diversity , as expected for neutral sites linked to sites under balancing selection [44] , is mostly apparent for the two immediately flanking genes ( the U-box and ARK3 ) , while levels of synonymous nucleotide diversity are comparable with that of the genomic background ( ≈2% , [65] , [66] ) for genes located further away on the chromosome ( Figure S2 ) , as previously reported [37] , [65] . In contrast , within the S-locus , sequence similarity is almost completely lacking , the only notable exceptions being the seven exons of SRK and some transposable elements of the same family . Interestingly , a pseudogenized partial duplicate of the ARK3 gene ( from the end of the first exon to the end of the gene ) is found within the S-locus in three different haplotypes: Al01 , Ah15 and Ah43 . These partial duplicates of ARK3 within the S-locus region could be responsible for the observation by Hagenblad et al . [67] of the occurrence of a pseudogenized paralog of ARK3 in some haplotypes , including one carrying allele Al01 at SRK . A similar partial duplicate sequence of ARK3 was found in the S-locus region of the recombinant C24 haplotype of A . thaliana , and it was hypothesized that this motif acted as the recombination breakpoint between the two common haplotypes A and C [57] . Interestingly , the duplicated ARK3 sequences in Al01 , Ah15 and Ah43 are more similar to ARK3 gene copies present in haplotypes other than their own ( Figure S3 ) . Assuming that this second copy initially originated through gene duplication from the same chromosome , this observation implies that inter-haplotype recombination does occur at the genomic position of this gene , and hence supports our conclusion that ARK3 indeed lies outside the non-recombining region . Moreover , while the partial duplicates of ARK3 in Ah15 and Ah43 are closely related , that of Al01 is not phylogenetically close , suggesting at least two independent duplication events . Annotation of the S-locus region revealed only the two incompatibility genes , SCR and SRK , plus TEs ( see below ) . A single copy of SCR and of SRK was found in each haplotype , whereas a previous study [60] described two copies of SCR in one haplotype from A . lyrata ( Al20 ) . Multiple gene copies are therefore the exception rather than the rule in the S-locus of Arabidopsis . Sequencing of the 206 . 7 Mb A . lyrata genome predicted 32 , 670 genes [59] , i . e . approximately 0 . 16 genes per kb . With only two genes in about 60 kb , the S-locus appears to have very low gene density ( ca . 4 . 8 times lower than the genomic background ) . Striking differences in the timescales of gene genealogies for the S-locus genes SCR and SRK as compared to the flanking genes were observed ( Figure 2 ) , with much deeper genealogies for SCR and SRK , as expected for genes under strong frequency-dependent selection [68] . Moreover , the gene genealogies of SCR and SRK ( Figure 2 ) were found to be more congruent than expected by chance ( Icong = 1 . 53; P-value = 0 . 0014 [69] ) . Specifically , the phylogenetic classes defined based on SRK sequences [39] ( class I: Al01; class II: Ah03 , Ah28 , Al18 and Al14; class III: Al13; class IV: all other haplotypes ) are conserved in the SCR tree . In contrast , the phylogenetic relationships among haplotypes were strikingly different for the flanking genes ( Figure S4 ) , as reported for haplotypes of the U-box and the ARK3 genes in A . thaliana [70] . Indeed , in our dataset gene genealogies of the flanking genes tend to cluster according to species overall , rather than to S-locus phylogenetic classes . This observation further supports the conclusion that the non-recombining region is confined to the S-locus and is determined by the two breakpoints identified based on sequence similarity . The S-locus region is variable in size across haplotypes , spanning from 31 kb ( haplotype Al14 ) to 110 kb ( haplotype Ah15 ) with an average size of 62 kb . Given that BAC sequences do not cover the totality of the S-locus from haplotypes Ah03 , Ah13 and Ah43 , these estimates are lower bounds . Also , several libraries that we constructed could not be exploited because no single clone showed both flanking genes used for screening , suggesting that the S-locus haplotypes they contain may have been larger than the average 100 kb typical of the BAC clones in our libraries . With an average size of 74 kb , haplotypes from SRK phylogenetic class IV are generally larger than haplotypes from classes I to III , showing an average size of 50 kb ( Table 1 ) . Figure 3 summarizes the gene organization within the S-locus and includes data from Kusaba et al . [60] , Boggs et al . [61] and Guo et al . [62] . Globally , we found that gene organization within the S-locus is highly variable with regard to gene order ( SRK located either on the ARK3 or the U-box side as compared to SCR , although the latter order was only found in a single haplotype , Al13 ) , relative orientation of SCR and SRK ( tail-to-tail , head-to-head or in the same direction ) , and distance separating them ( from less than 1 kb to about 26 kb; Table 1 ) . These patterns also vary among haplotypes within each of the SRK phylogenetic classes , with the exception of class II haplotypes showing mostly SCR and SRK oriented tail-to-tail and a location of SRK consistently very close to the flanking gene ARK3 in head-to-head orientation . Strikingly , these class II haplotypes were already reported to show common features that distinguish them from other phylogenetic classes [71] , [39] . We found here that the strong sequence similarity previously noted in the kinase domain of these haplotypes [71] is extended to the whole intergenic region ( about 900 bp in length ) between SRK and ARK3 ( Figure S5 ) , in contrast to comparisons with other classes of haplotypes or between classes ( Figure S1 ) . As suggested by [39] , these class II haplotypes could have originated by a gene conversion event implying unlinked members of the SRK gene family . Interestingly , this same intergenic region is also conserved between class II haplotypes and haplotypes Ah15 and Ah43 , two of the three haplotypes carrying a pseudogeneized duplicated copy of ARK3 . This observation strongly suggests that the duplication involved a recombination event between these haplotypes and a class II haplotype . Interestingly , while [62] suggested that haplotypes Al38 and Al50 lack the second exon of the SCR gene , we were able to detect the second exon upon closer examination applying the same approach than in our own data , suggesting that these haplotypes are indeed functional . In addition , while previous studies failed to detect a kinase domain for AlSRK01 [30] , our genomic approach confirmed that all SRK sequences we observed contained a full-length kinase domain . Transposable elements annotation with the CENSOR [72] and PLOTREP [73] programs revealed a strong density and diversified complements of TEs in the S-locus , with a representation of most families known in the A . thaliana genome ( detailed annotation and a complete list of TEs for each haplotype are shown in Figure S6 and Table S2 ) . In order to determine whether these observations are uncommon in the genomic background , we also used CENSOR [72] to estimate TE density along the A . lyrata genome divided in non-overlapping windows of 100 kb . Variation of TE density along chromosome 7 confirmed that the TE density of the S-locus sharply departs from its chromosomal background , being matched only by the centromeric region ( Figure 4 , and Figure S7 for the other chromosomes ) . This difference is not due to an invasion by a single class of TEs , since the quantitative difference in density was observed for most TE families ( Figure S8 ) . While most haplotypes have higher TE density than the genomic background , there is striking variability in TE density among haplotypes . Indeed , TE density depends on SRK phylogenetic classes , which are themselves associated with dominance with higher density in the more dominant haplotypes ( Figure 5A and 5B ) . Since levels of dominance are in turn expected to correlate with S-haplotype frequency in natural populations [74] , [42] , [75] , we plotted TE density against haplotype frequency , as estimated from S-locus genotype surveys in A . lyrata [76] and A . halleri ( P . Goubet et al . unpublished data ) . We find that variation in TE density is even better captured by haplotype frequencies , with rare haplotypes being more enriched in TEs than more frequent haplotypes ( Figure 5C ) .
Contrasted patterns of conservation between the S-locus and its flanking regions are in line with two previous investigations comparing three and five haplotypes in A . thaliana [49] and A . lyrata [62] , respectively . Based on a more extensive collection of S-haplotypes , we could precisely map the breakdown of synteny to two narrow regions very close to the 5′ or 3′ ends of the coding regions of the flanking genes U-box or ARK3 , respectively . Note that [37] and [65] reported some level of co-segregation of flanking genes with variation at SRK in local A . lyrata populations , while our global sample from two related species might have left sufficient time for recombination to uncouple the S-locus region from its genomic background . Using this objective criterion to define the S-locus itself , we find that the S-locus has an average size of 62 kb , ranging from 31 to 110 kb among haplotypes , much larger than the average distance of 7 kb between the two S-locus genes , SCR and SRK , ranging from 1 kb to 26 kb . An orthologous sporophytic SI system occurs in the genus Brassica , although the S-locus is located in a different genomic region than in Arabidopsis . Based on the available sequences of four B . rapa haplotypes [53] , [55] and using a similar criterion to define the S-locus we determined that the S-locus was somewhat smaller than in Arabidopsis , ranging from 28 to more than 60 kb . In contrast , the distance separating SCR and SRK was less variable , ranging from 2 to 11 kb . In Brassica , however , the S-locus generally comprises a third gene , SLG which is a paralog of SRK lacking the kinase domain , and the overall region comprising these three genes ranged from 23 to 43 kb . In Ipomoea trifida ( Convolvulaceae ) , which also exhibits sporophytic SI but of a different molecular nature , the S-haplotype-specific divergent region between the only two sequenced haplotypes ( S1 and S10 ) extends over 50 and 34 kb , respectively [77] . In the gametophytic SI system of Prunus dulcis and P . mume ( Rosaceae ) , the S-locus was estimated as being a divergent genomic region of about 70 kb [78] and 15 to 27 kb [45] , respectively . In Antirrhinum hispanicum ( Plantaginaceae ) , the distance between the two component genes of haplotype S2 is 9 kb [79] . However , a major difference between the S-locus of the Brassicaceae and that of the Plantaginaceae and Solanaceae is that in the latter the pollen phenotype can be encoded by different members of the gene family to which the male determinant belongs , so that the S-locus comprises more than two genes [25] , making this comparison tricky . Overall , in spite of the large diversity of species and molecular mechanisms involved in the different SI systems , the size of S-loci seems to be fairly constant across taxa , ranging from 27 to about 110 kb , with Arabidopsis species apparently in the upper part of the range . Beyond the comparison with S-loci of other plants , the size of the S-locus can also be compared with that of the mating-type loci in fungi or green algae . In the basidiomycete Cryptococcus neoformans , sex determination is controlled by a locus including genes encoding a pheromone and its receptor . Haplotypes of this mating-type locus , α and a , represent a genomic region of approximately 105 to 130 kb [80] , hence slightly larger than the S-locus in A . halleri and A . lyrata . In another basidiomycete , Ustilago hordei , the mating-type locus consists of a single region comprising two complexes , a and b , between which recombination is suppressed . The distance between these two complexes was estimated to be 500 kb and 413 kb in the MAT-1 and MAT-2 strains , respectively [9] . In the ascomycete Neurospora tetrasperma , the non-recombining region comprising the mating-type locus covers 78 . 4% of the chromosome length , i . e . 6 . 9 Mbp [11] . In green algae , the mating-type locus of the unicellular Chlamydomonas reinhardtii consists of a highly rearranged 200-kb region [81] while that of the multicellular Volvox carteri is about 500% larger and contains many ORFs . Interestingly , C . reinhardtii is an isogamous species with two morphologically indistinguishable mating-types [7] while V . carteri shows morphological differentiation between the mating-types , suggesting the general conclusion that genomic regions involved in mating-type systems that are not associated with morphological differences between mates may span smaller genomic regions . In other words , the accumulation of genes with a role in expression of the morphological differences between mating-types [8] may contribute to some extent to the variation in size of the mating-type locus , in addition to transposable elements and non coding DNA accumulating in these regions . Because in homomorphic SI the mating-types are not associated with morphological differences , the S-loci may retain a smaller size . Only six sequences of SCR were previously described in Arabidopsis because of the difficulty of finding conserved regions to perform PCR amplifications [60] , [61] , [82] , [70] , [62] . Our important sequencing effort of the S-locus region resulted in the successful identification of full SCR sequences in ten new S-haplotypes in A . halleri and A . lyrata and only the second exon of SCR in one haplotype ( haplotype Ah43 , for which we could not obtain the full S-locus sequence ) , along with their cognate SRK partner . These results do not support the hypothesis of existence of non-functional haplotypes carrying only partial SCR sequences , as proposed by Guo et al . [62] , as we were able to localize the missing coding sequence for their two putative non-functional haplotypes when applying the ALN [83] software fed with all known SCR sequences . Congruence of SCR and SRK phylogenies reflects the coevolution necessary to maintain the specific SCR-SRK protein-protein recognition , and clearly indicates that recombination between the two SI genes has been precluded . Comparison of phylogenies between SCR and the S domain of SRK was already investigated by Sato et al . [32] for twelve haplotypes in Brassica oleraceae . They found that the hypothesis of an identical topology for the two trees was not rejected . Edh et al . [84] also compared SCR and SRK phylogenies in Brassica rapa , Brassica oleraceae and Brassica cretica class II haplotypes , but congruence between topologies could not be clearly demonstrated , perhaps as a consequence of the concerted evolution of the SLG and SRK genes within haplotypes , or of the more recent history of diversification within the class II lineage . In contrast , in the ascomycete Neurospora [85] , the non-self recognition system is controlled by two tightly linked genes , het-c and pin-c . In agreement with our results in the S-locus , congruence was found between topologies of the phylogenies of these two genes , but not with those of the flanking genes . When more SCR/SRK sequences become available , it will be interesting to study in more details the co-evolutionary process . Based on the study of nine haplotypes in A . thaliana , A . lyrata and Capsella rubella , Guo et al . [62] proposed that head-to-head orientation of SCR and SRK was the ancestral orientation in the Arabidopsis/Capsella lineage . However , the lack of conserved orientation pattern in our results based on a much larger number of haplotypes suggests that , in spite of the shared evolutionary history of SCR and SRK , the S-locus has experienced a history of frequent inversions and genomic rearrangements . At this stage , we argue that the ancestral orientation cannot be deduced . However , our results confirm that with a single exception SCR always occurs at the U-box side and SRK at the ARK3 side . Interestingly , the exception to this rule concerns haplotype Al13 , which was obtained from an A . lyrata strain ( MN47 ) with non-functional SI . This suggests the intriguing possibility that the observed inversion may have been associated with the breakdown of SI in this strain used for sequencing the A . lyrata genome . Strong structural variation among haplotypes seems to be a common feature of S-loci [86] and genomic rearrangements , particularly inversions , are known to be frequent in low recombination regions such as in sex chromosomes of mammals [3] , [14] , [87] and plants [19] or in the mating-type locus of green algae [81] . Evidence suggesting gradual suppression of recombination was found in sex chromosomes , and led to the concept of evolutionary strata [16]–[18] , [3] , [14] , [19] , [20] . These strata , composed of genes which stop recombining and therefore start diverging presumably at the same time , could have been caused by large inversions in the non-recombining sex chromosome [16] . As in sex chromosomes , inversions in the S-locus could have contributed to the reduction in recombination among haplotypes . However , no discrete strata of divergence among haplotypes can be identified . Instead , the proportion of sequence similarity changes abruptly to mostly zero within the S-locus region . Our results show that transposable elements are a major component of the S-locus region , as previously noted in other taxa [47] , [48] , [54] . On a wide scale , their density is higher in most S-haplotypes than in the genomic background . Such accumulation has already been observed in other genomic regions involved in mating-type and gender determination , and is not exclusive to the S-locus . Bachtrog [2] investigated four regions of the neo-sex chromosomes , containing homologous gene pairs , in Drosophila miranda . In each case , the neo-Y showed several transposable elements insertions that were absent from the neo-X . Similarly , Marais et al . [5] analyzed genetic degeneration of the Y chromosome in Silene latifolia , by examining seven sex-linked genes . Comparison of Y-linked genes and their X-linked homologs provided evidence that some of the Y-linked genes showed higher intron sizes , due to the accumulation of transposable elements . In the mating-type locus of the basidiomycete Ustilago hordei , sequencing of one of the two haplotypes , MAT-1 , revealed that this genomic region was particularly rich in both retroelements and repetitive DNA compared to U . maydis , in which the a and b complexes are unlinked [88] . Similarly , the chromosome carrying the mating-type locus in the fungus Microbotryum violaceum was found to be enriched in transposable elements as compared to autosomal chromosomes [10] . In A . thaliana , Wright et al . [89] compared the transposable elements accumulation in chromosome arms and in low-recombining regions surrounding the centromeres , i . e . centromeres , pericentromeric regions and heterochromatic knobs . These regions of reduced recombination were shown to exhibit greater TE copy numbers than chromosome arms , particularly for Gypsy retrotransposons and EnSpm transposons . Interestingly , our results showed that precisely these two TE families present densities twice higher in the S-locus than in the overall genome of A . lyrata , suggesting that the increased TE density noticed in the S-locus is effectively linked to the restricted recombination . Strikingly , we found that not all haplotypes present the same TE coverage , with dominant S-haplotypes ( SRK phylogenetic classes III and IV ) having higher TE density than those belonging to recessive classes ( I and II ) . Signatures of intragenic recombination have been found in SRK only in S-haplotypes belonging to recessive classes I and II [30] . It was suggested that recombination can occur only in individuals carrying two copies of the same functional S-haplotype , which is most probable for recessive haplotypes , because they are predicted to have high frequencies in natural populations [42] . Indeed , in A . lyrata , the most recessive haplotype was 12 . 75 times commoner than the most dominant haplotypes in Icelandic natural populations [90] . Our observation that TE density is inversely related to haplotype population frequency also suggests that recombination plays a role in preventing TE accumulation in the S-locus . In addition , haplotype frequency also influences the effective population size of gene copies within S-haplotypes [68] , so that genetic drift will be stronger in low-frequency dominant haplotypes ( in agreement with the mutational-hazard model of Lynch and Conery [91] ) , and this may also affect TE accumulation . Sex chromosomes in mammals also differ in opportunities for recombination and in effective population sizes [91] . Recessive S-haplotypes tend to behave like the X chromosome , and dominant ones are more like the Y chromosome . These differences may be an important source of variation of the size of the S-locus among haplotypes .
High Molecular Weight ( HMW ) DNA was prepared from young leaves of seven A . halleri and four A . lyrata haplotypes . For each extraction , approximately 20 grams of frozen leaf tissue was ground to powder in liquid nitrogen with a mortar and pestle used to prepare megabase-size DNA embedded in agarose plugs . HMW DNA of the various genotypes was prepared as described by Peterson et al . [92] and modified as described in [93] . Embedded HMW DNA was partially digested with HindIII ( New England Biolabs , Ipswich , Massachusetts ) , subjected to two size selection steps by pulsed- field electrophoresis , using a BioRad CHEF Mapper system ( Bio-Rad Laboratories , Hercules , California ) , and ligated to pIndigoBAC-5 HindIII-Cloning Ready vector ( Epicentre Biotecnologies , Madison , Wisconsin ) . Pulsed-field migration programs , electrophoresis buffer , and ligation desalting conditions were performed according to [94] . To evaluate the average insert size of each library , BAC DNA was isolated from about 384 randomly selected clones in each library , restriction enzyme digested with the rare cutter NotI , and analyzed by Pulsed-Field Gel Electrophoresis ( PFGE ) . All fragments generated by NotI digestion contained the 7 . 5 kb vector band and various insert fragments . Analysis of the insert sizes from the various BAC libraries showed a mean insert size comprised between 80 kb and 175 kb . Since the haploid genome of A . lyrata and A . halleri is estimated around 230 Mb and 250 Mb respectively , we picked the number of BAC clones required to obtain a library coverage of 5 genome equivalents . High-density colony filters were prepared from all the nine BAC libraries constructed using a robotic workstation QPix2 XT ( Genetix ) . BAC clones were spotted in double using a 5×5 or 6×6 pattern onto 22×22 cm Immobilon-Ny+ filters ( Millipore Corporate , Billerica , Massachusetts ) . On each filter , 27 648 to 41 472 unique clones were spotted in duplicate , and clones were grown at 37°C for 17 h . Filters were then processed as follows: ( 1 ) denaturation on Whatman paper soaked with a solution of 0 . 5 M NaOH and 1 . 5 M NaCl for 4 min at room temperature , and for 10 min at 100°C , ( 2 ) neutralization on Whatman paper soaked with 1 M TrisHCl pH 7 . 4 , and 1 . 5 M NaCl for 10 min , incubation in a solution of 0 . 25 mg/mL proteinase K ( Sigma Aldrich , St . Louis , Missouri ) for 45 min at 37°C , baking for 45 min at 80°C , and ( 3 ) fixation by UV on a Biolink 254 nm crosslinker ( Thermo Fischer Scientific , Waltham , Massachusetts ) with an energy of 120 , 000 µJoules . Radiolabelling of probes and hybridization of the filters were performed as described in [93] . Hybridized filters were imaged with a Storm 860 PhosphorImager ( GE Healthcare , Little Chalfont , UK ) , and analyses were performed using the HDFR software ( Incogen , Williamsburg , Virginia ) . Positive BAC clones detected by hybridization were validated individually by PCR amplification using the primer pairs used for probes synthesis ( Table S3 ) , and visualisation of PCR products after agarose gel electrophoresis . A total of fourteen BACs covering the S-locus region of 11 S-haplotypes were sequenced in this study ( two partially overlapping BACs were needed for haplotypes Al28 , Ah15 and Ah28 ) : eleven BACs were sequenced at Genoscope; two BACs ( containing haplotypes Al39 and Ah43 ) were sequenced at CNRGV; and a last one ( haplotype Al14 ) was sequenced by Beckman Coulter 485 Genomics . . All clones were sequenced using a 454 multiplexing technology on Titanium sequencer ( www . roche . com ) . De-novo assembly was performed by Newbler ( www . roche . com ) for each S-haplotype and only contigs representing the extremities of the BACs were organized at this step . BAC sequences covering the 11 S-haplotypes were obtained in two to nine contigs . Suggestion of orientation was provided with assembly for some sequences , but in most cases , only the first and last contigs were oriented . The relative order and orientation of other contigs were therefore unknown . When exons of SCR or SRK were in two different contigs ( i . e . haplotypes Al01 and Ah15 for SRK , Ah03 and Al39 for SCR ) , primers were defined with Primer3 [95] on both contigs . Because of the presence of repeated sequences including transposable elements , long-range ( using TaKaRa LA Taq Polymerase ) rather than classical PCR were performed in order to confirm the contiguity of the contigs . Annotation of BAC sequences was performed using two gene structure prediction programs with Arabidopsis parameters , FGENESH [96] and GENSCAN [97] . FGENESH has the advantage of being more accurate in detecting Arabidopsis genes but GENSCAN is more sensitive . Detected ORFs were blasted using BLASTX [98] and the obtained proteins were then aligned on BAC sequences with SPALN [99] and FGENESH+ [96] softwares . Because of its high nucleotide diversity , SCR was rarely detected by these two programs . Known SCR proteins were therefore aligned on BAC sequences using the semiglobal alignment procedure implemented on ALN [83] , which is more sensitive than SPALN and FGENESH+ . The results were then examined by eye in order to find the SCR gene and the cysteine residues that characterize this protein . Transposable elements were annotated with CENSOR [72] using the A . thaliana repetitive elements [v16 . 02] database of Repbase Update [100] . The results were then filtered and defragmented with PLOTREP [73] , using a minimum coverage of merged fragments of 10% . The full BAC sequences were aligned and compared using the “glocal” alignment procedure [101] implemented in VISTA [64] . This kind of alignment is able to detect rearrangements and inversions in sequences , and is particularly appropriate for divergent regions like the S-locus . Protein sequences of genes were aligned with CLUSTALW [102] . Alignments were then manually adjusted and phylogenetic trees were constructed using MEGA version 5 [103] , according to a Minimum Evolution ( ME ) analysis with the maximum composite likelihood method . The congruence between topologies of SCR and SRK trees was tested by computing an index of congruence , based on the size of their maximum agreement subtree , and comparing its value to a null-hypothesis distribution obtained by simulation of random trees [69] . A PERL script was developed to compare TE density between the twelve S haplotypes and the A . lyrata genome . CENSOR [72] was used in local on BAC sequences , excluding the S-locus flanking regions , and on non-overlapping windows of 100 kb along the eight chromosomal sequences of the A . lyrata genome version Araly1 ( http://genome . jgi-psf . org/Araly1/Araly1 . download . html [59] ) . Densities were thus calculated for each transposable element family in the A . lyrata genome and in the S-locus , according to the classification in [104] .
|
Self-incompatibility is a common genetic system preventing selfing through recognition and rejection of self-pollen in hermaphroditic flowering plants . In the Brassicaceae family , this system is controlled by a single genomic region , called the S-locus , where many distinct specificities segregate in natural populations . In this study , we obtained genomic sequences comprising the S-locus in two closely related Brassicaceae species , Arabidopsis lyrata and A . halleri , and analyzed their diversity and patterns of molecular evolution . We report compelling evidence that the S-locus presents many similar properties with other genomic regions involved in the determination of mating-types in mammals , insects , plants , or fungi . In particular , in spite of their diversity , these genomic regions all show absence of similarity in intergenic sequences , large depth of genealogies , highly divergent organization , and accumulation of transposable elements . Moreover , some of these features were found to vary according to dominance of the S-locus specificities , suggesting that dominance/recessivity interactions are key drivers of the evolution of this genomic region .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"genetics",
"biology",
"genomics",
"evolutionary",
"biology",
"genetics",
"and",
"genomics"
] |
2012
|
Contrasted Patterns of Molecular Evolution in Dominant and Recessive Self-Incompatibility Haplotypes in Arabidopsis
|
Lager-style beers constitute the vast majority of the beer market , and yet , the genetic origin of the yeast strains that brew them has been shrouded in mystery and controversy . Unlike ale-style beers , which are generally brewed with Saccharomyces cerevisiae , lagers are brewed at colder temperatures with allopolyploid hybrids of Saccharomyces eubayanus x S . cerevisiae . Since the discovery of S . eubayanus in 2011 , additional strains have been isolated from South America , North America , Australasia , and Asia , but only interspecies hybrids have been isolated in Europe . Here , using genome sequence data , we examine the relationships of these wild S . eubayanus strains to each other and to domesticated lager strains . Our results support the existence of a relatively low-diversity ( π = 0 . 00197 ) lineage of S . eubayanus whose distribution stretches across the Holarctic ecozone and includes wild isolates from Tibet , new wild isolates from North America , and the S . eubayanus parents of lager yeasts . This Holarctic lineage is closely related to a population with higher diversity ( π = 0 . 00275 ) that has been found primarily in South America but includes some widely distributed isolates . A second diverse South American population ( π = 0 . 00354 ) and two early-diverging Asian subspecies are more distantly related . We further show that no single wild strain from the Holarctic lineage is the sole closest relative of lager yeasts . Instead , different parts of the genome portray different phylogenetic signals and ancestry , likely due to outcrossing and incomplete lineage sorting . Indeed , standing genetic variation within this wild Holarctic lineage of S . eubayanus is responsible for genetic variation still segregating among modern lager-brewing hybrids . We conclude that the relationships among wild strains of S . eubayanus and their domesticated hybrids reflect complex biogeographical and genetic processes .
Humans changed from living in hunter-gatherer societies to agricultural societies in part through the domestication of animals and plants [1 , 2] . At the same time , humans began unwittingly domesticating microorganisms for the production of fermented beverages and foods , but the underlying source populations and genetic processes for microbial domestication are not well understood [3] . Beer is the most common fermented beverage in the world and can be classified as ale or lager , depending on the fermentation conditions and yeasts used . Ale-style beers are mainly produced by strains of S . cerevisiae [4] . In contrast , 94% of the beer market is dominated by lager-style beers , which are fermented at colder temperatures by allopolyploid hybrids of S . cerevisiae x S . eubayanus ( syn . S . pastorianus syn . S . carlsbergensis ) [5] . Two hybrid lineages of lager-brewing yeasts have been described based on genome content and phenotypic traits [6–9] , leading to extensive debate about their origins . The two simplest models proposed to explain the origins of the Saaz and Frohberg lineages are through a single shared hybridization event [9–11] or through two or more independent hybridization events [6 , 12–15] . More complex models involving backcrossing have also been discussed by several authors [9–11 , 14 , 15] . All known modern lager strains are aneuploid . Genetic contributions from S . eubayanus have been argued to confer enhanced cold-tolerance , while genetic contributions from S . cerevisiae may confer other adaptions to the brewing environment , such as maltotriose fermentation [16–19] . Although the S . cerevisiae parent of lager yeasts seems to be closely related to modern ale strains [6 , 13 , 15] , identifying close relatives of the S . eubayanus parent has proven more challenging . Since the discovery of the species in 2011 in Patagonia , South America [5] , rare strains of S . eubayanus have been isolated in North America [20] , Asia [21] , and New Zealand [22] . Other than interspecies hybrids [5 , 23] , no European isolates of S . eubayanus have been reported . Genome sequence comparisons have shown the Patagonian type strain to be 99 . 56% identical to the S . eubayanus subgenome of a lager-brewing hybrid [5] , while a Tibetan isolate was shown to be 99 . 82% identical [21] . Previous population and phylogenetic studies of S . eubayanus suggest that it may contain up to five known phylogenetically distinct clades . Two distinct and highly diverse populations have been described in South America ( Patagonia A and Patagonia B ) where they have been commonly associated with Nothofagus [20] , as well as Araucaria araucana [24] . Recently , an isolate from New Zealand was shown to belong to the Patagonia B clade by multi-locus phylogenetic analysis [22] . Previously isolated North American strains were shown to be the result of recent admixture between the two Patagonian populations [20] . Three lineages have been isolated in Asia , mostly in association with Quercus , including the Tibetan lineage and two early-diverging lineages that could be regarded as distinct subspecies ( Sichuan and West China ) [21] . Analyses of population differentiation and genetic diversity have not been performed on the latter three lineages , and all five lineages have not been thoroughly analyzed together in the same phylogenetic study . To improve our understanding of the genetic diversity and phylogeography of S . eubayanus and its domesticated European hybrids , we have integrated existing multi-locus datasets and added several new isolates from North America ( North Carolina , Washington , and New Brunswick ) . To extend these analyses , we have also performed whole genome sequencing ( WGS ) on available isolates . These results support the existence of a relatively low-diversity Holarctic lineage , which includes wild isolates from Tibet and North Carolina , as well as the hypothetical ancestor of the European interspecies hybrids . Depending on the region of the genome examined , this Holarctic lineage is embedded within or sister to one of the Patagonian populations of S . eubayanus . Genomic analyses further show that none of the known wild S . eubayanus strains is the sole closest relative to the lager-brewing hybrids . Instead , we infer that lager yeasts drew from alleles that were segregating among a Holarctic lineage of S . eubayanus .
Our ongoing high-sugar enrichment surveys of yeast from soil , leaves , bark , mushrooms , and other natural substrates in North America isolated seven new strains of S . eubayanus: one from Washington State , USA; two from North Carolina , USA; and four from New Brunswick , Canada ( Fig 1A , S1 Table ) . The new S . eubayanus strains were isolated from novel tree hosts , including the bark of Cedrus sp . , the bark and soil of Pinus taeda , and the bark of Quercus rubra . North American isolates of S . eubayanus remained quite rare overall ( <1% of yeast isolates ) , except at specific sampling sites , and were only slightly biased toward the tree order Fagales ( S1 Text , S1 Fig ) . To determine how the new North American strains are related to South American [5 , 20] , Asian [21] , and New Zealand strains [22] , we performed multi-locus phylogenetic analyses . Specifically , we partially sequenced nine nuclear coding sequences and three nuclear intergenic regions , consisting of a total of ~9 . 8 kbp , as well as one mitochondrial gene ( 500 bp ) . Existing multi-locus data was utilized at this stage , rather than WGS data , because the Chinese strains are not available for study . North American strains displayed three different types of ancestry: 1 ) the strain from Washington was embedded within the Patagonia B clade and was more closely related to the strain from New Zealand than any other Patagonia B strain , 2 ) the strains from New Brunswick were identical at these loci to three previously characterized admixed strains from Wisconsin , USA [20] , and 3 ) the strains from North Carolina were closely related to the strains from Tibet and lager beer ( Fig 1B , S1 Text ) . This latter "Holarctic" subgroup of strains ( Tibet , North Carolina , and Lager ) was well supported phylogenetically and was more closely related to the Patagonia B clade than to any other population ( Fig 1B ) . Phylogenetic supernetwork analysis and examination of the individual gene trees revealed a complex history for the strains in the Patagonian populations and their close Holarctic relatives , but it failed to unambiguously identify the closest relative of lager yeasts ( S2 and S3 Figs , S1 Text ) . To determine the consensus relationships among the wild populations of S . eubayanus and the domesticated lager-brewing hybrids , we compared the complete genome sequences of 33 strains , including representatives of both known lager yeast lineages ( Saaz and Frohberg ) and S . uvarum as the outgroup . In contrast to previously reported topologies citing a personal communication [25] and weak support in a multi-locus dataset [22] , WGS data strongly agreed with our multi-locus phylogenetic tree and placed the Patagonia A population as an outgroup to a clade containing the Patagonia B population plus the strains from the Holarctic lineage ( Fig 1C ) . Even with WGS data , it remained unclear whether the Holarctic subgroup was embedded within the Patagonia B population or was sister to it . In contrast , the New Zealand strain was closely related to the Washington strain , both falling within Patagonia B . These analyses further showed that , on average , the S . eubayanus subgenomes of both the Saaz and Frohberg lager yeast lineages were more closely related to the representative strain from Tibet than to known strains from North Carolina or Patagonia . Nonetheless , analysis of the full single nucleotide polymorphism ( SNP ) dataset revealed extensively conflicting phylogenetic signals , which are displayed by the presence of nodes subtended by multiple edges in a phylogenetic network ( Fig 1D ) . Concatenated phylogenies display the consensus topology supported by a dataset , which can obscure phylogenetic incongruence due to recombination , incomplete lineage sorting , and other biological processes . When genome-scale datasets are used , maximum support values can be obtained , even when different loci strongly support conflicting topologies [26 , 27] . To explore how recombination within and between populations has influenced the ancestry of S . eubayanus strains , we developed a simple and easily visualized test statistic and assessed its performance on one of the seven nearly identical admixed strains from North America ( Fig 2D ) . First , across the genome , we plotted the average pairwise nucleotide sequence divergence ( and standard deviation ) of this strain compared to the Patagonia B and Patagonia A strains , clearly demonstrating regions more closely related to one population or the other ( Fig 2A ) . This approach also revealed genomic regions of high genetic diversity within populations ( Fig 2A ) ( e . g . the broader standard deviations of the left arm of chromosome IV among Patagonia A , and of the left arm of chromosome VII among Patagonia B strains ) . Next , for each window , we calculated the log2 of the pairwise divergence ratio using the strain with the minimum pairwise divergence value from each population . This ratio produced sharp transitions between positive and negative values , which corresponded to likely recombination breakpoints ( Fig 2B ) . Our quantitative log2 ratio approach was generally concordant with a well-established program ( PCAdmix ) that uses a principal component analysis ( PCA ) -based method with hidden Markov model smoothing to assign ancestry to chromosomal regions according to the population contributing to it ( Fig 2C ) . All seven admixed strains shared the same population ancestry in each chromosomal region , suggesting a recent radiation of this admixed lineage across the Great Lakes-Saint Lawrence Seaway . Similar plots were constructed to determine whether the sequenced Tibetan strain was the closest relative of lager yeasts at all loci or whether there was indeed evidence for a more complex ancestry ( Fig 3 ) . Although most of the genomes of both the Saaz and Frohberg lager representatives were more closely related to the Tibetan genome than to the North Carolina genomes ( i . e . log2 divergence ratio values < 0 ) , 19 regions were more closely related to the North Carolina genomes in both the Saaz and Frohberg strains ( i . e . log2 divergence ratio > 0 . 118 or 0 . 096 for Saaz and Frohberg , respectively , unbiased P < 0 . 019 , permutation test ) ( Figs 3B , 3D and 4A ) . Each of these regions was supported by PCAdmix ( Fig 4B ) , and PCAdmix detected several additional regions where the lager strains seemed to be more closely related to the North Carolina strains than to the Tibetan strain . The log2 ratio statistic and PCAdmix define windows differently , either based on physical genomic distance or the number of SNPs , respectively . Therefore , as expected , the methods did not always partition genomes in exactly the same places . Strong support for this alternative topology was confirmed by conventional phylogenetic analyses ( Fig 4C and 4D , S4 Fig ) . In a handful of cases , a Patagonia B representative was actually more closely related to the parent of one or both of the lager lineages than the Tibetan strain was ( S5 and S6 Figs ) . These regions could be due to incomplete lineage sorting , introgression , or different rates of evolution among wild S . eubayanus strains , but overall , they show that lager yeasts and wild strains of S . eubayanus have complex ancestries . In particular , none of the known wild isolates of S . eubayanus is the sole closest relative to lager-brewing strains . Instead , as in the case for most natural , sexually reproducing species , the data suggest an important role for outcrossing and incomplete lineage sorting in maintaining genetic variation and creating recombinant individuals . Surprisingly , comparison of the log2 divergence ratio values of the Saaz and Frohberg representatives against the North Carolina strains and the reference of Tibet ( Fig 4 , S5A and S6A Figs ) highlighted at least five genomic regions where the ancestries of the Saaz and Frohberg representatives differed dramatically ( Fig 4A ) . Several additional loci also had non-overlapping log2 ratios between Saaz and Frohberg , which provides further evidence of the complex ancestries of these lineages ( Fig 4A ) . We closely inspected seven regions where the log2 divergence ratio , PCAdmix , or both methods suggested that the lager lineages had different alleles ( Fig 4 ) . The discordant ancestries of three of these regions were strongly supported by conventional phylogenetic analyses ( Fig 4E–4G ) . In each case , the North Carolina strains were more closely related to one lager strain , while the Tibetan strain was more closely related to the other . To ensure that the phylogenetic signals in these three regions were not artifacts , we closely inspected them using several orthogonal methods , including de novo assembly , PCR , local investigation of conflicting phylogenetic signals , examination of heterozygosity , and examination of copy-number variants . For example , the strongest phylogenetic signal for the region on chromosome X came from a 3-kbp region that placed the Frohberg and Tibetan strains sister to each other on a long branch ( S7 Fig ) . Although this region contains a solo LTR in most strains , de novo assembly confirmed that the solo LTR was absent in the Tibetan and Frohberg strains and was not responsible for the phylogenetic signal . Additionally , although the Frohberg strain had multiple copies of the S . eubayanus subgenome in this region , there was no detectable heterozygosity . Heterozygosity was also too low in the other regions of phylogenetic interest to confound results ( S8 Fig ) ; indeed , overall these regions had less heterozygosity ( 1 . 08*10−4 and 8 . 49*10−5 heterozygous sites/bp for Saaz and Frohberg , respectively ) than the genome as a whole ( 2 . 08*10−4 and 4 . 86*10−4 heterozygous sites/bp for Saaz and Frohberg , respectively ) ( S9 Fig ) . Differences between the regions of interest and the genome as a whole in copy-number variation ( S8 and S9 Figs ) and genetic diversity ( S8 Fig , S2 Table ) were also not the cause of the phylogenetic incongruence . Instead , we infer that the Saaz and Frohberg strains examined possess different alleles that were drawn from standing variation segregating among wild strains of S . eubayanus . To delineate the number of populations of S . eubayanus and determine how well differentiated they are , we analyzed the multi-locus data from the complete strain set using STRUCTURE ( S1 Text ) . Strains from West China were inferred to be an independent population and excluded from subsequent analyses . Analyses of WGS data using multiple methods suggested that Patagonia A and Patagonia B-Holarctic were independent populations and recovered the admixed strains ( Fig 5 ) . Although divisions beyond K = 2 were not significant with STRUCTURE ( Fig 5A ) , principal component and coancestry analysis with fineSTRUCTURE provided some support for dividing Patagonia A into two subpopulations ( PA-1 and PA-2 , Fig 5B and 5C ) . Similarly , these analyses split Patagonia B-Holarctic into three subpopulations , one containing most of the non-admixed strains from Holarctic ecozone ( Holarctic: North Carolina , Lager , Tibet ) , one containing only S . eubayanus strains from South America ( PB-2 ) , and a final subpopulation containing South-American and non-South American strains ( PB-1 ) . These analyses also provided additional information about closest relatives of the admixed and lager strains . The fineSTRUCTURE coancestry heatmap suggested that PB-1 and PA-2 were the closest relatives of the admixed strains ( Fig 5B ) . These results were also supported by analysis of D-statistics , where the most significant values were obtained when PB-1 and PA-2 were tested as donors to the admixed strains ( S3 Table ) . Analysis with PCAdmix suggested that PB-1 contributed about 58% of the genome to the admixed strains , whereas PA-2 contributed 42% , results consistent with the phylogenetic analyses and an f4-ratio test ( S3 Table , Fig 1D ) . Analysis with PCAdmix for the lager genomes further suggested that strains more closely related to the Tibetan strain contributed 66% of the S . eubayanus genetic material , whereas strains more closely related to those from North Carolina contributed 34% ( S1 Text ) . Nonetheless , we caution that the few available data are best interpreted as pointing to the existence of standing variation across the Holarctic lineage , rather than direct ancestry or admixture involving these specific extant strains . These results , together with the nucleotide diversity statistics ( Fig 6A ) , the pairwise comparison of Fst , the distribution of SNPs ( Fig 6B ) , and phylogenetic analysis ( Fig 1B ) support at least four distinct populations of S . eubayanus: Patagonia A , Patagonia B-Holarctic , Sichuan , and West China ( Fig 6A ) . The nucleotide diversities of the West China population and the Holarctic lineage were lower than either population from Patagonia ( Fig 6A , S4 Table ) . In contrast to the other populations or groups , including the Holarctic lineage as a whole , only the 10 strains from Tibet had significantly negative values for Tajima’s D , Fu and Li's D , and Fu's F ( S4 Table ) . The Tibet group’s Fay and Wu’s H value was not significantly different from zero ( H = 0 . 76 P > 0 . 05 , calculated using Patagonia B strains as an outgroup ) , which is consistent with a neutral demographic explanation , such as a recent local population expansion across the vast region of Tibet surveyed .
The patterns of diversification and differentiation between S . eubayanus populations are remarkably reminiscent of those described recently for its sister species , S . uvarum ( S10 Fig ) [23] . Specifically , both species include early-diverging subspecies in East Asia or Australasia . Both species have two highly diverse , partially sympatric populations in Patagonia that are about 1% divergent in DNA sequence . In both cases , one of these populations is closely related to a relatively low-diversity lineage with a Holarctic distribution that gave rise to domesticated hybrid yeasts that ferment economically important products . In contrast to the process of introgression seen in domesticated strains of S . uvarum , lager yeasts were generated through allopolyploidization of S . eubayanus and S . cerevisiae . Genetic mechanisms of hybridization aside , the deep parallels between the diversifications of these two sister species in the wild suggest that similar biogeographical and ecological forces may explain their distributions . The presence of wild S . uvarum in Europe further suggests that Holarctic representatives of S . eubayanus are present , or may have been present in the past , somewhere in Europe . Although non-hybrid isolates of European S . eubayanus remain elusive , we expect European strains of S . eubayanus would have relatively low genetic diversity , belong to the Holarctic lineage , and be genetically similar to isolates from Tibet and North Carolina , as well as to the parents of lager yeasts . Importantly , any European strains that might eventually be discovered will not be the closest relative to all lager yeasts at all loci because , as this study shows , standing genetic variation in S . eubayanus made it through the bottleneck of hybridization that generated modern lager yeasts . All of the currently proposed models of hybridization are compatible with this data , including multiple hybridization events [6 , 12–15] , differential loss-of-heterozygosity among heterozygous ancestors [11] , or more complicated backcrossing scenarios [9–11 , 14 , 28] . The complexity of lager yeast ancestry means that identifying the alleles relevant for specific traits may require a broad sampling of S . eubayanus genetic diversity from across the Holarctic ecozone . In contrast to the frequent isolation of S . eubayanus from Nothofagus in Patagonia [5] , the rare Northern Hemisphere strains of S . eubayanus described here and in other recent studies [20 , 21] were isolated in association with several different tree genera ( S1 Fig ) . These findings suggest that our understanding of S . eubayanus ecology is still quite limited or may be an indication of its generalist character , as has recently been argued for S . cerevisiae [29] . Expanded sampling of substrates beyond the conventional hosts of Quercus and Nothofagus [30] , even in South America [24] , may be critical to gaining a fuller view of the ecological and genetic diversity of S . eubayanus . Additional isolates will also be key for evaluating competing demographic models to explain the relationship between the Holarctic lineage and the Patagonia B population . One possibility is that a large ancestral population was split by vicariance , perhaps as the climate warmed following the last glacial period . Alternatively , long-range dispersal could have occurred between the Northern Hemisphere and South America , potentially in either or both directions . The relative diversities of the Holarctic and Patagonia B lineages and the confinement of a signature of recent demographic expansion to the Tibetan strains argue that dispersal from South America into the Holarctic may be more likely . Nonetheless , the distribution of clades defies a simple explanation and appears to require cladogenic events in multiple locations , both for S . eubayanus and its sister species S . uvarum . Although humans undoubtedly played a role in selecting for the allopolyploid hybrids that became lager yeasts , human activity is not required to explain the spread of wild S . eubayanus across the Holarctic ecozone . Even conservative molecular clock estimations place all S . eubayanus cladogenic events , including the origin of the Holarctic lineage , well outside of the range of written human history ( S11 Fig ) . Moreover , no known strain is a close enough relative to the ancestor of lager yeasts to be compatible with human-mediated transfer to Europe via the Silk Road [21] or any hypothesis involving colonial era transfer to Europe from South America [5] or North America . How yeasts migrate is still controversial . Proposed natural mechanisms include long-range dispersal by birds [31 , 32] , short-range dispersal by insects [33] , or dispersal by wind [34] . The former may be particularly relevant because some bird migration flyways from Patagonia to Greenland or Alaska , overlap with European or Asian migration routes , respectively [35] . Clear cases for human-associated yeast dispersal have been made for industrial strains of S . cerevisiae , including the dispersal of Wine/European strains to wine-making regions all over the world [36–41] , as well as some interspecies hybrids used in wine production [42] . Interestingly , Wine/European strains of S . cerevisiae have retained considerable genetic diversity , perhaps because large effective population sizes were maintained and because of the semipermeable nature of the vineyard environment [41] . European strains of S . paradoxus have also been inferred to have been dispersed to North America and New Zealand , possibly in association with Quercus [25 , 39 , 43] . A recent population genomic analysis of the former case revealed extremely low levels of diversity and a coalescence date consistent with colonial era dispersal [44] . The genomic diversity that we observed among the admixed strains of S . eubayanus from Wisconsin and New Brunswick is also consistent with a very recent dispersal to opposite ends of the Great Lakes-Saint Lawrence Seaway . The number of inferred breakpoints ( 40 total crossovers , Fig 2B ) is similar to the number observed in one round of meiosis in S . cerevisiae [45] , and each Patagonian population seems to have contributed approximately half of their genomes . Since all seven admixed strains share the same breakpoints and have nearly identical genome sequences ( of 325 variable SNPs , only 37 differentiate Wisconsin from New Brunswick , Fig 2D ) , they are likely descended quite recently from a single individual that underwent haploselfing after an outcrossing event and one round of meiosis . Although we cannot be certain whether this dispersal across North America and the dispersal of S . paradoxus to North America were anthropic [44] , they demonstrate that recent continent-scale dispersal is detectable in yeast using WGS data . In contrast , the mean genetic distance among S . eubayanus Holarctic genomes is well over 100 times higher ( 0 . 1989% for the Tibetan , North Carolina , and lager strains versus 0 . 0013% for the admixed strains of S . eubayanus and 0 . 0009% for the North American strains of S . paradoxus from Europe ) . In conclusion , S . eubayanus biogeography and the origins of lager yeasts have proven more complex , but also much richer , than initially hypothesized . Here we have presented evidence that lager yeasts are derived from a relatively low-diversity lineage of S . eubayanus with a Holarctic distribution . These strains from the Holarctic lineage diversified from within one of two diverse populations found primarily in Patagonia . This pattern of diversification is similar to that of its sister species , S . uvarum . Although the S . eubayanus subgenomes of lager yeasts were drawn from the Holarctic lineage , none of the known S . eubayanus isolates is their sole nearest relative . Indeed , for the first time , we have shown that variation segregating among wild S . eubayanus persists among the allopolyploid lager-brewing yeasts . These findings strongly suggest that further sampling of the Northern Hemisphere for S . eubayanus will , not only enhance our understanding of the natural history and genetic diversity of this important species , but offer valuable insight into the sources of diversity among modern brewing strains .
New S . eubayanus strains were isolated from two locations in the USA , Washington State ( yHKS509 ) and North Carolina ( yHRVM107 , yHRVM108 ) , by following previously described high-sugar enrichment protocols at 10°C [46] . Four new S . eubayanus were isolated by enrichment from New Brunswick ( yHDPN421-yHDPN424 ) , Canada , as previously described [47] , with the exception that the samples were incubated in liquid medium for seven months at 4°C , followed by a second culture step on solid medium for two weeks at 4°C . Strains were initially identified by PCR and Sanger-sequencing of the ITS region of the rDNA locus ( see S1 Text ) . Complete results of these yeast biodiversity surveys will be reported elsewhere , and our recent publications represent less than half of the yeast strains isolated [46 , 47] . For the phylogenetic and nucleotide diversity analyses , we selected genes and intergenic sequences to integrate the maximum amount of sequencing data available from previous studies [20–22] ( S1 Table ) . Additional genes from Patagonian and the newly isolated S . eubayanus strains were PCR-amplified and Sanger-sequenced ( S4 Table ) . Reads from sequenced genes were assembled using the STADEN Package v1 . 7 [48] . The COX2 sequence of strain CDFM21L . 1 was assembled in GENEIOUS v6 . 1 . 6 using the reads retrieved by BLASTing the S . eubayanus COX2 sequence against SRR1507225 from the SRA database of NCBI [21] . Individual genes of strain P1C1 were retrieved by BLASTing against its genome assembly ( S1 Text ) . New sequences generated were deposited in GenBank under accession numbers KR871406-KR871626 . Phylogenetic gene trees and the supernetwork were reconstructed following our previous approach [20] . The supernetwork was reconstructed using the relative average for edge weights and using the filter option to discard the splits from PDR10 ( a gene undergoing balancing selection or reciprocal introgression between some populations ) ( Dataset A ) ( S1 Text ) . An additional Neighbor-Net phylogenetic network was reconstructed for the SNP dataset using SplitsTree v4 . 12 . 8 [49] . Genomic libraries for available S . eubayanus strains ( S1 Table ) , one representative strain from the Saaz lineage of lager yeast ( CBS1503 ) , and one representative strain from the Frohberg lineage of lager yeast ( W34/70 ) were generated as described previously [50] and sequenced using Illumina paired-end sequencing ( S5 Table ) . Details on the identification of high-quality single nucleotide polymorphisms ( SNPs ) can be found in S1 Text . Illumina reads were deposited in the SRA database of NCBI under accession number SRP064616 . After removing positions with gaps in any strain , whole genome nucleotide divergence graphs were constructed by calculating the pairwise number of segregating sites per nucleotide or divergence ( d ) in windows of 50 , 000 bp using the PopGenome package for R [51] . To compare how closely related various strains of interest ( i . e . lager or admixed ) were to a portion of the genome of two defined reference strains ( e . g . North Carolina and Tibet ) , the value of the log2 of the ratio of the d values were calculated for each window ( see S1 Text ) . The whole genome phylogenetic tree was reconstructed from WGS data using RAxML v8 . 1 [52] . For phylonetwork and population analyses , SNPs were selected using strict coverage and quality filters ( details in S1 Text ) . Based on the comparisons of the log2 divergence ratios or the PCAdmix results , genomic regions of interest were extracted for phylogenetic analyses ( see S1 Text ) . Regions of interest were extracted from whole genome assemblies reconstructed using iWGSv1 . 01 [53] . A multi-locus concatenated alignment from Dataset A ( ~7 . 7 kbp ) was generated using FASconCAT v1 . 0 [54] . Multi-locus concatenated alignment and WGS data were used for diversity statistics , polymorphism comparisons , and population analyses ( see S1 Text ) . The concatenated alignment was also used to reconstruct a Maximum-Likelihood phylogenetic tree in RAxML v8 . 1 using the same parameters as for the individual gene trees . A second recombinant-free concatenated alignment of the coding sequences from Dataset B ( Dataset A where IntMD , MET2 , and MLS1 sequences , which had low information content , were discarded ) was generated using IMGC [55] and FASconCAT . The 380 fourfold degenerate sites in this alignment were used to estimate divergence times . Divergence time reconstruction was performed as we described previously [20] . The number of populations for the SNP dataset were inferred using STRUCTURE v2 . 3 . 4 [56] . fineSTRUCTURE v2 [57] was used to generate coancestry heatmaps and to perform PCA . Parental contributions to the genomes of Wisconsin , New Brunswick , Saaz , and Frohberg strains were estimated using a hidden Markov model of evolution implemented in PCAdmix v1 . 0 [58] , and chromosomes were partitioned according to the output results . Analyses of f- and D-statistics were performed in ADMIXTOOLS v3 . 0 [59] .
|
Yeasts are key industrial microbes , most notably Saccharomyces cerevisiae , which is used to make a variety of products , including bread , wine , and ale-style beers . However , lager-style beers are brewed with interspecies hybrids of S . cerevisiae x Saccharomyces eubayanus . After its discovery in South America in 2011 , rare strains of S . eubayanus have also been isolated outside of South America . Here we compare the genome sequences of several new and recent isolates of S . eubayanus from South America , North America , Australasia , and Asia to unravel the relationships of these wild isolates and their domesticated European hybrids . Two South American populations have the highest genetic diversity . One of these populations is closely related to a relatively low-diversity lineage that is spread across the Northern Hemisphere and includes the S . eubayanus parents of lager yeasts . Interestingly , we find that none of the wild isolates of S . eubayanus is the sole closest relative of lager-brewing hybrids . Instead , we show that standing variation among wild S . eubayanus strains contributed to the genetic makeup of lager yeasts . Our findings highlight the complex ancestries of lager yeasts and the importance of broader sampling of wild yeasts to illuminate our understanding of the sources of genetic variation among industrial hybrids .
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2016
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Complex Ancestries of Lager-Brewing Hybrids Were Shaped by Standing Variation in the Wild Yeast Saccharomyces eubayanus
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We study the statistics of spike trains of simultaneously recorded grid cells in freely behaving rats . We evaluate pairwise correlations between these cells and , using a maximum entropy kinetic pairwise model ( kinetic Ising model ) , study their functional connectivity . Even when we account for the covariations in firing rates due to overlapping fields , both the pairwise correlations and functional connections decay as a function of the shortest distance between the vertices of the spatial firing pattern of pairs of grid cells , i . e . their phase difference . They take positive values between cells with nearby phases and approach zero or negative values for larger phase differences . We find similar results also when , in addition to correlations due to overlapping fields , we account for correlations due to theta oscillations and head directional inputs . The inferred connections between neurons in the same module and those from different modules can be both negative and positive , with a mean close to zero , but with the strongest inferred connections found between cells of the same module . Taken together , our results suggest that grid cells in the same module do indeed form a local network of interconnected neurons with a functional connectivity that supports a role for attractor dynamics in the generation of grid pattern .
Grid cells are neurons in the medial entorhinal cortex ( MEC ) , one synapse away from the hippocampus , that show a strikingly regular spatial selectivity [1] . Each grid cell has several firing fields that spread out in a hexagonal pattern , tessellating the environment in which the animal navigates . The locations of these firing fields are unaffected by the velocity of the animal , and they persist in the absence of external landmarks , suggesting that they make up an intrinsic metric for space [1–3] . These cells were first discovered in rodents [1 , 2] , but have recently also been reported in bats [4] , monkeys [5] , and humans [6] , supporting the possibility that grid cells form a part of the neural circuitry underlying the brain’s internal representation of space in all mammals . Two main properties of grid cells are their spacing ( the shortest distance between two firing fields ) and their orientation relative to an axis of the environment . Anatomically close grid cells tend to have the same orientation and spacing , with spacing increasing along the dorsoventral axis of MEC [1 , 3] . This increase is stepwise rather than continuous , such that grid cells can be clustered with respect to spacing . These clusters also share other properties , such as orientation , and are therefore referred to as modules [7] . A third property of grid cells is their spatial phase , which is defined as the location of the grid pattern relative to a reference point in the environment . For cells with similar grid pattern , i . e . cells from the same module , one can also measure the difference in spatial phase by calculating the shortest distance between firing fields of two cells . No apparent relationship between the anatomical distance and the difference in spatial phase of pairs of neurons has been observed [1] . Since their discovery , grid cells have been under intense investigation , with studies ranging from experimental work to theoretical models , in hopes of revealing the underlying network mechanisms behind their coding; see [8 , 9] for recent reviews . In particular , population-wise response properties [1 , 7 , 10] support the idea that the formation of grid cells is predominantly a network phenomenon , and that recurrent connectivity in MEC plays an important role . The main network model of grid cells , the continuous attractor model , would suggest that the hexagonal firing of grid cells emerges due to specific connectivity patterns between the neurons . In several of these models neurons are considered to be arranged in a two-dimensional network according to their phase . Cell pairs beyond a certain phase distance inhibit each other , while those closer to each other are coupled by excitation [11–13] , or less inhibition [13 , 14] , as idealized by a ‘Mexican hat’ type of connectivity . Although connectivity plays important roles in network models of grid cells and in shaping neuronal correlations , little has been done to study the correlation structure and functional connectivity in the MEC in vivo , as well as how they change with properties of grid cells , e . g . phase separation and theta modulations . In other words , statistical analyses of multi-neuronal spike trains of the type routinely performed on data recorded from other parts of the nervous system [15–17] , is still lacking . Such analyses can shed light on how grid cells encode information at the population level and how they interact with each other , providing substance for understanding the network mechanisms behind the formation of grid cells . In this paper we aimed at studying the statistical properties of grid cells’ multi-neuronal spike trains by analyzing recordings from two rats while they foraged freely in two-dimensional environments . We therefore first measured the correlations between these cells , beyond what is expected from space dependent rate variations , using the same approach as [18]: we averaged the Pearson correlation coefficients between firing rates of pairs of neurons during multiple passes through spatial bins covering the environment . With spatial bins small enough the effect of possible correlations due to rate covariations between two cells is removed . These correlations are referred to as noise correlations . We found that these correlations decay as the phase difference between cell pairs increases . This is consistent with previous analyses of pairs of grid cells recorded on a linear track [18] . Second , we fit a statistical model that assumes a pairwise maximum entropy distribution over the spikes generated in a time bin , given the spike pattern in the previous time bin and external covariates also referred to in the text as external fields . This model is known in the statistical physics community as the kinetic Ising model and belongs to the class of generalized linear models ( GLMs ) [19] with short time memory kernels . We considered an extensive list of external covariates known to modulate the firing of grid cells to explain the covariations in firing rates of neurons , ranging from spatially and temporally constant input , to spatial fields formed as the sum of Gaussian basis functions , as well as fields for speed , theta oscillations , and head and running directions . We evaluated the explanatory power of these models by comparing their likelihood values and found that speed , head direction and running direction had little power in explaining the data , while theta oscillation phase and pairwise couplings had more explanatory power . Although there were variations in terms of the relative strength of the couplings depending on the assumptions about the external fields , we consistently found that the inferred connections maintained a pattern that supports the attractor network hypothesis: cells with nearby phases tend to excite each other while those further apart inhibit each other . We also found that the strongest connections were among cells within the same module , that the connections were both negative and positive , and that none of our conclusions were sensitive to data limitations .
To calculate correlations between pairs of grid cells , beyond what is expected from spatial rate covariations , we binned the spike data into 1 ms intervals and smoothed the firing rates with a 20 ms Gaussian filter . The trajectory of the animal was then binned spatially by dividing the environment into a number of N × N square boxes , using different values of N = 2 , 3 , 4 , 5 , 10 , 15 , 20 , 40 , 75 . Noise correlations , Cij , between cells i and j were then determined as the mean of the Pearson correlation coefficients , ρ , calculated over the trajectories through each spatial bin ( see Material and Methods ) . As shown in Fig . 1 , in the case of dividing the environment into 20 × 20 spatial bins , we found noise correlation values close to zero , or slightly negative , for cells with non-overlapping spatial fields . On the other hand , cell pairs close in phase distance showed positive noise correlation values that increased for cells closer to each other in phase; see Fig . 1A and B . The slope ( β̂ ) and intercept ( α̂ ) of a linear regression line ( not shown ) are β̂=−0 . 22 and α̂=0 . 09 for data set 1 , and β̂=−0 . 25 and α̂=0 . 11 for data set 2 , all significantly different from 0 ( t-test , P < 0 . 001 ) . Since data set 1 included neurons from 3 separate modules , we also studied the dependence of the noise correlations on the phase difference between cells for each three modules separately . Except for the module with the largest field spacing ( Fig . 1E ) , where the phase dependence was weak ( intercept and slope of linear regression not significantly different from 0 ( t-test , P>0 . 7 ) ) , the modules showed a significant pattern similar to that of all modules pooled together shown in Fig . 1A ( intercept and slope of linear regression significantly different from 0 ( t-test , P<0 . 001 ) ) . Similar results were found when other spatial bin sizes were used . This extends the results of [18] to two dimensions and also shows the variations in the phase dependence of the correlations to the module size . Good empirical estimates of the noise correlations , as defined above , require that the rat makes enough passes through each spatial bin during the recording session . This means that the bins cannot be too small , otherwise there would be very few visits to most of the bins , and some of the bins may never be visited at all . On the other hand , if the bins were too big , the variations in rate from one pass through the bin to another would be be too large and , therefore , Cij would not exclude the rate covariations . We , therefore , looked at how consistent our estimates of the correlations were as a function of the spatial bin size by calculating the Pearson correlation coefficient between the correlations measured , using a random half of the visits to each spatial bin with those measured from the other half ( see Material and Methods ) . The most stable estimate was with 20 bins per side of the box ( or 7 . 5 cm ) , which is what we have used in Fig . 1 . In this best case scenario , for data set 1 , the Pearson correlation coefficient is 0 . 56 for the full data , with both halves of the data in all 20 sets of random halves still demonstrating the phase dependent pattern shown in Fig . 1 . Cells with nearby grid patterns had stronger positive correlations , while those further apart in phase demonstrated a slightly negative , or no correlations ( the slope and intercept of the linear regression lines were all significantly different from 0 ( t-test , P < 0 . 03 ) ) . This was also the case for the 20 random halves of data set 2 . The pairwise correlation analysis done here is a good first step , however , it suffers from a number of shortcomings . First of all , it is really a pairwise measure , which excludes the interactions with other neurons , and thus a perceived correlation between two cells might really be explained by the presence of a third neuron or external covariates . Second , although we take into account spatial covariations in rate , there is no systematic way of evaluating how much other covariates , such as theta oscillations or head direction , contribute to the correlations between cells . Given the fact that grid cells are known to covary with these , it is important to evaluate their influence when analyzing correlations between grid cells . While pairwise correlation analysis suffers from these problems , they can be addressed , to a large extent , using statistical models of the GLM type . This is what we will do in the rest of the paper . As a statistical model , we considered the simplest maximum entropy model to include both asymmetric couplings and time varying external input: the kinetic Ising model . The activity of the cells was binned in 10 ms bins , and a binary variable Si ( t ) was associated to each neuron in each bin , which would be equal to +1/-1 denoting the presence/absence of spikes emitted by neuron i within time bin t . Letting the state of each neuron at time t depend on the state of the population in the previous time step t − 1 and some covariates , independent of the state of the system , the maximum entropy distribution over the state Si ( t ) of neuron i at time t is [20] P ( S i ( t ) | { S ( t − 1 ) } ) = exp [ S i ( t ) H i ( t − 1 ) ] 2 cosh [ H i ( t − 1 ) ] , ( 1 ) H i ( t − 1 ) = h i ( t − 1 ) + ∑ j J i j S j ( t − 1 ) ( 2 ) where Jij would be identified as the functional coupling from neuron j to neuron i , and hi ( t ) as the time varying covariate which in statistical physics terminology is called an external field . As mentioned in the introduction , Eq . 1 defines a GLM , where in each time bin , mostly only one or zero spikes per bin are observed and the interaction kernel extends one time step in the past . With binary states and only one time step kernels , this model represents the simplest possible model capable of capturing functional connectivity from neural data , which is convenient given the finite time in which the neural recordings were taken . This model should not be confused with the maximum entropy equilibrium models ( equilibrium Ising model [21 , 22] ) , which assume symmetric couplings and are not related to the GLMs . Given Eq . 1 , we asked what values of the parameters hi ( t ) and Jij are the most likely to generate the observed data . Both exact and fast approximate algorithms for solving the inverse kinetic Ising model have been developed [23] similar to other GLM models [15 , 16 , 19] . The exact solution is found by maximizing the log-likelihood function L [ S , J , h ] = ∑ i t [ S i ( t + 1 ) H i ( t ) − log 2 cosh H i ( t ) ] ( 3 ) with respect to hi ( t ) and Jij . The term ‘exact’ is used here in the sense that if data is generated by a kinetic Ising model , this learning algorithm would recover the parameters exactly in the limit of infinite data . The log-likelihood is the logarithm of the probability of observing the data at hand given that it was generated from the model , and thus measures how well the model explains the statistics in the observed data . In our analysis we have used the natural logarithm . An important issue in dealing with a model of this type is choosing the external field . In the absence of couplings , the external field , hi ( t ) , can explain the variations in the firing rate as the rat navigates in space . Ideally , the external fields can be inferred by binning the environment into small spatial bins , assuming that the external field in each bin takes a constant value for each neuron . If the rat passes through each bin many times , the external field in each bin can be reliably estimated . However , during a recording period , and as described above , the requirement of passing through small spatial bins many times is rarely satisfied . Alternatively , the spatial input could arise as the sum of two-dimensional Gaussian basis functions with the basis set spanning the environment . By inferring the parameters of a linear combination of Gaussian basis functions ( see Material and Methods for details ) , an accurate representation of the spatial field can be found , even with a reduced amount of data , as shown in the following . Focusing on data set 1 , which had the most cells , we first inferred couplings , assuming that each neuron receives an external field which is constant across time and space , hi ( t ) = hi . Next , we studied how the inferred couplings were affected by increasing the spatial resolution of the external fields , hi ( t ) , to account for the spatial variation in firing rate by dividing the environment into spatial bins , considering the cases of bins of size 37 . 5 cm and then bins of size 7 . 5 cm , assigning one external field per box to each cell . We also considered external fields in the form of a sum of Gaussian basis functions . Fig . 2 shows the resulting couplings , plotted against couplings found in the model that assumed spatially and temporally constant external input , hi , for each neuron . As can be seen , increasing the resolution of the external fields made the couplings weaker but not inconsistent with the constant field case , even in the case of Gaussian fields , where the spatial rate maps were well captured by the model , as shown in Fig . 3 . In this case , there was a significant weakening of the couplings ( the estimated variance of the Gaussian field model couplings ( S Gauss 2 ) was significantly smaller than that of the constant field model ( S constant 2 ) , ( F-test for equal variances , P<0 . 001 ) ) . In each of the models , the total external fields were negative and often strong , as one would expect for data sets with low firing rates ( mean firing rate 2 . 4 Hz ) . Interestingly , no matter which of the various external fields we used , when neurons i and j both belong to one of the two smaller modules of data set 1 , or the one module of data set 2 , the inferred couplings , Jij , showed a consistent dependence on the spatial phase difference , with nearby phases showing positive Jij while those further away more negative values . This is shown in Fig . 4 for both data sets for the case of the Gaussian fields . The slopes and intercepts of linear regression lines were all significantly different from zero , both for the full data and the 20 sets of random halves ( t-test , P<0 . 02 ) for all figures except for Fig . 4E , where the slope and intercept of linear regression were not significantly different from 0 ( t-test , P>0 . 7 ) . We remind that with the Gaussian fields , the correlations between two cells due to overlapping fields are explained away . Since many cells in our data had some theta phase and head directional preferences , we also considered a model in which each cell was coupled to the head direction of the animal and the LFP theta oscillation through coupling constants that were inferred from the data; see Material and Methods . In general , there were only small differences between the couplings when theta and head direction were added . This can be seen in Fig . 5A , which shows the couplings in the model with Gaussian fields with and without theta included . In this case , we observed a small but selective change , depending on the phase preference of the neurons . The cells could be clustered into two groups according to their theta phase preference ( see Material and Methods ) : one with connections between cells of similar theta phase preference , and the other with connections between cells with opposite preference . Couplings between cells with similar theta phase preference were on average positive ( average ( μ ) significantly different from 0 ( t-test , P<0 . 001 ) ) , whereas couplings between cells of opposite theta preference were on average negative ( μ < 0 , P<0 . 001 ) . As shown in Fig . 5B , including the time-varying phase of theta as an external covariate resulted in shifting the coupling strength towards less positive values for pairs of cells that prefer the same phase of theta ( μno theta > μtheta , P<0 . 001 ) , whereas the opposite was true for couplings between cells that showed preference to opposite phase of theta ( μno theta < μtheta , P<0 . 001 ) . One would expect , based on the experimental indications of modules operating independently , that grid cells of the same module are more likely to participate in the same functional network than neurons from different modules . We found that the couplings within and between modules in data set 1 both had means close to zero ( within modules ( mean±std ) : −0 . 01±0 . 13 , between modules: −0 . 01±0 . 09 ) . However , the within module couplings had a greater variance ( S within 2 > S between 2 , P<0 . 001 ) ) , i . e . there was a higher proportion of couplings with high absolute values within modules than between , as can be seen in Fig . 6 . This result was found to be stable with respect to data limitations , as shown in the next section . In this section we consider a number of factors that could have influenced our estimations of the couplings , and show that our results were stable with respect to these factors . It is known that some grid cells show phase precession . This could be an additional source of correlation , so we tried to address how phase precession can influence the couplings . We first investigated whether or not any of the cells in our data phase precess , focusing on data set 1 . In general , quantifying phase precession in two-dimensions is a difficult task due to the changes in the animals movement direction within the field . To classify cells as phase precessing or not , we thus used a novel approach described in [24] , correlating the distance to the field peak projected onto the current running direction with the phase of theta at the time of spikes . Our analysis revealed that 13 of the 27 grid cells showed significant phase precession ( 5 of 8 in module 1 , 6 of 7 in module 2 , and 2 of 7 in module 3 ) . We then excluded the couplings between phase precessing cells from the analysis for the two smaller modules and found that this did not remove the trend reported in Fig . 4 between the spatial phase difference and the inferred couplings . As can be seen in Fig . 7A , there was still a significant negative relationship between coupling value and spatial phase distance for cell pairs in which at least one of the cells do not show significant phase precession ( both the slope ( β̂=−0 . 60 ) and intercept ( α̂=0 . 21 ) of the linear regression line are significantly different from 0 ( t-test , P<0 . 001 ) ) . It has been suggested that correlations and thus inferred couplings from multi-electrode recordings can be biased due to problems with spike sorting [25 , 26] . Since the main part of our conclusion is on the phase dependence of the correlations and functional connections and not their actual value , and since the phase of grid cells appears to be not anatomically ordered , it is unlikely that a phase dependent bias would be introduced to the correlations due to mistakenly assigning spikes to wrong cells . In addition to this , the cells in the two data sets analyzed here were recorded using hyperdrives that consist of 14 independently movable tetrodes [7] . It has been suggested that a tetrode is unlikely to record signals from cells farther than 65μm away [27] . As the distance between tetrodes on the hyperdrive is approximately 250±50 μm , it was very unlikely that the same cell was recorded on two tetrodes , and in that way confound our results across tetrodes . We therefore examined the couplings versus spatial phase for cell pairs from different tetrodes , and found that this led to a qualitatively similar result , as shown in Fig . 7B ( both the slope ( β̂=−0 . 59 ) and intercept ( α̂=0 . 20 ) of the linear regression were significantly different from 0 ( t-test , P<0 . 001 ) ) . In order to investigate the stability of the inferred couplings and the various covariates to data limitations we inferred the parameters of the models using only half of the data , and compared them with the ones from the other half . For this , we defined the spike data as being made up of consecutive time pairs , ( S ( t ) , S ( t+1 ) ) and created partitions by randomly selecting 50% of the pairs . In this way , we generated 20 random sets , and for each set inferred the couplings using constant fields without taking theta and head direction into account , and Gaussian fields with theta and head directional input included ( the full model ) . In general , the inferred couplings from these random halves were correlated with each other . As shown in Fig . 7C and D , the within module couplings were more stable than the between-module ones , with an average Pearson correlation coefficient of 0 . 88 versus 0 . 73 for the constant field model , and 0 . 70 versus 0 . 51 for the full model . We noticed that the self-couplings are the ones that are most stable from one half to the other , showing a Pearson correlation coefficient of 0 . 94 between the couplings inferred from the two halves for the full model . We also found that the mean absolute values of the within and between module couplings maintained their relationship , with stronger couplings between cells within module than those between modules , for all 20 random partitions of the data ( S within 2 > S between 2 , P<0 . 005 for all 20 random partitions , in both constant field model and Gaussian field model ) . The analyses reported here were produced using the data from two recordings of grid cells , the biggest of them consisting of 27 grid cells . This was the biggest data set we had access to , but still represents only a small fraction of the true local cell population . One might wonder how much the connections between these cells would be influenced if we had access to recordings from more cells . As described in Material and Methods , data set 1 included neurons which were not classified as grid cells . We found that using this entire data set ( 65 cells ) did not affect the couplings between grid cells ( see Fig . 7E ) . In order to evaluate the strength of the statistical effect of the couplings and the external covariates on explaining the correlations in spike trains , we calculated the log-likelihood of half of the data using parameters inferred from the other half for various models for both data sets . The results are shown in Fig . 8A-D . To correct for the number of parameters , the total log-likelihood was penalized according to the Akaike correction , that is by subtracting the number of inferred parameters ( covariates and couplings ) used in each model ( see Material and Methods ) [28] . The negative log-likelihoods of the models without the couplings are also shown . In a likelihood ratio test , all covariates gave a significant increase ( P < 0 . 001 ) compared to the constant field model . This was also the case where we included the couplings in each of the models compared to the same model without couplings . In general , adding head direction as a covariate had little effect on the likelihood . The effect was even weaker when including speed as a covariate , or using running direction instead of head direction ( see methods ) , with the penalty from the Akaike correction larger than the increase in likelihood from the inclusion of the parameters . For the case of constant fields , adding couplings and then theta had the most significant effect . It is interesting to note that , when comparing the constant field model to the model with spatial fields , the impact on the likelihood from including the couplings is reduced , as would be expected by explaining away the spatial component of the correlations . Adding theta resulted in a consistent increase in the log-likelihood yielding 0 . 0025 for the model with constant fields and 0 . 0026 for spatial .
What is known about the connectivity in the grid cell network is primarily based on anatomical in vitro studies . Recent studies show that stellate cells in layer II are connected to each other primarily through inhibitory interactions [14 , 29] , and that the inhibitory drive varies dorsoventrally as the size of the grid spacing changes [30] . As opposed to the connections between layer II stellate cells , within-layer recurrent excitation has been found between the main type of principal cells , namely pyramidal cells , in both layer III and V [31] . Although the picture drawn by these studies emphasizes the role of recurrent interactions in developing the properties of grid cells , it does not show how interactions between grid cells quantitatively depend on properties such as theta rhythmicity and spatial phase separation , properties that play a major role in computational models of grid cells . A previous work on in vivo recordings that studied phase dependence of the interactions between cells in MEC focused on pairwise correlation analysis by using recordings from one dimensional tracks [18] , showing that cells with nearby phases have stronger correlations than those far apart in phase . Another recent in vivo study used strongly peaked cross-correlations as a signal for the presence of connections and has concluded that grid cells with a wide range of phases project to a given inhibitory neuron [32] . To analyse the multi-neuronal recordings in grid cells we took a different approach from previous studies: that of statistical inference . We used a kinetic Ising model and studied how functional connections depend on phase difference between grid cells , their level of theta modulation , speed modulation and head directionality , and the statistical role that these connections play in shaping multi-neuronal activity . The kinetic Ising model that we used here for the inference is a model with minimal assumptions: ( 1 ) it is the maximum entropy distribution over the spikes of neurons at time t , given the spikes at time t−1 [20] , and ( 2 ) it is pairwise ( meaning it only takes into account the first-order non-trivial interactions ) . Being a generalized linear model , it is closely related to other GLMs used for analyzing population recordings from other parts of the brain [15–17] , and it also employs the maximum entropy approach used by many in analyzing neural [21 , 22] or other biological data [33] . Our analysis showed that the correlations and the functional connections between grid cells demonstrate a spatial phase dependence , even when spatial variations in rate ( as well as other possible sources of correlations , such as theta oscillations and head direction ) are taken into account . Both correlations and functional connections were positive for small phase differences . Functional connections became negative , while the correlations approached zero , for larger spatial distances for cells in the one module in data set 2 , and in the two smaller modules in data set 1 . This connectivity provides support for a role played by attractor dynamics as suggested by several modelling efforts [11–14] . The trend in the phase dependence was , however , less clear in the third module in data set 1: the common inhibitory portion was represented , but we did not find any functional excitation between cells close in phase , possibly because of the lack of recorded cells with similar phase in this module . We also found that the absolute value of the couplings was bigger for pairs of cells that belonged to the same module than those belonging to different modules . This supports the idea that neurons in the same module form a more coherent population of neurons , bound together in a stronger manner than those in different modules . In attractor models of grid cells , the phase dependent connectivity pattern allows the network to maintain a continuum of stable states such that , if the neurons of a single module could be aligned according to their phases , the activity on that neural sheet would itself show a regular pattern of activity . This local and relatively rigid relationship between within-module grid cells has been surprisingly well supported . First identified in [1] , grid cells were found to locally share both orientation and spacing that were later observed to remap and deform coherently [7 , 10 , 34] . It has also been shown that the characteristics of the grid pattern of one cell were more stable relative to other grid cells than with respect to local features of the environment [10] . This was even more pronounced in novel environments where the individual fields were still changing significantly relative to the environment while remaining relatively stable between cells [10] , further suggesting that the coding of the grid cells is more coherent within the grid cell population than it is with the actual space it is encoding . Even more convincing , a recent study looking at a large population of cells taken from single animals in the same environment showed that the cells clustered into a finite number of modules [7] suggesting there exists not only the large number of cells necessary for an attractor map but that there might be a finite number of these networks working together to better provide a metric of space . Our work complements these studies in that we show that there exists the functional connectivity of the type necessary to establish the patterned network activity that has been proposed to explain the above experimental observations . As opposed to the attractor model [11–13] , other grid cell model frameworks , the oscillatory interference [35] and the adaption model [36] , were originally conceived as single cell models that suggest that the periodic firing comes from a combination of convergent input and cellular mechanisms within an individual neuron . As such , the role they have prescribed for the lateral connectivity has been mainly to align the grid patterns of the cells , without requiring any phase dependence in the couplings per se . However , it has recently been noted [9 , 37] that in the adaptation model , interactions between grid cells can also be learned , resulting in a developmental model for the phase dependent connectivity which could later sustain a continuous attractor dynamics . In addition to aligning the grids , this connectivity will allow the adaptation model to code for novel environments much more rapidly while maintaining the stabilizing benefit of having convergent spatial input . In our statistical inference , we considered various external covariates that comprise what is known about the single cell coding of these cells , including spatial , speed , theta oscillations , head direction and running direction inputs . Adding these additional covariates to the models with constant field or Gaussian fields had little effect on the connectivity , but there was a significant weakening of the couplings when we compared the couplings of the Gaussian model to those of the model with constant fields . This is not surprising , as a component of the correlations in the model with constant fields was likely due to overlapping fields which was better explained by the spatial component of the Gaussian model . One benefit of using a statistical model is the quantification of the relative contribution of the individual covariates to the overall likelihood of the data under the model , with the spatial component having the strongest impact followed by functional connectivity and theta preference . Speed , head direction and running direction , as covariates , had a small impact in all cases that we considered . In all the statistical models , ranging from constant external field to Gaussian with and without theta and head direction , we found that the model without couplings was worse at explaining the statistics of the data than the same model with couplings , even when the Akaike corrections were taken into account . Further support for the significance of the couplings come from the stability of the connectivity when inferred from separate halves of the data . Since the self-couplings appeared to be the most stable when one random partition of the data was compared to the other , we wondered how the rest of the couplings would react if we did not include the self-couplings . With the refractory period in mind , positive self-couplings might seem counter-intuitive . However , the refractory period lasts for only a few milliseconds , and we use 10 ms time bins . In addition , grid cells are primarily active only when the animal is in the cell’s spatial fields , and silent otherwise , i . e . the state of a grid cell in a time bin is likely to be equal to the state in the previous time bin , which a statistical model could interpret as a positive self-coupling . Removing self-couplings , however , had little effect on the couplings between cells ( Pearson correlation coefficient > 0 . 98 for the constant model and the full model , for both data sets ) . Stellate cells of MEC layer II , the main grid cell candidates , are known to functionally inhibit each other . In our analysis , the inferred connections were both inhibitory and excitatory . There are a few points to note regarding this apparent contradiction . First , considering the recording locations of the tetrodes in data set 1 ( see Supplementary figure 4 ( rat 14147 ) in [7] ) , and that a number of cells in this data set show head direction preferences , a property rarely observed in the layer II population [3] , many of these cells are most likely recorded from deeper layers where , as mentioned , both intra- and interlayer excitatory connections between principal cells have been found . For data set 2 , on the other hand , it seems probable that a bigger fraction of the cells is from layer II ( see Supplementary figure 14 ( rat 13855 ) in [7] ) . It is , however , not possible to confirm the exact location or principal cell type for the cells analyzed here . Second , the relationship between the inferred functional connections and the underlying anatomical connectivity is a nontrivial one which may involve other non-recorded neurons . It is also possible that the correlations driving the functional connectivity come from a common input that was not accounted for here . This input , however , should be non-spatial , non-directional and independent of theta phase , but still depend on the spatial phase difference between pairs of neurons and whether or not they belong to the same module . It would be interesting to see what such a signal could look like . The existence of such an input would , of course , leave the question open as to how the local network is connected , while opening a new possibility that the grid cell modules play a role in encoding currently unidentified features that are neither spatial or directional . Since it is possible in computer simulations to identify the presence or absence of a synapse based on the inference of functional connections [38 , 39] , it would be very interesting to see how the inferred functional couplings and correlations look like for a data set exclusively from layer II cells for which the actual functional connectivity between stellate cells is known . In addition , considering the fact that modules span layers [7] , our results also make a case for taking a closer look at the between layer connectivity and how the different cell types and connectivity patterns might work together to develop the grid cell code . With Gaussian fields , the model with only theta has a slightly higher likelihood than the one with only couplings , although the couplings still exhibit the phase dependence shown in Fig . 4 . The relative improvement gained by pairwise connections in explaining the data is known to scale with the size of the recorded population [21 , 40 , 41] , while other sources of higher order correlations will also scale up . It would therefore be interesting to see how the relative contribution of the various factors , in particular that of theta oscillations , will scale compared to that of the pairwise couplings . Future large-scale recordings of grid cells should allow us to perform such analyses .
Two recordings of the activity of cells in the MEC area of two Long Evans male rats ( from [7] ) were analyzed in this paper . One recording , referred to as data set 1 , consisted of a total of 65 cells ( rat 14147 in [7] ) , where 27 were classified as grid cells ( mean firing rate: 2 . 4 Hz ) . These 27 cells distributed over 7 tetrodes , and 22 of them could be assigned to one of three modules ( see [7] for methods ) . The number of cells in each module , along with mean spacing and orientation is given in Table 1 . The other recording , data set 2 , consisted of 8 grid cells ( mean firing rate: 2 . 8 Hz ) distributed over 3 tetrodes ( rat 13855 in [7] ) . All 8 cells belonged to the same module . Mean spacing and orientation for this module is listed in Table 1 . The movement of the rats is shown in Fig . 9 . The spikes were binned into 10 ms time bins , but using both 20 ms and 5 ms time bins led to similar results . Using the binned data , a spike matrix of −1’s and 1’s was constructed , where a ‘−1’ indicated that the cell did not fire in time bin t , and a ‘1’ indicated that the cell emitted one or more spikes in time bin t . More than one spike rarely happened ( both data sets: average over cells = 0 . 1 ( ±0 . 1 ) % of the time bins ) . Noise correlations were defined as C i j = 〈 ρ ( r ¯ i a , r ¯ j a ) 〉 a where r ¯ i a is a 1×k vector consisting of the average firing rate of neuron i in each of the k trajectories through spatial bin a , and ρ ( ⋅ , ⋅ ) is the Pearson correlation coefficient ( PCC ) , defined as: ρ ( r ¯ i a , r ¯ j a ) = E [ ( r ¯ i a − 〈 r ¯ i a 〉 k ) ( r ¯ j a − 〈 r ¯ j a 〉 k ) ] σ [ r i a ] × σ [ r j a ] with both the expectation ( E ) and the standard deviations ( σ ) over the k trajectories . Random partitions: Each spatial bin has a given number of visits . To split the data into two random partitions , for all visited bins , a randomly chosen half of the visits to each bin was assigned to one partition , the other half to the other partition . The cells could be divided in two clusters based on preferred phase of theta . The theta phase preference was defined as the peak in a circular kernel smoothed density estimate of the distribution of theta value at spike time . The number of clusters were defined as the number of local peaks in a kernel smoothed density estimate of the distribution of theta phase preference peaks for all cells . A circular k-means clustering algorithm were performed to assign cells to clusters . The clusters are shown in Fig . 10 . We used the kinetic Ising model to infer the functional network connectivity , i . e . we assumed that the observed spike train comes from the probability distribution in Eq . 1 . We constructed different versions of the model by varying the form of the external field in several ways as described in the introduction and in more details below . To allow the external field of the kinetic Ising model to account for the spatial variations in the firing of the grid cells , we started , for data set 1 , by dividing the environment globally into K square boxes . We defined three models with increasing spatial resolution , with K = 4 × 4 ( 37 . 5 cm boxes ) in the first model , and K = 20 × 20 ( 7 . 5 cm boxes ) in the second . For each K , we defined external fields αik for each cell i and box k . The field resulting from this spatial discretization is then h i S ( t ) = Σ k α i k I k ( t ) , where Ik ( t ) is a function indicating the presence ( 1 ) or absence ( 0 ) of the animal in box k at time t . We further increased the resolution of the spatial fields using Gaussian basis functions centered on an evenly spaced M × M square lattice covering the recording environment . The spatial field for cell i at time t is then h i S ( t ) = ∑ j k α i j k exp [ − ( ( x ( t ) − x j k ) 2 + ( y ( t ) − y j k ) 2 ) / r 2 ] + h i ( 4 ) where ( xjk , yjk ) and r are the vertices of the regular lattice and the widths of the basis functions , respectively . To determine the optimal values of M and r ( M = 15 and r = 8 . 5 cm ) , we maximized the likelihood for a range of values of M and r and chose the values of the parameters that gave the highest Akaike-adjusted likelihood value . To include the external theta phase preference , we computed the fast-Fourier transform of the local field potential ( LFP ) and set the theta rhythm to the maximum component between 4–12 Hz . From this , we constructed a theta input vector , where each element was the angular average ∈ ( −π , π] of the theta phase in that time bin . The partial field for cell i at time t due to local field potential theta preference is then h i LFP ( t ) = ∑ k α i k exp [ − d ( Θ ( t ) , Θ k ) 2 / ( π / 6 ) 2 ] + h i ( 5 ) where d ( Θ ( t ) , Θk ) is the minimum angular distance between Θ ( t ) , the theta phase in time bin t , and Θk , the k’th component of a set of 10 equally spaced angular phases . The number of angles and width of Gaussian ( π/6 ) was selected by maximizing the Akaike-adjusted likelihood of the model in the same way parameter values for M and r in the model with spatial fields were chosen , as described above . The head and running direction components was also accounted for using sums of Gaussian basis functions hiHD ( t ) =∑kαikexp[−d ( φ ( t ) , φk ) 2/ ( π/6 ) 2]+hi ( 6 ) where φ ( t ) is the head direction ∈ ( −π , π] at time t , calculated from the projection of two LEDs onto the horizontal plane , and φk is the angular position of the kth basis function . The number of basis functions ( 10 ) and width of Gaussian ( π/6 ) were selected by maximizing the Akaike-adjusted likelihood of the model , the same way it was done for parameter choice in the spatial and theta model . Speed was also incorporated into the model with a simple time-varying field , αi s ( t ) , where s ( t ) is the average speed in the 100ms window around each time bin . In all of the models , the parameters , Jij , hi and α’s , were found by maximizing the likelihood function given in ( 3 ) for the data under the different models by gradient ascent . When comparing the models , we first Akaike-corrected the log-likelihood . The Akaike information criterion ( AIC ) is a measure to compensate for overfitting by models with more parameters , where the preferred model is that with the minimum AIC value , defined as A I C = − 2 ln ( L [ D | θ M L ] ) + 2 k ( 7 ) where D is the observed data , and L[D∣θML] is the likelihood at the maximum likelihood ( ML ) estimates of the parameters θ ( θML ) , and k is the number of parameters [28] . Equivalent to the method described above , we corrected the total log-likelihood as l n ( L A kaike ) = − A I C 2 .
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The way mammals navigate in space is hypothesized to depend on neural structures in the temporal lobe including the hippocampus and medial entorhinal cortex ( MEC ) . In particular , grid cells , neurons whose firing is mostly restricted to regions of space that form a hexagonal pattern , are believed to be an important part of this circuitry . Despite several years of work , not much is known about the correlated activity of neurons in the MEC and how grid cells are functionally coupled to each other . Here , we have taken a statistical approach to these questions and studied pairwise correlations and functional connections between simultaneously recorded grid cells . Through careful statistical analysis , we demonstrate that grid cells with nearby firing vertices tend to have positive effects on eliciting responses in each other , while those further apart tend to have inhibitory or no effects . Cells that respond similarly to manipulations of the environment are considered to belong to the same module . Cells belonging to a module have stronger interactions with each other than those in different modules . These results are consistent with and shed light on the population-based mechanisms suggested by models for the generation of grid cell firing .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Material",
"and",
"Methods"
] |
[] |
2015
|
Correlations and Functional Connections in a Population of Grid Cells
|
Leishmaniasis is the world’s second deadliest parasitic disease after malaria , and current treatment of the different forms of this disease is far from satisfactory . Alkylphospholipid analogs ( APLs ) are a family of anticancer drugs that show antileishmanial activity , including the first oral drug ( miltefosine ) for leishmaniasis and drugs in preclinical/clinical oncology trials , but their precise mechanism of action remains to be elucidated . Here we show that the tumor cell apoptosis-inducer edelfosine was the most effective APL , as compared to miltefosine , perifosine and erucylphosphocholine , in killing Leishmania spp . promastigotes and amastigotes as well as tumor cells , as assessed by DNA breakdown determined by flow cytometry . In studies using animal models , we found that orally-administered edelfosine showed a potent in vivo antileishmanial activity and diminished macrophage pro-inflammatory responses . Edelfosine was also able to kill Leishmania axenic amastigotes . Edelfosine was taken up by host macrophages and killed intracellular Leishmania amastigotes in infected macrophages . Edelfosine accumulated in tumor cell mitochondria and Leishmania kinetoplast-mitochondrion , and led to mitochondrial transmembrane potential disruption , and to the successive breakdown of parasite mitochondrial and nuclear DNA . Ectopic expression of Bcl-XL inhibited edelfosine-induced cell death in both Leishmania parasites and tumor cells . We found that the cytotoxic activity of edelfosine against Leishmania parasites and tumor cells was associated with a dramatic recruitment of FOF1-ATP synthase into lipid rafts following edelfosine treatment in both parasites and cancer cells . Raft disruption and specific FOF1-ATP synthase inhibition hindered edelfosine-induced cell death in both Leishmania parasites and tumor cells . Genetic deletion of FOF1-ATP synthase led to edelfosine drug resistance in Saccharomyces cerevisiae yeast . The present study shows that the antileishmanial and anticancer actions of edelfosine share some common signaling processes , with mitochondria and raft-located FOF1-ATP synthase being critical in the killing process , thus identifying novel druggable targets for the treatment of leishmaniasis .
Leishmaniasis , caused by several species of the protozoan Leishmania parasite , is one of the world’s most neglected diseases in terms of drug research and development , and for which current therapy is not satisfactory [1] . At present , about 350 million people in 98 countries worldwide are at risk of contracting leishmaniasis , and some 0 . 9–1 . 6 million new cases occur yearly , with about 0 . 7–1 . 2 million cases of self-healing cutaneous leishmaniasis , which can result in disfiguring skin lesions , and 0 . 2–0 . 4 million cases per year of life-threatening visceral leishmaniasis , which is a fatal disease if left untreated [1–3] . Leishmaniasis is the world’s second-deadliest parasitic disease after malaria , with a tentative estimate of 20 , 000 to 40 , 000 leishmaniasis deaths occurring annually [3] , and has been classed as a category 1 disease ( “emerging and uncontrolled” ) by the World Health Organization ( WHO ) . At present there are very few available antileishmanial drugs , being in general toxic , and the first line drugs are becoming ineffective due to emerging drug resistance [1 , 2] . Thus , the development of novel therapeutic drugs is urgently needed . Leishmaniasis is transmitted by the bite of a female sandfly vector ( Lutzomyia in the Americas and Phlebotomus elsewhere ) infected with Leishmania parasites . Infection of humans and other animal hosts is initiated by flagellated promastigotes that develop within the digestive tract of the sandfly vector and are injected during a sandfly blood meal . Promastigotes are internalized into a number of phagocytic host cells , including neutrophils , dendritic cells , and macrophages , but proliferate only within the macrophage as aflagellate amastigotes [4 , 5] . The so-called alkylphospholipid analogs ( APLs ) constitute a class of structurally-related antitumor compounds with multiple therapeutic indications , and include a number of clinically relevant and/or promising drugs , such as miltefosine ( hexadecylphosphocholine ) , edelfosine ( 1-O-octadecyl-2-O-methyl-rac-glycero-3-phosphocholine ) , perifosine ( octadecyl ( 1 , 1-dimethyl-piperidinio-4-yl ) -phosphate ) and erucylphosphocholine ( ( 13Z ) -docos-13-en-1-yl 2- ( trimethylammonio ) ethyl phosphate ) ( ErPC ) ( Fig 1 ) [6–8] . So far , miltefosine is the only APL that has entered the clinic , registered as Impavido , the first orally-effective treatment for visceral leishmaniasis , and as Miltex , a topical chemotherapy and palliative treatment in cutaneous metastases from breast cancer [7 , 9 , 10] . APLs induce an apoptosis-like cell death in Leishmania parasites [11 , 12] , but their antiparasitic mechanism of action remains unknown , although lipid metabolism [13] and dramatic increases in membrane dynamics [14] have been suggested to play a role . The ether lipid edelfosine induces apoptosis in tumor cells , involving cholesterol-rich lipid rafts and mitochondria [15–20] . Lipid rafts are membrane microdomains highly enriched in cholesterol and sphingolipids , and recent findings in mammalian cells suggest that lipid rafts act as death-promoting scaffolds , where Fas/CD95 and downstream signaling molecules are recruited to tigger apoptosis [21–23] . Raft domains have also been described in Leishmania spp . , although their biochemical and functional characterization remains incomplete [24] . Here , we analyzed whether our knowledge on processes involved in the anticancer activity of APLs could provide some insight into their antileishmanial mechanism of action . In addition , we tested the effect of APLs on intact and Leishmania-infected macrophages as the main host cells for Leishmania parasites , as well as the in vivo antileishmanial activity of edelfosine in animal models . In this study , we report the existence of common mechanisms that underlie the antileishmanial and antitumor activities of the APL edelfosine , involving mitochondria , lipid rafts and FOF1-ATP synthase ( also named as FOF1-ATPase ) , which might open up new avenues for the development of novel targeted therapies .
Animal procedures were approved by the institutional research commission of the University of Salamanca , and were approved by the Ethics Committee of the University of Salamanca ( protocol approval number 48531 ) . Animal procedures complied with the Spanish ( Real Decreto RD1201/05 ) and the European Union ( European Directive 2010/63/EU ) guidelines on animal experimentation for the protection and humane use of laboratory animals , and were conducted at the accredited Animal Experimentation Facility ( Servicio de Experimentación Animal ) of the University of Salamanca ( Register number: PAE/SA/001 ) . Edelfosine was from R . Berchtold ( Biochemisches Labor , Bern , Switzerland ) . Miltefosine was from Calbiochem ( Cambridge , MA ) . Perifosine and erucylphosphocholine were from Zentaris ( Frankfurt , Germany ) . Rotenone , malonate , antimycin A , azide , oligomycin and CCCP were from Sigma ( St . Louis , MO ) . The Leishmania strains used in this study were: L . donovani ( MHOM/IN/80/DD8 ) , L . major LV39 ( MRHO/SU/59/P ) , L . panamensis ( MHOM/CO/87/UA140 ) , L . infantum ( MCAN/ES/96/BCN/150 , MON-1 ) , kindly provided by Iván D . Vélez ( Programa de Estudio y Control de Enfermedades Tropicales–PECET- , Medellin , Colombia ) , Ingrid Müller ( Imperial College London , London , UK ) and Antonio Jiménez-Ruiz ( Universidad de Alcalá , Alcalá de Henares , Spain ) . To visualize Leishmania parasites inside macrophages we used GFP-L . panamensis promastigotes [25 , 26] . Leishmania promastigotes were grown at 26°C in RPMI-1640 culture medium ( Invitrogen , Carlsbad , CA ) , supplemented with 10% fetal bovine serum ( FBS ) , 2 mM L-glutamine , 100 U/ml penicillin , and 100 μg/ml streptomycin . Promastigotes were treated at 26°C with the indicated compounds during their logarithmic growth phase ( 1 . 5 x 106 parasites/ml ) . Late stationary promastigotes were obtained after incubation of the parasites for more than 6 days with a starting inoculum of 1 x 106 parasites/ml . Leishmania axenic amastigotes were obtained as previously described [27] . Human acute T-cell leukemia Jurkat ( American Type Culture Collection , Manassas , VA ) , myeloid leukemia HL-60 ( American Type Culture Collection ) , multiple myeloma MM144 ( provided by A . Pandiella , CIC , IBMCC , Salamanca , Spain ) , and cervical carcinoma HeLa ( American Type Culture Collection ) cell lines , as well as the mouse macrophage cell line J774 ( American Type Culture Collection ) were grown in RPMI-1640 culture medium supplemented with 10% FBS , 2 mM L-glutamine , 100 U/ml penicillin , and 100 μg/ml streptomycin at 37°C in humidified 95% air and 5% CO2 . Saccharomyces cerevisiae yeast ( BY4741 strain: MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0 ) was grown on standard synthetic complete medium ( SDC ) , which consisted of synthetic minimal medium ( SD; 0 . 17% yeast nitrogen base without amino acids , 2% glucose and supplements according to the requirements of the strains ) with 0 . 079% complete supplement mixture ( ForMedium , Norwich , UK ) . Yeast cultures were incubated at 30°C , and growth of cells untreated or treated with edelfosine was monitored by optical density at a wavelength of 595 nm ( OD595 ) . Cells were incubated for the indicated times and sample aliquots were taken to measure absorbance at 595 nm . Edelfosine was used at the concentrations indicated in the corresponding figure in liquid medium . The atp7Δ mutant was obtained from the EUROSCARF haploid deletion library in the BY4741 background [28] . This atp7Δ mutant was complemented with the corresponding wild-type gene expressed from a centromeric plasmid ( pRS416 ) , and yeast growth was determined as above . L . infantum transfected with pX63-Neo or pX63-bcl-xL were kindly provided by Antonio Jiménez-Ruiz ( Universidad de Alcalá , Alcalá de Henares , Spain ) and grown in medium containing 100 μg/ml G418 ( Sigma ) [29] . HeLa cells ( 1–2 x 105 ) were transfected with 2 μg of pSFFV-bcl-xL or pSFFV-Neo expression vector [15] , using Lipofectin reagent ( Life Technologies , Carlsbad , CA ) . Transfected clones were selected by growth in the presence of 500 μg/ml G418 , and monitored by Western blotting using the 2H12 anti-29 kDa Bcl-XL monoclonal antibody ( BD Biosciences PharMingen , San Diego , CA ) . Murine bone marrow cells were obtained by flushing out the femurs of mice from ( C57BL/6 x BALB/c ) F1 ( CBF1 ) mice and cultured as previously described [30] in hydrophobic Teflon bags ( Biofolie 25 , Heraeus , Hanau , Germany ) with DMEM culture medium containing 10% FBS , 5% horse serum , 2 mM L-glutamine , 60 μM 2-mercaptoethanol , 1 mM sodium pyruvate , 1% non-essential amino acids , 100 U/ml penicillin , 100 μg/ml streptomycin , and the supernatant of L929 fibroblasts at a final concentration of 15% ( v/v ) as a source of colony-stimulating factors which drive cells towards a >95% pure BMM [31] . CBF1 mice were treated orally with edelfosine ( 5 mg/kg body weight ) , daily for 13 days in 100 μl PBS , and then BMM were prepared as above . No weight loss or other visible side-effects were observed in mice treated with edelfosine . Cell viability at the indicated times was measured by the WST-1 reduction to formazan method ( Roche Diagnostics , Basel , Switzerland ) . 105 cells were incubated for 2 h at 37°C with 10 μl WST-1 solution in 0 . 2 ml DMEM culture medium supplemented with 10% FBS in a flat-bottom microtitre plate , and then absorbance was determined at 440 nm . The production of superoxide anion ( 2 x 105 cells in 0 . 2 ml Hepes-DMEM without pH indicator and containing 125 μM lucigenin , 37°C ) was initiated by addition of 50 μg zymosan , and measured as lucigenin-dependent chemiluminescence using a Microlumat LB96P ( Berthold , Wildbad , Germany ) [32] . NO end product nitrite was measured using the Griess reagent as previously described [32] . Culture supernatant was mixed with 100 μl of 1% sulphanilamide , 0 . 1% N- ( 1-naphthyl ) ethylenediamine dihydrochloride and 2 . 5% H3PO4 . Absorbance was measured at 540 nm in a microplate reader ( Molecular Devices , Ismaning , Germany ) . LPS from S . abortus equi was kindly provided by Chris Galanos ( Max-Planck-Institut , Freiburg , Germany ) . IFN-γ was determined by a commercially available ( Pharmingen ) sandwich ELISA test according to the manufacturer’s protocol . The ATP content was determined by the luciferin–luciferase method [33] . The assay is based on the requirement of luciferase for ATP in producing light ( emission maximum 560 nm at pH 7 . 8 ) . Briefly , cells ( 2 x 106 ) were harvested after treatment , resuspended in 1X PBS , and assayed for ATP using the Molecular Probes ATP determination kit ( Thermo Fisher Scientific , Waltham , MA ) . The amount of ATP in each experimental sample was calculated from a standard curve prepared with ATP and expressed as percentage of the amount of ATP found in untreated control cells . 1 . 5 x 106 Leishmania spp . promastigotes or axenic amastigotes , and 106 Jurkat cells or other human cells were incubated in the absence or presence of the indicated concentrations of APLs for different incubation times , and then analyzed for DNA breakdown by flow cytometry , using a fluorescence-activated cell sorting ( FACS ) Calibur flow cytometer ( Becton Dickinson , San Jose , CA ) , as previously described [16] . Quantitation of apoptotic-like cells was monitored following cell cycle analysis as the percentage of cells in the sub-G0/G1 region , representing hypodiploids or apoptotic-like cells [16] . 2 x 106 Leishmania parasites and 106 Jurkat cells were pelleted by centrifugation , washed with PBS , incubated in 1 ml PBS containing 20 nM 3 , 3′-dihexyloxacarbocyanine-iodide ( DiOC6 ( 3 ) , green fluorescence; Molecular Probes , Leiden , The Netherlands ) and 2 μM dihydroethidine ( HE , red fluorescence after oxidation; Sigma ) at room temperature and darkness for 20 min , and then analyzed on a Becton Dickinson FACSCalibur flow cytometer as previously described [16] . Macrophages , cultured in RPMI 1640 culture medium containing 10% FBS , were incubated for 4 h with stationary-phase L . panamensis promastigotes at a 10:1 parasite-to-macrophage ratio . Then , cell monolayers were extensively washed and incubated in complete culture medium with or without edelfosine for 24 h . The intracellular parasite load was calculated by limiting dilution assay as previously reported [34] . Alternatively , macrophage monolayers infected with green fluorescent protein ( GFP ) -expressing p6 . 5-egfp-L . panamensis parasites were cultured in glass coverslips placed into culture vessels ( Corning , Lowell , MA ) . After 24 h , coverslips were washed , and the rate of intracellular amastigotes and infected macrophages was visualized using a fluorescence microscope . Results are shown as the percentage of infected macrophages and as the parasite/macrophage ratio after counting 100 macrophages . Four-week-old male Syrian golden hamsters ( Mesocricetus auratus ) ( about 120 g ) were obtained from Charles River Laboratories ( Lyon , France ) and maintained in a pathogen-free facility . Animals were handled according to institutional guidelines , complying with the Spanish legislation , in an animal room with 12-h light/dark cycle at a temperature of 22°C , and received a standard diet and water ad libitum . Hamsters were inoculated intradermally in the nose with 1 x 106 stationary-phase promastigotes in a volume of 50 μl PBS and treated with a daily oral administration of edelfosine ( 20 mg/kg in water ) , or an equal volume of vehicle ( water ) as previously described [26] . Nose swelling was evaluated through weekly caliper measurements , and compared with the nose size before inoculation and treatment . Evolution index of the lesion was calculated as the size ( mm ) of the lesion during treatment/size of the lesion before treatment . No loss in animal body weight and no sign of morbidity were detected during the 28-day drug treatment , and animals were killed , following institutional guidelines , 24 h after the last drug administration . Parasite burden in the infected tissues was calculated by limiting dilution assay as previously described [26] . Apoptosis-like cell death was also analyzed in situ by the TUNEL technique using the Fluorescein Apoptosis Detection System ( Promega , Madison , WI ) , according to the manufacturer’s instructions . Parasites were fixed with 4% formaldehyde for 20 min on microscope slides , permeabilized with 0 . 2% Triton X-100 , stained for fragmented DNA using the above kit , and then propidium iodide was added for 15 min to stain both apoptotic-like and intact cells as previously described [17 , 35 , 36] . Propidium iodide stained all cells in red , whereas fluoresecin-12-dUTP was incorporated at the 3’-OH ends of fragmented DNA , resulting in localized green fluorescence within the nucleus of apoptotic-like cells . Samples were analyzed with a Zeiss LSM 510 laser scan confocal microscope ( Carl Zeiss AG , Jena , Germany ) . L . panamensis and HeLa cells were treated for 1 h with 10 μM fluorescent edelfosine analog all- ( E ) -1-O- ( 15’-phenylpentadeca-8’ , 10’ , 12’ , 14’-tetraenyl ) -2-O-methyl-rac-glycero-3-phosphocholine ( PTE-ET ) ( Fig 1 ) , kindly provided by F . Amat-Guerri and A . U . Acuña ( Consejo Superior de Investigaciones Cientificas , Madrid , Spain ) as described [17 , 36 , 37] , and then incubated with 100 nM cell-permeant MitoTracker probe ( Molecular Probes ) for 20 min to label mitochondria . Colocalization was analyzed by excitation of the corresponding fluorochromes in the same section of samples , using a fluorescence microscope ( Axioplan 2; Carl Zeiss MicroImaging , Inc . , Oberkochen , Germany ) and a digital camera ( ORCA-ER-1394; Hamamatsu , Hamamatsu City , Japan ) . Parasites ( 2 x 106/ml ) were incubated in serum-free medium with 2 . 5 mg/ml methyl-β-cyclodextrin ( MCD ) for 40 min at 26°C , and then washed 3 times with PBS , and resuspended in complete culture before edelfosine addition . For cholesterol depletion in Jurkat cells , 2 . 5 x 105 cells/ml were incubated with 2 . 5 mg/ml MCD for 30 min at 37°C in serum-free medium , and then washed 3 times with PBS , and resuspended in complete culture before edelfosine addition . Drug uptake was measured as previously described [15 , 36] , after incubating 2 x 106 parasites or 106 Jurkat cells with 10 nmol [3H]edelfosine ( 10 μM ) ( Amersham Buchler , Braunschweig , Germany ) for 1 h in RPMI-1640 , 10% FBS , and subsequent washing ( six times ) with PBS + 2% BSA . [3H]edelfosine ( specific activity , 42 Ci/mmol ) was synthesized by tritiation of the 9-octadecenyl derivative ( Amersham Buchler , Braunschweig , Germany ) . Lipid rafts were isolated from 1 x 108 Leishmania promastigotes or 8×107 Jurkat cells by using nonionic detergent lysis conditions and centrifugation on discontinuous sucrose gradients as previously reported [38 , 39] . Twelve 1-ml fractions were collected from the top of the gradient , and 25 μl of each fraction were subjected to sodium dodecylsulfate ( SDS ) -polyacrylamide gel electrophoresis ( PAGE ) and assayed for the location of GM1-containing lipid rafts using the GM1-specific ligand cholera toxin ( CTx ) B subunit conjugated to horseradish peroxidase ( Sigma , St . Louis , MO ) . The proteomic analysis was performed in the proteomics facility of Centro de Investigación del Cáncer ( CIC ) , Salamanca , Spain , which belongs to ProteoRed , PRB2-ISCIII . Samples ( 100 μg protein ) from pooled fractions enriched in lipid rafts ( fractions 3–6 from the sucrose gradient ) were precipitated with methanol/chloroform , and then the pellets were resuspended in rehydration buffer ( 7 M urea , 2M thiourea , 4% CHAPS , 50 mM DTT , 5 mM TCEP , 15 mg DeStreak , 0 . 5% IPG buffer ) . Samples were applied to 13 cm IPG strips with a nonlinear pH gradient of 3 to 10 ( Amersham Biosciences ) . Isoelectric focusing was performed at 50 V for 12 hours , 500 V for 1 h , 1000 V for 1 h , a voltage gradient ranging from 1000 to 8000 V for 30 min , and finally 5 h until the voltage reached 35000 V . Strips were treated with SDS equilibration buffer ( 375 mM Tris-HCl pH 8 . 8 , 6 M urea , 20% glycerol , 2% SDS ) plus 2% DTT for 15 min for protein denaturation , and then with equilibration buffer plus 2 . 5% iodoacetamide for protein alkylation . The second dimension electrophoresis was performed on 10% SDS-polyacrylamide gels . The protein spots were visualized with Sypro Ruby Protein Gel Staining ( Invitrogen , Carlsbad , CA ) . Spots of interest were automatically excised with Proteineer Spot Picker robotics workstation ( Bruker Daltonics , Billerica , MA ) . The digestion was performed as previously described [40] . For MALDI-TOF peptide mass fingerprinting , a 0 . 5 μl aliquot of matrix solution ( 5 g/l 2 , 5-dihydroxybenzoic acid in 33% aqueous acetonitrile plus 0 . 1% trifluoroacetic acid ) was manually loaded onto a 400 μm diameter AnchorChip Target plate ( Bruker Analytic GmbH , Bremen , Germany ) probe , and 1 μl of the above peptide extraction solution was added and allowed to dry at room temperature . Samples were analyzed on a Bruker Ultraflex MALDI-TOF mass spectrometer ( Bruker-Franzen Analytic GmbH , Bremen , Germany ) . Each raw spectrum was opened in FlexAnalysis 3 . 0 ( Bruker Daltonics ) software and processed and analyzed using the following parameters: signal-to-noise threshold of 1 , Savitzky-Golay algorithm for smoothing , tangential algorithm for baseline substraction , and centroid algorithm for monoisotopic peak assignment . In all cases , resolution was higher than 9000 . The generated peaks were submitted to Mascot Server ( version 2 . 2 , February 2007 ) [41] using Bio Tools 3 . 1 ( Bruker Daltonics ) software , and searched against Uniprot database for human sequences and NCBI database for Leishmania sequences . Search parameters were set as follow: searches were restricted to all sequences for human searches and Other Eukaryota ( 69482 sequences ) for Leishmania searches , up to one missed tryptic cleavage , mass accuracy of 100 ppm , MH+ monoisotopic masses , carbamidomethyl cysteine as fixed modification , and methionine oxidation as variable modification . Mowse scores with a value greater than 65 for human searches and 61 for Leishmania searches were considered as significant ( p<0 . 05 ) . Data are shown as mean ± SD . Between-group statistical differences were assessed using the Student’s t test . A P-value of <0 . 05 was considered statistically significant .
First we analyzed the ability of different APLs ( Fig 2A and 2B ) in promoting apoptosis-like cell death in different Leishmania spp . promastigotes and human cancer cell lines , as assessed by DNA breakdown determined by flow cytometry . Our results showed that APLs ranked edelfosine > miltefosine ≥ perifosine > erucylphosphocholine ( ErPC ) for their leishmanicidal activity ( Fig 2A ) , and edelfosine > perifosine > miltefosine ≅ erucylphosphocholine ( ErPC ) for their antitumor activity ( Fig 2B ) , when incubated for 24 h at 10 μM with several Leishmania spp . promastigotes , including L . donovani ( visceral leishmaniasis ) , L . panamensis ( cutaneous and mucocutaneous leishmaniasis ) , and L . major ( cutaneous leishmaniasis ) , or with human cancer cell lines , including myeloid leukemia HL-60 cells , multiple myeloma MM144 cells , and cervical cancer HeLa cells . This drug concentration ( 10 μM ) corresponded to the pharmacologically relevant concentration range of edelfosine in plasma ( 10–20 μM ) , previously determined in a number of in vivo and pharmacokinetic studies [19 , 42 , 43] . We also found that edelfosine was very efficient in promoting cell death in additional human leukemic cell lines , including human T-cell acute lymphoblastic leukemia ( T-ALL ) cell lines Jurkat ( 53 . 4 ± 6 . 2% apoptosis ) and CEM-C7H2 ( 58 . 2 ± 5 . 9% apoptosis ) . Edelfosine was equally effective against different Leishmania subgenera , including Leishmania Leishmania ( L . donovani , L . major ) and Leishmania Viannia ( L . panamensis ) ( Fig 2A ) . The relative difference between the abilities to promote cell death of edelfosine vs . miltefosine was more evident using tumor cells than Leishmania spp . promastigotes , suggesting that processes involved in the mechanisms of action of both drugs are partially conserved , but not identical . For the ensuing studies , we focused our attention on the most effective compound , namely edelfosine , which has been considered as the APL prototype . Edelfosine induced DNA breakdown after 9-h incubation with L . panamensis promastigotes , and the percentage of parasites with a hypodiploid DNA content ( sub-G0/G1 cell population ) increased with the incubation time ( Fig 2C ) , suggesting an apoptosis-like cell death in Leishmania parasites , similar to the apoptotic response triggered in cancer cells [15 , 17 , 35 , 44] . Edelfosine ( 5 or 10 μM ) also induced apoptosis-like cell death , as assessed by an increase in the sub-G0/G1 population , in L . panamensis axenic amastigotes ( Fig 2D ) . Because Leishmania are obligate intracellular parasites that infect macrophages within the mammalian host , we examined the location of edelfosine in L . panamensis-infected J774 macrophage-like cells . We have previously found that mouse J774 macrophages were rather resistant to edelfosine [26] , and 10 μM edelfosine was unable to mount an apoptotic response after 24-h incubation ( <2 . 5% apoptosis ) . Using the blue-emitting fluorescent edelfosine analog PTE-ET ( Fig 1 ) , a bona fide compound to explore the subcellular localization of edelfosine [17 , 19 , 36 , 37 , 45] , we found that it was mainly located into the parasites inside the macrophage ( Fig 2E ) , which were visualized by using infective GFP-L . panamensis parasites [25] . Edelfosine treatment highly diminished the amount of infected J774 macrophages and the number of parasites per macrophage ( Fig 2F and 2G ) . Following limiting dilution experiments , we found that edelfosine was the most effective APL , when compared to miltefosine , perifosine and erucylphosphocholine ( ErPC ) , in killing L . major amastigotes in infected mouse BMM ( Fig 2H ) . Edelfosine was highly dependent on its molecular structure for its antileishmanial activity , since a structurally related compound , ET-18-OH ( 1-O-octadecyl-rac-glycero-3-phosphocholine ) ( Fig 1 ) , containing a hydroxyl group instead of the methoxy group at the C2 position , was unable to kill Leishmania protozoa ( Fig 2H ) , similarly to what has been found in cancer cells [15 , 44] . We have previously shown the potent antitumor activity of orally-administered edelfosine in different xenograft animal models [19 , 43 , 46] . Recently , we have also found that edelfosine was effective in the treatment of leishmaniasis in different animal models when used at 26 mg/kg body weight [26] . Here , we found that oral treatment of edelfosine at a lower dose ( 20 mg/kg body weight ) exerted a potent in vivo antileishmanial activity in L . panamensis–infected golden hamsters ( Fig 2I–2K ) , an appropriate animal model for reproducing the pathological features of human leishmaniasis [47] . L . panamensis promastigotes were inoculated into the nose of 16 golden hamsters , and then animals were randomly distributed into two cohorts of eight hamsters . Each cohort received a daily oral administration of edelfosine or water vehicle ( control ) for 28 days . Disease progression was monitored by nasal swelling , determined by serial caliper measurements , and ulceration . Oral treatment with edelfosine led to a dramatic decrease in nasal swelling and parasite load at the site of infection ( Fig 2I–2K ) , and ameliorated the signs of leishmaniasis , leading to an almost normal morphologic appearance ( Fig 2K ) . Edelfosine has been shown to be taken up preferentially by tumor cells , whereas normal non-malignant cells incorporated a relatively much lesser amount of the ether lipid [15 , 17 , 35] . Here , we found that normal mouse BMM took up large amounts of edelfosine , at even higher levels than the mouse RAW 309 Cr . 1 tumor macrophage cell line ( Fig 3A ) . However , edelfosine induced cell death in the transformed macrophage cell line , but spared BMM ( Fig 3B ) . Interestingly , edelfosine blocked zymosan-induced respiratory burst in BMM ( Fig 3C ) . Furthermore , BMM from mice that were orally treated with edelfosine ( 5 mg/kg body weight , daily ) for two weeks showed a lower capacity to generate superoxide anion , NO and IL-12+IL-18-induced IFN-γ , when compared to BMM from mice treated with water vehicle ( Fig 3D–3F ) . These results suggest that edelfosine treatment decreases macrophage pro-inflammatory responses . Our data , together with the above accumulation of edelfosine into the parasites in Leishmania-infected macrophages , suggest that edelfosine-induced killing of Leishmania is mediated by a direct action of the drug on the parasite , and not via generation of macrophage-derived antiparasitic molecules . DNA breakdown induced by edelfosine treatment in Leishmania was further assessed by the TUNEL assay , staining all cells in red through the binding of propidium iodide to DNA , and only those cells with fragmented DNA and free 3’-OH ends in green . Interestingly , we detected first kinetoplast-mitochondrial DNA degradation , followed by nuclear DNA fragmentation upon treatment of L . panamensis promastigotes with edelfosine ( Fig 4A ) . These results suggest that the death process induced by edelfosine in Leishmania spp . parasites starts at the mitochondrial level . Next , we analyzed the subcellular localization of edelfosine in Leishmania promastigotes . The fluorescent edelfosine analog PTE-ET , which has been previously shown to fully mimic the antitumor [17 , 19 , 36 , 37 , 45 , 48] and antileishmanial [49] actions of the parent drug edelfosine , accumulated mainly in the mitochondria of L . panamensis promastigotes , as indicated by colocalization with the specific mitochondrial marker MitoTracker ( Fig 4B ) . PTE-ET also co-localized with MitoTracker-positive structures in human cervical carcinoma HeLa cells ( Fig 4C ) . We next examined the time-course of the effect of edelfosine on the following mitochondrial-related processes in L . panamensis promastigotes: a ) ROS generation , through the conversion of non-fluorescent dihydroethidine ( HE ) into red fluorescent ethidium ( Eth ) after its oxidation via ROS; and b ) changes in ΔΨm , through the accumulation of the fluorescent cationic probe DiOC6 ( 3 ) ( green fluorescence ) , which depends on the mitochondrial potential . As shown in Fig 5A , untreated parasites exhibited a high ΔΨm [ ( DiOC6 ( 3 ) ) high] , and the levels of intracellular ROS were undetectable [ ( HE → Eth ) low] . Edelfosine induced first an increase in the percentage of cells with ( HE → Eth ) high , and then a loss in ΔΨm ( Fig 5A ) . Changes in ROS generation and ΔΨm disruption apparently preceded DNA breakdown . Edelfosine induced Eth staining , i . e . ROS generation , in kinetoplasts , as assessed by using DNA staining to identify L . panamensis nuclei and kinetoplasts ( mitochondrial DNA ) ( Fig 5B ) . The inhibitor of the mitochondrial permeability transition pore cyclosporin A [50] , and the antioxidant agents glutathione ( GSH ) and N-acetylcysteine ( NAC ) , inhibited edelfosine-induced cell death in L . panamensis promastigotes ( Fig 5C ) . Likewise , cyclosporin A , GSH and NAC inhibited edelfosine-induced apoptosis in human T-cell leukemia Jurkat cells ( Fig 5D ) . Taken together , our data suggest a critical role of mitochondria in the antileishmanial and antitumor activities of edelfosine , and that both ROS generation and ΔΨm disruption are involved in edelfosine-induced cell death in Leishmania parasites and human cancer cells . We next examined whether the mitochondrial respiration chain was involved in edelfosine-induced ROS production , by using the following mitochondrial respiration inhibitors: rotenone , complex I inhibitor; malonate , complex II inhibitor; antimycin A , complex III inhibitor; azide , complex IV inhibitor; oligomycin , mitochondrial FOF1-ATP synthase inhibitor; and carbonyl cyanide m-chlorophenyl hydrazone ( CCCP ) , one of the most frequently used uncouplers of oxidative phosphorylation [51] . Incubation of cells with CCCP , which disrupts the proton gradient by carrying protons across the mitochondrial membrane and causes mitochondrial depolarization , prompted the generation of ROS in L . panamensis and Jurkat cells , and increased edelfosine-induced ROS generation ( Fig 5E and 5F ) . Incubation of L . panamensis and Jurkat cells for 9 h with rotenone , malonate , antimycin A or azide , affecting the electron transport at specific sites , neither promoted ROS generation nor affected edelfosine-mediated ROS production significantly ( Fig 5E and 5F ) . In contrast , oligomycin , a specific inhibitor of the membranous proton channel ( FO ) of mitochondrial FOF1-ATP synthase [52] , reduced ROS production levels induced by edelfosine in L . panamensis and Jurkat cells ( Fig 5E and 5F ) . Lower concentrations of oligomycin in L . panamensis ( 1 μM ) as compared to Jurkat cells ( 10 μM ) were used , because Leishmania parasites were very sensitive to higher concentrations of oligomycin , resulting in cytotoxicity . Thus , our data suggest that FOF1-ATP synthase plays a role in edelfosine-mediated ROS production in both Leishmania and tumor cells , a process that eventually leads to cell death . In order to generalize and further support the role of mitochondria in edelfosine-induced cell death in Leishmania parasites and tumor cells , L . infantum and HeLa cells stably transfected with the expression vectors pX63-Neo ( Leishmania ) and pSFFV-Neo ( HeLa ) , containing the human bcl-xL open reading frame ( pX63-bcl-xL and pSFFV-bcl-xL , respectively ) , were used . L . infantum and HeLa cells transfected with control empty pX63-Neo and pSFFV-Neo plasmids , respectively , were used as controls and behaved similarly to wild-type nontransfected Leishmania promastigotes and tumor cells , regarding edelfosine-induced cell death ( Table 1 ) . We found that Bcl-XL ectopic expression in L . infantum promastigotes and HeLa tumor cells inhibited the percentage of hypodiploid cells following edelfosine treatment ( Table 1 ) , further supporting the critical role of mitochondria in the induction of apoptosis-like cell death in Leishmania and tumor cells treated with edelfosine . The concentration of edelfosine was increased to 40 μM in the case of L . infantum promastigotes as they were rather resistant to APL treatment [26] . Taken together , these assays support a crucial role of mitochondria in edelfosine-induced cell death in both Leishmania spp . parasites and tumor cells . Thus , our results with two different species of Leishmania , causing distinct forms of disease , namely L . panamensis ( cutaneous and mucocutaneous leishmaniasis ) and L . infantum ( visceral leishmaniasis ) , converge on the critical role of mitochondria in the killing activity of edelfosine on Leishmania parasites . Because membrane rafts are a major target in the antitumor action of edelfosine [17–19 , 36 , 39] , we analyzed , in a comparative way , the putative role of lipid rafts in the antileishmanial and anticancer activities of edelfosine . First , we found that raft disruption by preincubation of L . panamensis promastigotes with the cholesterol-depleting agent MCD [53] , led to a partial , but statistically significant , inhibition of both edelfosine-induced cell death ( Fig 6A ) and edelfosine uptake ( Fig 6B ) , suggesting a role for lipid rafts in Leishmania cell death . In addition , cholesterol depletion by MCD strongly inhibited edelfosine-induced cell death and drug uptake in the human T-cell leukemia Jurkat cells ( Fig 6A and 6B ) . It is interesting to note that edelfosine uptake , assessed by the incorporation of [3H]edelfosine , was higher in Leishmania promastigotes than in tumor cells . The anticancer activity of edelfosine has been shown to depend on the redistribution of lipid raft protein composition [17–19 , 36 , 38 , 39 , 54] . Because the above data indicated a remarkable parallelism between the mechanisms of action of edelfosine against Leishmania parasites and leukemic cells , we next isolated lipid rafts from untreated and edelfosine-treated L . panamensis promastigotes by fractionation of low-density detergent-insoluble membranes using discontinuous sucrose density gradient centrifugation . The position of lipid rafts in the sucrose gradients was determined by the presence of ganglioside GM1 , detected using the GM1-specific ligand CTx B subunit ( Fig 7A ) , which binds ganglioside GM1 [55] , mainly found in rafts [56] . Following a proteomic study of the lipid raft fractions in L . panamensis promastigotes , we found a dramatic translocation to lipid rafts of mitochondrial FOF1-ATP synthase β subunit following drug incubation in L . panamensis promastigotes ( Fig 7B and 7C ) . In addition , we found that oligomycin inhibited edelfosine-induced ΔΨm disruption and cell death in Leishmania ( Fig 7D ) . These data suggest the involvement of FOF1-ATP synthase and its translocation to lipid rafts in the anti-Leishmania activity of edelfosine . We also isolated lipid rafts from untreated and edelfosine-treated human T-cell leukemia Jurkat cells through sucrose gradient centrifugation , and the fractions enriched in lipid rafts were identified using the GM1-specific ligand CTx B subunit ( Fig 8A ) . Similarly to L . panamensis parasites , a proteomic study of the lipid raft fractions from untreated and drug-treated cancer cells showed a major translocation of mitochondrial FOF1-ATP synthase β subunit to lipid rafts upon drug incubation in Jurkat cells ( Fig 8B and 8C ) . Furthermore , oligomycin inhibited edelfosine-induced ΔΨm loss and cell death in Jurkat cells ( Fig 8D ) . Because the above data suggested that FOF1-ATP synthase could play a major role in the cytotoxic activity of edelfosine against Leishmania promastigotes and cancer cells , we next sought to obtain genetic evidence for the role of this enzyme in edelfosine cytotoxicity by using yeast as a eukaryotic model organism . We used Saccharomyces cerevisiae yeast atp7Δ mutant , with a deletion in the gene encoding for subunit d of the stator stalk of mitochondrial FOF1-ATP synthase , which is conserved in mammalian cells [57] . Since yeast is a facultative anaerobe and can survive with severely crippled mitochondrial function , we employed S . cerevisiae , which has been previously shown to be sensitive to edelfosine [58] , as a good model for genetic ablation assays . We chose the yeast atp7Δ mutant because ATP7 is essential for FOF1-ATP synthase function , but is not essential for growth in yeast . ATP7 deletion leads to a “petite” phenotype that is slow-growing and unable to survive on nonfermentable carbon sources [57] . We found that edelfosine inhibited wild-type yeast growth at 30 and 60 μM ( Fig 9A ) , but atp7Δ mutant strain was resistant at these drug concentrations ( Fig 9B ) . This edelfosine-resistant phenotype was reverted by transformation of the atp7Δ mutant with a centromeric plasmid containing the atp7 wild-type gene ( Fig 9C ) . Taken together , these results strongly support the involvement of FOF1-ATPase in the killing activity of edelfosine .
Leishmaniasis therapy is currently far from satisfactory and search for novel druggable targets and new therapeutic approaches is urgently needed . APLs , originally developed as anticancer agents , have proved to show antileishmanial activity , but their mechanisms of action remain to be fully elucidated . Our data reported here indicate that the APL edelfosine is a promising drug against Leishmania spp . parasites and tumor cells , and unveil common underlying processes in the killing activity of this APL on both Leishmania and cancer cells . Edelfosine killed both Leishmania promastigotes and amastigotes by an apoptosis-like process involving DNA breakdown , and edelfosine oral treatment exerted a potent in vivo antileishmanial activity . We found here that edelfosine killed intracellular Leishmania amastigotes within macrophages , but spared the host cells . Results reported here point out a number of remarkable actions of edelfosine on macrophages , namely: a ) normal BMM take up edelfosine , but unlike cancer cells , they are spared; b ) edelfosine accumulates in the Leishmania amastigotes inside macrophages; c ) edelfosine treatment , in vitro and in vivo , reduces the pro-inflammatory capacity of macrophages . These results suggest that edelfosine kills Leishmania parasites by acting directly on the parasite . Edelfosine and other APLs have been shown to act rather selectively on a wide range of malignant cells , mainly due to their predominant uptake by tumor cells [15 , 17–19 , 43 , 59] . The ability of macrophages to take up edelfosine constitutes the first evidence for the incorporation of edelfosine in a normal resting cell type at similar amounts as in cancer cells . This is of major importance in leishmaniasis because macrophages are the main host cells in Leishmania infection . We have previously found that the fluorescent edelfosine analog PTE-ET accumulated into naïve macrophages , especially around the nucleus , but once naïve macrophages were infected with Leishmania spp . , an intense fluorescent signal was detected in the intracellular parasites [26] . These data , together with the findings reported here , indicate that edelfosine is taken up by naïve macrophages in significant amounts , and therefore it might affect some macrophage functions , such as the ones herein described , namely , a decrease in the generation of superoxide anion and nitric oxide , as well as in the production of IL-12+IL-18-induced IFN-γ . Interestingly , when Leishmania parasites enter the macrophage , a substantial amount of drug is translocated to the sites where the parasites are located and then the drug is incorporated into the protozoa [26] . Thus , edelfosine could affect both macrophage functions and Leishmania survival . The finding that edelfosine diminishes the capacity of macrophages to mount an inflammatory response might be relevant , as severe inflammation at the site of infection leads to tissue destruction in leishmaniasis [60] . In this regard , edelfosine has also been reported to display a potent anti-inflammatory action through L-selectin shedding in human neutrophils , thus preventing neutrophil extravasation [31] , and recent in vivo evidence further supports the anti-inflammatory and immunomodulatory effect of edelfosine [61–63] . Furthermore , edelfosine promotes apoptosis in mitogen-activated T lymphocytes [64] . On these grounds , edelfosine can affect in different ways the major leukocyte types involved in inflammation , namely neutrophils , macrophages and lymphocytes , thus leading eventually to decreased inflammatory responses . We have also found here that edelfosine accumulates in mitochondria in both Leishmania parasites and tumor cells , leading to ΔΨm loss and an apoptosis-like cell death . These results agree with recent reports showing a major location of different fluorescent edelfosine analogs in the mitochondria of cancer cells [37 , 65] . Our data indicate that edelfosine induces firstly DNA fragmentation in the Leishmania kinetoplast-mitochondrion followed by nuclear DNA breakdown , while cell death in Leishmania parasites and tumor cells can be inhibited by protecting mitochondria through ectopic Bcl-XL expression . These results indicate a critical role of mitochondria in the edelfosine-induced cell killing mechanism in Leishmania parasites and tumor cells . Interestingly , the data reported here suggest that FOF1-ATP synthase plays a principal role in the edelfosine-induced killing activity in both Leishmania parasites and cancer cells . The involvement of the FOF1-ATP synthase in edelfosine cytotoxicity was further assessed through gene deletion experiments conducted in yeast , by showing that the lack of ATP7 , which results in a defective FOF1-ATP synthase , inhibited edelfosine toxicity . Drug sensitivity was restored when atp7Δ mutant yeast were transformed with the cognate wild-type gene . Thus , the results shown here strongly indicate by genetic and biochemical approaches that FOF1-ATP synthase is involved in the killing activity of edelfosine in both Leishmania parasites and human tumor cells . The major role of mitochondria in edelfosine-induced Leishmania killing was further assessed by the generation of ROS in the parasite mitochondrion and the involvement of ROS in edelfosine-induced Leishmania promastigote cell death . Interestingly , edelfosine-induced ROS generation in Leishmania promastigotes was inhibited by oligomycin , an inhibitor of the FO subunit of the mitochondrial FOF1-ATP synthase . Taken together , our data suggest a role for mitochondria and ROS generation in the execution of edelfosine-mediated apoptosis , and oligomycin is able to prevent edelfosine-induced ΔΨm collapse and DNA degradation in both Leishmania parasites and cancer cells . These data highlight a major role of the FO component of the FOF1-ATP synthase in the edelfosine-induced ΔΨm dissipation , ROS generation and cell death . In this regard , the involvement of FOF1-ATP synthase in the apoptotic response induced in glioblastoma cells by erucylphosphomocholine ( ErPC3 , Erufosine ) , another APL member , has been suggested [66] . Furthermore , oligomycin has also been reported to suppress TNF-induced apoptosis in human epithelioid carcinoma HeLa cells [67] . The mechanism by which FOF1-ATPase contributes to edelfosine-induced cell death remains to be established . FOF1-ATPase resides in the inner membrane of mitochondria and can pump protons in forward and reverse directions , either pumping protons into the mitochondrial matrix , flowing down their concentration gradient and leading to ATP generation , or pumping protons out of the mitochondrial matrix while hydrolyzing ATP . Because edelfosine affects membrane lipid organization , making membranes more fluid [68 , 69] , it might be suggested that edelfosine makes the outer membrane more porous , thus favoring the leakage of H+ ions from the outer-inner intermembrane space into the cytosol , which leads to the dissipation of the proton gradient . As a consequence , the FOF1-ATP synthase could run in reverse , that is , hydrolyzing ATP and alkalinizing the matrix by proton extrusion . Because matrix alkalinization has been shown to cause opening of the mitochondria permeability transition pore [70] , the FOF1-ATP synthase could facilitate cell death by this mechanism . This explanation has been previously proposed for the effect of oligomycin in inhibiting Bax-induced apoptosis in yeast and mammalian cells [71] . In this regard , we have found that edelfosine treatment led to a reduction in the ATP content of L . panamensis promastigotes ( Fig 10 ) . Furthermore , edelfosine has been reported to act through lipid rafts in human leukemic cancer cells [17–19 , 22 , 23 , 36 , 39] , and recent evidence suggests a raft-mediated connection between the cell membrane and mitochondria in the action of edelfosine [20 , 37 , 38] . Here , we have found the involvement of lipid rafts in the antileishmanial activity of edelfosine , and edelfosine treatment led to a dramatic recruitment of mitochondrial FOF1-ATP synthase into rafts in both Leishmania promastigotes and cancer cells . These findings are in line with the identification of lipid rafts in Leishmania parasites [24] , and with the impairment of miltefosine action against L . donovani by membrane sterol depletion [72] . The results reported here suggest a redistribution of the FOF1-ATP synthase within the mitochondria or to additional raft domains in other cellular membranes following edelfosine treatment , thus altering the normal function of the enzyme and affecting cell viability . It could also be envisaged that the action of edelfosine on lipid rafts and mitochondria might underlie the inhibition of superoxide anion production in edelfosine-treated macrophages , generated by the NADPH-oxidase located at the macrophage cell membrane [73] , and the enhancement of mitochondria-dependent ROS generation in drug-sensitive cells . The results reported here highlight a major role for mitochondria and lipid rafts in the mechanism of action of edelfosine as both antileishmanial and anticancer drug . Nevertheless , cancer cells seem to be more dependable on lipid rafts than parasites , as shown by the relatively higher inhibition observed in drug uptake and drug-induced cell death when rafts were disrupted by sterol depletion ( Fig 6 ) . This putative mechanism of action involving mitochondria , and briefly depicted in Fig 11 , seems to be common to both Leishmania parasites and tumor cells . The fact that protecting mitochondria by Bcl-XL ectopic expression leads to an inhibition in drug-induced cell death , further supports the major role of mitochondria and mitochondrial-mediated pathways in the killing activity of edelfosine in both Leishmania parasites and human cancer cells . Previous data on human cancer cells have demonstrated the involvement of mitochondria in the pro-apoptotic activity of edelfosine as an antitumor drug [16 , 18 , 20 , 37 , 38 , 46 , 74] , and the results reported here extrapolate this notion to its leishmanicidal activity . In addition , the present results pinpoint the major role of FOF1-ATPase in the killing activity of edelfosine against Leishmania parasites and tumor cells . We have previously found a link between lipid rafts and mitochondria in the mechanism of action of edelfosine [37 , 38] , suggesting an edelfosine-mediated redistribution of lipid rafts from the plasma membrane to mitochondria [37 , 38] . The results reported here indicate that FOF1-ATPase is either translocated to cell surface lipid rafts or to raft domains present in mitochondria . A number of reports have shown the presence of raft-localized FOF1-ATP synthase at the cell surface of several cell types , having been proposed to act as a receptor for different ligands , a proton channel , or a modulator of extracellular ATP levels , involved in numerous biological processes through still unclear mechanisms [75–81] . Our results cannot discern between a cell surface and a mitochondrial localization for the raft-associated FOF1-ATP synthase following edelfosine treatment reported here . Thus , a putative translocation of the mitochondrial FOF1-ATP synthase to the cell surface cannot be ruled out at the moment , and additional experimental approaches should be applied to elucidate the prevailing localization of raft-located FOF1-ATP synthase . However , our present data indicating an accumulation of the ether lipid in the mitochondria of both Leishmania parasites and cancer cells , lead us to suggest that a plausible mechanism could involve the translocation of edelfosine from the plasma membrane to the mitochondria where it would ultimately exert its cytotoxic activity promoting the accumulation of FOF1-ATP synthase into mitochondrial rafts , and leading to the dissipation of the mitochondrial membrane potential , ROS generation , and eventually cell demise ( Fig 11 ) . The fact that kinetoplast-mitochondrion was the first organelle where ROS metabolites were generated and DNA was broken down , preceding nuclear DNA fragmentation , points out the critical role of mitochondria as a major target in the search for effective drugs to treat leishmaniasis . It is tempting to suggest that a link between lipid rafts and mitochondria could lead to interesting hints to unveil a novel framework in both Leishmania and cancer therapy . The present data also indicate that our insight on how edelfosine works as an antitumor drug can be of great aid to and give valuable information to uncover the mechanism of action of its leishmanicidal activity , which could be hypothetically extrapolated to other antileishmanial drugs , and might be of inspiration to further identify potential common therapeutic targets in cancer and leishmaniasis . Taken together , our data indicate that the edelfosine antileishmanial and antitumor mechanisms of action share similar molecular processes , involving mitochondria , lipid rafts and FOF1-ATPase . This study provides a molecular explanation on how the antitumor drug edelfosine acts as an antileishmanial agent , and highlights that mitochondria , lipid rafts and FOF1-ATPase act as major players in cell death modulation , opening new avenues for therapeutic intervention in leishmaniasis and cancer . Our results show that the ether phospholipid edelfosine can be a promising orally administered therapeutic agent and a lead compound in the search for novel and much-needed antileishmanial agents , and identify lipid rafts , mitochondria and FOF1-ATPase as appealing new antileishmanial targets . Furthermore , the results shown here indicate that edelfosine is very effective in killing different species of Leishmania parasites , as well as in distinct developmental stages , such as promastigotes and amastigotes . Interestingly , recent data have shown an increasing rate of relapse against miltefosine and a decline in its efficacy [82–85] , which could correspond to the readiness in acquiring experimental drug resistance to miltefosine in vitro [86–88] . We have previously shown that edelfosine is less prone to lead to drug resistance development than miltefosine , and displays a higher antileishmanial activity than miltefosine against a wide variety of Leishmania spp . [26] . Thus the potent leishmanicidal activity of edelfosine , together with its low toxicity profile [31] , warrants further clinical evaluation for this ether lipid as a possible therapeutic agent against different forms of leishmaniasis .
|
Leishmaniasis is a major health problem worldwide , and can result in loss of human life or a lifelong stigma because of bodily scars . According to World Health Organization , leishmaniasis is considered as an emerging and uncontrolled disease , and its current treatment is far from ideal , with only a few drugs available that could lead to drug resistance or cause serious side-effects . Here , we have found that mitochondria and raft-located FOF1-ATPase synthase are efficient druggable targets , through which an ether lipid named edelfosine exerts its antileishmanial action . Edelfosine effectively kills Leishmania spp . promastigotes and amastigotes . Our experimental animal models demonstrate that oral administration of edelfosine exerts a potent antileishmanial activity , while inhibits macrophage pro-inflammatory responses . Our results show that both Leishmania and tumor cells share mitochondria and raft-located FOF1-ATPase synthase as major druggable targets in leishmaniasis and cancer therapy . These data , showing a potent antileishmanial activity of edelfosine and unveiling its mechanism of action , together with the inhibition of the inflammatory responses elicited by macrophages , suggest that the ether lipid edelfosine is a promising oral drug for leishmaniasis , and highlight mitochondria and lipid raft-located FOF1-ATP synthase as major therapeutic targets for the treatment of this disease .
|
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2017
|
Mitochondria and lipid raft-located FOF1-ATP synthase as major therapeutic targets in the antileishmanial and anticancer activities of ether lipid edelfosine
|
Epstein-Barr Virus ( EBV ) is an enveloped double-stranded DNA virus of the gammaherpesvirinae sub-family that predominantly infects humans through epithelial cells and B cells . Three EBV glycoproteins , gH , gL and gp42 , form a complex that targets EBV infection of B cells . Human leukocyte antigen ( HLA ) class II molecules expressed on B cells serve as the receptor for gp42 , triggering membrane fusion and virus entry . The mechanistic role of gHgL in herpesvirus entry has been largely unresolved , but it is thought to regulate the activation of the virally-encoded gB protein , which acts as the primary fusogen . Here we study the assembly and function of the reconstituted B cell entry complex comprised of gHgL , gp42 and HLA class II . The structure from negative-stain electron microscopy provides a detailed snapshot of an intermediate state in EBV entry and highlights the potential for the triggering complex to bring the two membrane bilayers into proximity . Furthermore , gHgL interacts with a previously identified , functionally important hydrophobic pocket on gp42 , defining the overall architecture of the complex and playing a critical role in membrane fusion activation . We propose a macroscopic model of the initiating events in EBV B cell fusion centered on the formation of the triggering complex in the context of both viral and host membranes . This model suggests how the triggering complex may bridge the two membrane bilayers , orienting critical regions of the N- and C- terminal ends of gHgL to promote the activation of gB and efficient membrane fusion .
Epstein Barr Virus ( EBV ) or Human Herpesvirus 4 ( HHV-4 ) is a gammaherpesvirus that is ubiquitous in humans and predominantly infects host epithelial and B cells , in which it establishes long-term latency . It is an oncogenic virus associated with a wide array of human tumors including epithelial cell tumors such as nasopharyngeal and gastric carcinomas , and lymphoid malignancies like Hodgkin and Burkitt lymphoma . Primary infection in children and young adults manifests itself as acute infectious mononucleosis . Subsequent reactivations of the virus are asymptomatic and managed effectively by the immune system in healthy adults . However , immunocompromised patients suffer from severe opportunistic disorders , such as post-transplant lymphoproliferative disease ( PTLD ) , oral hairy leukoplakia and HIV/AIDS related malignancies [1] . Viral membrane fusion is a requisite step for infection for all lipid bilayer encased viruses , such as the herpesviruses , and requires one or several virus-encoded glycoproteins that orchestrate the merging of viral and host membranes in a step-wise manner [2] . This overall process leads to the release of viral capsid into the host cytoplasm initiating infection . The entry of EBV into B cells is complex and involves at least five different glycoproteins ( EBV gp350/220 , gH , gL , gp42 and gB ) [3]–[6] . Of these five proteins , four ( gH , gL , gB and gp42 ) are indispensable for membrane fusion with B cells and three ( gH , gL and gB ) are required for fusion with epithelial cells [7] , [8] . gH , gL and gB form the core fusion machinery common to all herpesviruses . During B cell entry , EBV gp350/220 binds to complement receptor 2 ( CR2/CD21 ) [9]–[11] concentrating virus to the B cell surface , but this interaction does not activate membrane fusion or virus entry . The gp42 protein forms stable , high affinity complexes with the gHgL complex [12] , and also binds to human leukocyte antigen ( HLA ) class II [13] which acts as the triggering receptor for EBV entry into B cells [4] . The role of gHgL in herpesvirus entry has remained unclear , but most recently it has been suggested to act primarily as a regulator of gB activation rather than as a direct participant in driving membrane fusion [14] . gB is the most conserved glycoprotein in the herpesvirus family and it belongs to the class III viral fusion protein group , which includes the VSV G and baculovirus gp64 fusion proteins [15] . The putative post-fusion crystal structures of HSV and EBV gB have been solved [16] , [17] and functional studies have highlighted its role as the likely fusion protein , with two critical fusion loops that are thought to interact directly with the host membrane [18]–[20] . Based on its structural similarity with the VSV G [21] protein , herpesvirus gB is thought to undergo a large conformational change that would drive virus-cell membrane fusion [4] . A key role of EBV gp42 is in determining the cell tropism of the virus . Binding of gHgL/gp42 complexes to HLA class II drives B cell infection , but gp42 binding to gHgL inhibits EBV entry into epithelial cells [4] , [8] , [22] . This inhibitory effect can be recapitulated by peptides containing ∼33 residues from the N-terminus of gp42 [23] , which bind gHgL with similar high affinity as intact gp42 . Epithelial cell entry is thought to require a direct interaction between gHgL and integrin receptors ( αvβ5 , αvβ6 , and αvβ8 but not αvβ3 ) , distinguishing at least two distinct modes of entry mediated by gHgL [24]–[26] associated with the two different physiological target cells of the virus . EBV gp42 is a multifunctional type II glycoprotein , with an N-terminal domain of ∼100 amino acids and a C-terminal C-type lectin domain ( CTLD ) . The N- and C- terminal domains engage gHgL and HLA , respectively , to mediate B cell membrane fusion and virus entry . We previously reported the structures of gp42 in the presence and absence of HLA class II [27] , [28] . The complex structure revealed that HLA class II binds to the gp42 CTLD ( residues 94–221 ) only through the HLA β-chain . The gp42 structures have shown that its N-terminal region ( residues 33–93 ) is extended and mostly disordered . Gp42 binds gHgL through this flexible N terminal region with nanomolar affinity [12] , [23] and the interaction has been mapped to gp42 residues 36–81 [29] . We also identified a hydrophobic pocket ( HP ) located at the gp42 CTLD canonical binding site [27] , [30] , which is important for its ability to trigger membrane fusion subsequent to HLA binding . The role of the hydrophobic pocket in the gp42 CTLD has remained elusive , but we have observed that it undergoes a small structural change after gp42 binding to HLA [27] , [28] , which could be important in activating membrane fusion . Mutations within this pocket inhibit fusion , but not binding to gHgL or HLA , highlighting its functional importance in B cell fusion [30] . Finally , the gp42 ectodomain is cleaved in-vivo after the N-terminal transmembrane domain ( residues 9–29 ) and this cleavage is required for productive viral fusion [31] . EBV gHgL is a heterodimeric protein with a rod-shaped structure ( 100 Å long and 30–60 Å wide ) having four domains arranged linearly one after the other [32] . gL is entirely contained in domain I ( D-I ) , which also includes the N-terminal 65 residues of gH . Mutations that affect membrane fusion are found throughout the EBV gHgL molecule , but studies have identified clusters of important residues within the N- and C-terminal domains and the D-I/D-II interface [33]–[36] . Structural comparisons with HSV-2 gHgL and PRV gH structures also reveal conformational differences in the interdomain arrangements , which could have functional significance [37] , [38] . In particular , the D-I/D-II interdomain arrangement of EBV gHgL differs from HSV-2 gHgL , giving rise to a large groove present only in the EBV gHgL structure [32] , [37] . This D-I/D-II groove is adjacent to a KGD motif implicated in binding integrins for mediating EBV entry into epithelial cells . Mutation of the KGD loop also has effects on gp42 binding and B cell fusion [39] . Here we study the assembly and structure of the biochemically reconstituted gHgL/gp42/HLA triggering complex . We demonstrate that the two gp42 ligands , gHgL and HLA , bind essentially independently of each other , consistent with HLA receptor binding inducing limited conformational changes in the viral protein complex [28] . We determined the 29-Å resolution structure of the complex by negative-stain electron microscopy ( EM ) , demonstrating that it can adopt open and closed conformations and revealing that the functionally important gp42 HP interacts with gHgL . We further demonstrate that mutations located at this novel gHgL-gp42 interface disrupt membrane fusion . The overall architecture of the B cell triggering complex suggests that it may participate in early stages of EBV-mediated B cell entry by bringing viral and target cell membranes into closer proximity , and by positioning key residues in gHgL to engage the gB fusion protein .
We previously demonstrated that EBV gHgL and gp42 form a tight 1∶1 complex [12] and that the holo-complex with a representative HLA molecule , HLA-DR1 , can be prepared and isolated biochemically [40] . Here we expanded these studies to encompass another HLA isotype , HLA-DQ2 , which also serves as an EBV B cell entry receptor [4] , [41] , [42] . The individual proteins , gHgL , gp42 and HLA-DQ2 , were expressed and purified and their interactions initially characterized by gel filtration chromatography ( Figure 1A ) . The purified proteins exhibited homogenous single peak profiles consistent with their approximate molecular weights ( Table 1 ) . Using a Superdex 200 gel filtration column , the gp42 protein eluted at volume of ∼14 . 5 ml corresponding to an estimated MW of 50 kDa , the HLA-DQ2 eluted at ∼12 . 8 ml ( apparent MW ∼104 kDa ) and the gHgL eluted at ∼12 . 5 ml ( apparent MW ∼118 kDa ) ( Figure 1A ) . Addition of gp42 to gHgL resulted in the quantitative formation of a new , higher molecular weight peak eluting at ∼11 . 6 ml ( MW ∼174 kDa ) , consistent with previous observations [12] . The addition of excess HLA-DQ2 to preformed gHgL/gp42 complex resulted in the formation of an even larger complex eluting at 10 . 7 ml with an estimated apparent MW of ∼255 kDa , consistent with HLA-DQ2 forming stable complexes similar to those observed with HLA-DR1 [40] ( Figure 1A ) . The components of each peak were verified by SDS-PAGE ( data not shown ) , further demonstrating the recruitment of all proteins into the larger ∼255 kDa complex peak . The elution volumes and estimated molecular weights of the proteins and complexes are collected in Table 1 . Similar complexes could be assembled with HLA-DQ2 loaded with two different peptides in the HLA peptide-binding groove ( both endogenous and exogenous , in the form of CLIP1 and α1 gliadin peptides respectively ) , consistent with a minimal impact of HLA-bound peptides on the gp42 interaction and EBV entry . We refer to the reconstituted gHgL/gp42/HLA-DQ2 complex as the “triggering complex” for B cell entry . Studies of the gp42 structure alone and in complex with HLA-DR1 suggested the potential for conformational changes to be induced in the gp42 hydrophobic pocket ( HP ) after receptor binding [27] , [28] . Such conformational changes could relay the information from HLA receptor binding to enable gHgL activation of gB-mediated membrane fusion . We therefore investigated quantitatively the assembly of the triggering complex , in order to establish if the energy of HLA receptor binding might be used to initiate conformational changes in gHgL/gp42 . The assembly of the gHgL/gp42/HLA complex can proceed from the isolated proteins along two pathways that form a closed thermodynamic cycle ( Figure 1B ) . The binding of two ligands to gp42 is not necessarily independent and there are a number of mechanistic scenarios in which binding of gHgL and HLA to gp42 could be cooperative or competitive . If gp42 binding to gHgL is independent of HLA binding to gp42 , then the interaction of gp42 or gHgL/gp42 complexes with HLA-DQ2 would have identical affinities ( Figure 1B; top and bottom of the reaction cycle ) . Similarly , if the binding of these two gp42 ligands is independent , then gp42 binding to gHgL should be identical to the binding of gp42/HLA-DQ2 complexes to gHgL ( Figure 1B; left and right of the reaction cycle ) . However , if the binding of HLA to gp42 induces , for example , conformational changes that are transmitted to gHgL , or if gHgL and HLA molecules exhibit any other form of coupling energetics upon binding to gp42 , this would be reflected in changes in the binding affinities of bound vs . free gp42 with either ligand . To examine the binding rates and affinities associated with the formation of the gHgL/gp42/HLA complex , we used biolayer interferometry ( BLI ) binding methods using a ForteBio Octet RED96 biosensor instrument ( Figure 2 ) . In a first set of experiments , biotinylated gHgL was immobilized on Streptavidin ( SA ) biosensor tips and gp42 binding was studied by varying its concentration over the range of 0 . 4–100 nM ( Figure 2A ) . Previous experiments using fluorescence polarization ( FP ) to measure the binding between gHgL and gp42-derived peptides have estimated the binding affinity to be on the order of 1–5 nM [23] , [29] . The Octet binding data was fit globally with a 1∶1 interaction model , providing an overall affinity value ( KD ) of 1 . 4 nM for the EBV gHgL interaction with gp42 ( Figure 2A and Table 2 ) and a t1/2 for dissociation of ∼30 minutes . Equilibrium analysis of the BLI data provide an independent measure of the KD of ∼4 nM , consistent with the kinetic analysis . The biosensor binding data are in overall good agreement with the peptide FP KD estimate , suggesting that the N-terminus of gp42 does provide the majority of the binding energy between gp42 and gHgL . To facilitate the measurement of gp42 binding to HLA-DQ2 in the presence and absence of gHgL , biotinylated HLA-DQ2 loaded with the CLIP1 peptide was immobilized onto SA biosensor tips . Binding of the wt gp42 protein was investigated over the concentration range of 2–1200 nM ( Figure 2B ) . The data were fit best using a 2∶1 heterogeneous ligand binding model , providing affinity values for the primary component ( KD1 ) of 54 nM and a binding constant of 3 . 2 nM for a secondary minor component ( KD2 , Table 2 ) . The 2∶1 heterogeneous ligand model does not represent two ligands binding simultaneously to a target protein , but is a model for 2 ligand species binding with different binding constants . This may arise from heterogeneity in the gp42 or the HLA proteins . Because gp42 has a tendency to aggregate , the heterogenous ligand kinetics could reflect the interaction of monomeric vs . aggregated gp42 with HLA-DQ2 . The KD1 closely follows the overall independent KD value of 53 nM obtained upon steady state analysis of the same data ( Table 2 ) , confirming the KD obtained through kinetic analysis . Complexes of gHgL/gp42 were freshly prepared and isolated by gel filtration chromatography for comparative binding studies with biotinylated HLA-DQ2 ( Figure 2C ) . The concentration of the gHgL/gp42 complexes was varied over the range between 1–800 nM by serial dilution . Global curve fitting of the binding data with a variety of models was attempted and a 2∶1 heterogeneous ligand model gave the best fit ( Figure 2C ) , yielding a KD1 value of 118 nM ( Table 2 ) . The overall equilibrium affinity could be independently estimated from the saturation of the binding curves , providing a KD value of ∼120 nM for the formation of the ternary complexes , which is in good agreement with the kinetic KD1 ( Figure 2D and Table 2 ) . Attempts to quantitatively measure gHgL binding to preformed gp42/HLA complexes using the biosensor were confounded by the weaker affinity and faster dissociation rate of gp42/HLA compared to gp42/gHgL complexes . Overall , these data indicate that the binding affinity of gHgL/gp42 with HLA-DQ2 is within 2-fold of gp42 alone , with a free energy difference of <0 . 5 kcal/mol , suggesting that there are no major energetic interactions coupling the binding of these two ligands to gp42 . The results indicate that HLA receptor binding does not induce major conformational changes in the gHgL/gp42 complex that require receptor binding energy , and that gp42 binds its two ligands essentially independently . Previous studies have shown that the gp42 hydrophobic pocket undergoes a small change in structure upon HLA binding [27] , [28] , consistent with the conclusions of these binding studies . Purified gHgL/gp42/HLA-DQ2 complexes were examined by negative-stain EM ( Figure 3A ) . The images revealed that the sample was composed of two major types of particles: gHgL/gp42/HLA-DQ2 and gHgL/gp42 and only 5–10% of total particles represented intact gHgL/gp42/HLA-DQ2 . The dissociation of HLA-DQ2 might result from the low sample concentration required for the negative-stain EM corresponding to ∼50 nM , which is close to the measured KD . To reconstruct the 3D structures of both gHgL complexes , homogeneous particles were selected by image classification and the 3D reconstructions were carried out using the random conical tilt ( RCT ) method [43] implemented in the SPIDER software [44] . Class averages revealed that the gHgL/gp42 complexes adopted different arrangements of the gp42 CTLD relative to gHgL ( Figure 3B ) . The CTLD appears to be able to migrate along the length of the gHgL rod-like structure , while remaining in relatively close proximity to the gHgL surface . These observations are consistent with gp42 binding with high affinity through a flexible peptide segment located within its N-terminal 86 residues , followed by a short flexible linker separating the N-terminal domain and the CTLD [23] , [27] , [29] . The EM observations further indicate that the gp42 CTLD does not interact strongly with gHgL . Analysis of the gHgL/gp42/HLA-DQ2 complexes shows that these exist in at least two states that are approximately equally populated ( Figure 3C ) . Nearly 50% of the complexes adopt a “closed” conformation in which gHgL and HLA-DQ2 molecules appear to be more closely aligned , whereas the other 50% adopt a more heterogenous “open” state ( Figure 3C ) . For the open state , the two arms of the ternary complex vary in orientation with angles of ∼40°–115° relative to each other . Representative 3D EM maps of the ternary and gHgL/gp42 complexes were obtained from particle classes with estimated resolutions of 29 Å ( “closed” state ) , 36 Å ( “open” state ) , and 36 Å ( gHgL/gp42 complex ) ( Figure 4 ) . The successful reconstruction of the ternary complex indicates that the gHgL/gp42/HLA-DQ2 complex adopts a relatively homogeneous architecture , particularly in the closed conformational state . Although the “open” gHgL/gp42/HLA-DQ2 and gHgL/gp42 complexes are more variable , representative 3D reconstructions for these complexes were also generated ( Figure 4B , C ) . The EM maps were used for fitting the known component crystal structures of gHgL ( PDB ID: 3PHF ) and the gp42/HLA-DR1 complex ( PDB ID: 1KG0 ) . The structures fit unambiguously in the maps ( Figure 4A–C ) , positioning gp42 near to gHgL , with HLA-DQ2 projecting away from the viral protein complex in both the open and closed conformations . In the closed conformation , gHgL and gp42/HLA-DQ2 are in an approximately parallel arrangement with HLA-DQ2 in proximity to gH D-IV ( Figure 4A ) . In the “open” conformation , gp42 is rotated by about 30° , leading to gp42/HLA-DQ2 tilting towards a more perpendicular orientation relative to gHgL ( Figure 4B ) . The gHgL/gp42 complex lacking HLA-DQ2 is similar to its counterpart in the intact complex ( Figure 4C ) . The pseudo-atomic EM models place the gp42 hydrophobic pocket ( HP ) at the interface between D-II and D-III in gHgL ( Figure 5A ) . The interface involves two sides of the gp42 HP , which interact with gH residues in a loop between D-II helices ( 2α-6 and 2α-7; residues K275–E282 ) and a D-III helix ( 3α-9 helix; residues Q503–E520 ) ( Figures 5B , 5C ) . Although specific contacts cannot be conclusively identified at this resolution , gp42 residues that have been previously shown to be important for triggering membrane fusion are located at the interface . Linker insertion mutants at gp42 residues 193 , 206 and 210 , as well as the point mutation of F210A , reduce membrane fusion but do not affect binding to gHgL or HLA receptor [30] , and these are located close to the gH interface ( Figure 5B ) . The structure suggests that the well-defined architecture of the triggering complex is at least in part due to interactions between the gp42 CTLD and gHgL , which is mediated through the gp42 HP . Mutations in the gp42 HP may therefore disrupt membrane fusion activity by specifically affecting the gp42 CTLD interaction with gHgL . The composite EM model also positions the C-termini of gH and the HLA-class II ectodomains to one side of the triggering complex ( Figure 5A ) , within ∼70 Å of each other in the closed complex . This model suggests that the formation of the triggering complex could affect the approach of the viral and cellular membranes prior to membrane fusion . The gH protein transmembrane ( TM ) domain lies within ∼6 residues from the observed C-terminus of the gH ectodomain , placing the viral membrane in close proximity to the observed structure . Superposition of HLA-DQ2 onto the HLA-DR1 in our model predicts that ∼9 residues link the observed C-termini of the HLA α and β chains to their respective TM domains and the target cell membrane ( Figure 5A ) . Interestingly , the gp42 ectodomain must be cleaved from its N-terminal TM domain to be active in membrane fusion [31] , suggesting a potential geometric constraint in this assembly . No structural information is available on the location of the N-terminus of gp42 that is proximal to the membrane , as this is a flexible region of ∼60 amino acids ( residues 33–94 ) that are involved in high affinity gH binding . Although the structures incorporated into our EM model lack the gH and HLA TM and cytoplasmic domains , there is no evidence that these regions would affect the overall ectodomain conformations . Residues in a loop ( the ‘158’ loop ) at the junction of the gp42 HP and the HLA binding site have been implicated in mediating conformational changes to the gp42 HP after receptor binding [28] . Previous mutagenesis studies demonstrated that mutations in the 158 loop could affect both HLA binding and membrane fusion , but mutations at the tip of this loop were not tested to see if these showed specific defects in membrane fusion but not HLA binding [27] , [30] . In the EM model of the complex , residue I159 , which resides at the tip of the 158 loop , is oriented to interact with the gH D-III helices near residues 492 and 507 ( Figure 5B , C ) . We mutated I159 to cysteine to test its effects on membrane fusion and to also allow additional labeling of gp42 at this site . Since wt gp42 has an unpaired cysteine at residue 114 , this was also mutated to serine to avoid secondary labeling outside of the 159 position . Both the C114S single mutant and C114S/I159C double mutant ( referred to as I159C ) were expressed and purified to homogeneity for membrane fusion studies . The ability of the mutants to stimulate fusion with B cells was measured in a cell-cell fusion assay in which CHO cells transfected with gB , gHgL and a T7 luciferase reporter construct are mixed with Daudi B cells expressing T7 RNA polymerase . The addition of soluble gp42 to these cells triggers fusion , which is quantified by measuring luciferase activity [7] . First , we established that the free cysteine at residue 114 in wt gp42 is not important for its function . The wt gp42 protein was subjected to reduction with Tris ( 2-carboxyethyl ) phosphine , hydrochloride ( TCEP ) and alkylation with iodoacetamide ( IAA ) to potentially block the free thiol and this did not affect fusion activity ( Figure 6A ) . The gp42 C114S mutant also showed fusion levels comparable to wild type gp42 ( Figure 6A ) , further indicating that the cysteine is not a functionally important residue . We then tested the effects of the I159C mutation in the C114S background . The I159C mutant , treated with either TCEP alone , to reduce the 159C thiol , or with TCEP & IAA , to reduce and alkylate the introduced cysteine , showed a significant defect in membrane fusion activity . The reduced and alkylated I159C protein activity dropped to below 10% of wild type levels , ( Figure 6B ) . However , when the I159C mutant was only reduced with TCEP and not alkylated , it retained some residual membrane fusion activity , suggesting that covalent modification of the cysteine at residue 159 by IAA was in part responsible for reducing the mutant protein function ( Figure 6B ) . In order to demonstrate that specific modification of residue 159 was affecting gp42 function , we modified the I159C position with larger , more easily identifiable substituents by reacting the mutant protein with maleimide-PEG 2 , 000 ( mPEG2K ) and maleimide-PEG10 , 000 ( mPEG10K ) . Both pegylated I159C proteins could be purified to homogeneity by gel filtration chromatography ( Figure 6C ) . The modification could be verified by observable shifts in apparent MW in gel filtration chromatography for both the mPEG2K and mPEG10K modified proteins ( Figure 6C ) and by SDS-PAGE for the mPEG10K modified protein . Fusion assays were conducted with the gel filtration purified , PEG-modified I159C mutants ( Figure 6B ) . The pegylated I159C mutants all gave levels of membrane fusion of 5–10% , similar to the alkylated gp42 I159C protein , indicating that PEGylation disrupted fusion activity similarly to the alkylated gp42 I159C protein ( Figure 6B ) . We investigated the binding of the I159 mutant to both gHgL and HLA-DQ2 to rule out possible effects of the mutation on binding to either of these gp42 ligands . Octet biosensor binding data , obtained with both the single C114S mutant and the I159C/C114S double mutant , show nearly identical binding of the mutants to both gHgL and HLA-DQ2 proteins as compared to wt gp42 ( Figure 7 and Table 2 ) . The membrane fusion experiments demonstrate that mutation of the tip of the gp42 158 loop results in similar defects in membrane fusion as other gp42 HP mutations . However , in contrast to studies of previous HP mutants , here we demonstrate quantitatively that the I159 mutation has no significant effects on either the binding affinities or kinetics for gHgL or HLA interactions , but nonetheless membrane fusion is inhibited . These data further indicate that the gp42 HP interactions with gHgL are important in fusion activation , but do not contribute significantly to the high affinity tethering of gp42 to gHgL . We biochemically prepared triggering complexes with the IAA-treated gp42 I159C mutant ( gHgL/I159C/HLA-DQ2 ) to investigate its structure by EM , but did not observe any major differences in the structures . The IAA-treated 159C complexes also showed an essentially identical distribution between open ( 47% ) and closed ( 53% ) complexes . We also prepared triggering complexes with pegylated I159C mutant ( gHgL/I159C-mPEG-2K/HLA-DQ2 ) . Stable PEGylated gHgL/gp42/HLA-DQ2 complexes could be isolated by gel filtration chromatography ( Table 1 ) and these resulting complexes were examined by negative-stain EM ( Figure 7E ) . Although the PEGylated gp42 complexes appear to be structurally similar to the wt triggering complex , the distribution of particles in the open and closed conformations shifts to ∼78% open and 22% closed from the ∼50∶50 distribution observed with wt gp42 , suggesting that disrupting the closed conformational state of the triggering complex could be functionally important . Based on the EM model , we generated mutations in gH at the predicted interface with gp42 to test their effects on membrane fusion ( Figure 5C ) . Given the low resolution of the structure and the associated difficulty in accurately predicting gH-gp42 contacts , we designed mutations that introduce an NX[S/T] consensus motif predicted to generate novel N-linked glycosylated sites at the interface . The mutants studied include a gH DII mutant G276N/C278S/C335S with a nonglycosylated G276N/C278A/C335A control ( Figure 8A , B ) . Since C278 and C335 form a disulfide bond in wt gH , these two residues were mutated together , to avoid leaving a reactive free thiol at this site . Additional interface mutants in DIII included D511N/F513S , S507N/A509S , R488N/K490T , with corresponding controls that would not introduce the NX[S/T] glycosylation motif ( Figure 8A , C ) . The DIII mutants D511N/F513S and F513S mutants were not expressed well and showed no activity in fusion ( Figure 8C ) . The R488N/K490T mutant exhibited a significant reduction in B cell fusion function , but with a concomitant decrease in cell surface expression ( Figure 8C ) , whereas the control R488A point mutation expressed similar to wildtype and had only a slight effect on B cell fusion . The S507N/A509S and the A509S mutations both expressed similarly to wildtype . While the A509S mutation alone had no effect on membrane fusion , the S507N/A509S double mutant showed a reduction of ∼50% in membrane fusion with B cells ( Figure 8C ) , consistent with its position near the gp42 HP in the triggering complex structure . The introduction of a novel N-linked glycosylation site at gH residue G276 ( Figures 5C , 8B ) also reduced membrane fusion activity . The mutation of G276N/C278A/C335A does not introduce a NX[S/T] motif for glycosylation at position 276 and despite reduced cell surface expression , B cell fusion activity is slightly enhanced compared to wt gH ( Figure 8B ) . Introduction of the glycosylation site in the G276N/C278S/C335S mutant resulted in a strong reduction in B cell membrane fusion , consistent with a disruption of interactions with the gp42 HP predicted by the EM model ( Figure 8B ) . These data indicate that the 276 and 507 regions of gH are important in B cell membrane fusion , although significant disruption of fusion requires the introduction of larger perturbations than the selected point mutations tested here . Overall , the data support the conclusion that both sides of the predicted gp42-gHgL interface are functionally important for B cell membrane fusion activity .
It has been proposed that the herpesvirus gHgL protein might play various roles in membrane fusion , potentially participating directly in the mechanics of membrane fusion [45] or alternatively by acting purely as a regulator of gB activation and leaving the fusion process entirely to the fusion protein gB [4] , [32] The process by which receptor binding by gHgL or gHgL/gp42 leads to gB activation may involve gHgL conformational changes , but it has remained largely unclear . Here we studied the assembly and structure of the EBV B cell triggering complex , demonstrating that there is no significant energetic coupling between the binding of the two gp42 ligands , gHgL and HLA , suggesting that receptor binding energy is not required to initiate large conformational changes internally in the complex . We also determined the negative-stain EM structure of the triggering complex , observing that it adopts a well-defined closed state and a variable open conformation . Docking of the crystal structures of the component gHgL , gp42 and HLA proteins into the EM map provided a pseudo-atomic model of the complex . In addition to indicating that the B cell triggering complex forms well defined arrangements of the proteins , the EM models reveal three additional unanticipated observations . First , the gp42 CTLD forms direct contacts with gHgL at the junctions of D-II/D-III ( Figures 4A , 5A ) . Second , the gp42 HP , a region that is critical for fusion activation , is positioned at the interface with gHgL ( Figure 5B , C ) . Third , the C-terminal ends of gHgL and HLA-DQ2 are oriented to one side of this complex , suggesting that the assembly of the triggering complex may bring the viral and cellular membranes into proximity ( Figures 4A , 5A , 9 ) . We demonstrated that mutations of residues in both gp42 and gH at the gp42 HP-gH interface reduce membrane fusion with B cells ( Figures 6B , 8B , 8C ) , further validating the EM model . Our model of the B cell triggering complex answers several important questions that arose from previous structural and functional studies [4] . The gp42 HP lies above the gp42 HLA class II binding site and is required for B cell fusion . Mutations in the pocket abrogate fusion [30] , but not HLA or gHgL binding . Importantly we demonstrated here that mutation of gp42 residue I159 , implicated by the EM structure as interacting with gHgL , also blocked membrane fusion , but did not alter the binding kinetics or affinity of gp42 for gHgL or HLA-DQ2 . It had previously been postulated that gB or gHgL might interact with the gp42 HP [28] , but our EM model and functional studies clarify that gH is the HP ligand . We demonstrated that IAA- and PEG2K-modified gp42 159C proteins are both defective in activating membrane fusion , but EM images of the triggering complexes appear generally similar to the wt complexes . Interestingly , the PEG2K modified gp42 mutant did reveal a change in the percentage of complexes in the closed conformation , suggesting that alterations in the stability of this conformation could be responsible for the membrane fusion defect . However , given that the IAA-treated I159C complexes appear similar to wt , it may not be possible to discern small but critically important conformational changes in the low-resolution EM complexes that are responsible for the loss of function . It is also possible that changes in the dynamic stability of the complexes governed by the gp42-HP:gHgL interaction may not be evident in the distribution of closed and open states in the IAA-treated mutant , but this could potentially explain a reduction in fusion efficiency . The EM structure of the closed triggering complex reveals that the C-termini of membrane anchored gH and HLA-DQ2 would be positioned within 70 Å of each other and located to one side of the complex at the bilayer-bilayer interface during membrane fusion ( Figure 9 ) . C-terminal residues of gH [35] and the membrane anchor [46] itself have been implicated as important in fusion , although contradictory results have suggested that soluble gHgL could either activate or inhibit membrane fusion comparing HSV and EBV [46] , [47] . Studies using cryo-electron tomography ( cryo-ET ) of HSV-1 fusion in two model systems , adherent cells and synaptosomes , show distinct V/Y shaped structures ( ∼15 nm ) bridging the viral and cellular membrane prior to membrane fusion [48] , which were interpreted as representing gHgL and/or gB proteins . The vitreous ice frozen structures reveal dimpling of the lipid bilayer , as well as smaller structures near the site of membrane pinching and these V/Y shaped structures . This study also indicated that only a few glycoprotein complexes might be involved in organizing the viral and cellular membranes near the site of fusion [48] . The gHgL/gp42/HLA pseudo-atomic model that we have determined shares this overall V/Y shape . The predicted intermembrane distances of ∼10 nm for the closed gHgL/gp42/HLA complex is also similar to the bridging structures observed in the HSV studies , suggesting that these may represent a common structural feature formed at initial , prefusion stages of herpesvirus entry . Our mutagenesis studies of the gp42 HP and gH contact sites further indicate that this bridging structure plays a critical role in promoting membrane fusion events . The orientation of the EBV triggering complex relative to the viral and cellular membranes is unknown . However , comparison to the V/Y structures observed in the HSV-1 cryo-ET study suggests that the C-termini of the gH and HLA ectodomains might be oriented towards the central site of closest approach of the viral and cellular bilayers ( Figure 9 ) . The open and closed conformations of the triggering complex observed in the EM studies suggest that assembly of the complex , and specifically formation of the closed conformation , could play a role in drawing the viral and cellular membranes into closer proximity . The model of the triggering complex positions gHgL D-I on the opposite side of the structure from the TM domains anchored in the viral and cellular membranes and binding HLA receptor could potentially position D-I closer to the viral membrane surface ( Figure 9 ) . Two gL residues ( Q54 and K94 ) located at the tip of D-I are critical for the specificity of gB activation [33] . This placement of D-I away from the gH and HLA TM domains may enable the recruitment of gB to the triggering complex through the oriented gHgL D-I site . Once gB is activated , it could then potentially move from an initially peripheral location to a more central region of the virus∶cell interface to directly mediate fusion between the two membrane bilayers . Based on these observations , we propose a model for EBV entry into B cells , in which the triggering complex plays two potential roles , by bringing the viral and cell membranes into closer proximity , potentially through an open-closed conformational transition , and by orienting gHgL to enable the activation of gB-mediated fusion ( Figure 9 ) . The well-defined architecture of the gHgL/gp42/HLA assembly could induce dimpling or distortions of the viral and cellular membrane bilayers to accommodate the complex . The short linker of gH to the viral membrane may explain why gH D-IV residues have been shown to be important in membrane fusion [35] , [36] , as they could potentially play a role in deforming the viral bilayer . The orientation of gHgL complexes relative to the two bilayers may enable gB recruitment and activation . Finally , the merger of the viral and host membranes may relieve membrane distortions induced by gHgL/gp42/HLA complexes , allowing the transmembrane domains to relocate into the same final bilayer structure ( inset , Figure 9 ) . By contrast , studies with epithelial cells have indicated that soluble integrin receptor binding to gHgL may be sufficient to trigger membrane fusion [26] , suggesting potential differences in the energetics or process for activating gB . Further comparative studies of EBV epithelial cell triggering complexes and energetics are required to understand how infection of these two cell types is orchestrated by EBV fusion glycoproteins .
Detailed instructions for the expression and purification of EBV gHgL and gp42 have been previously published [12] . Baculovirus vector expression system ( BD Biosciences ) was used for making the soluble constructs of EBV gH , gL and gp42 . EBV gH residues 18–679 and gL residues 24–137 were fused to a gp64 leader sequence for secretion with the resultant addition of three N-terminal residues AMT for gH and AMD for gL each under the expression control of the p10 and polyhedron promoters respectively . The gHgL protein expressed in this study had no purification tags . Gp42 residues 33–223 were cloned into the pBacGus-3 vector , with the gp64 signal sequence and N-terminal 6-His and S tags as previously described [12] . ESF921 media ( Expression systems ) and HyQ media ( Hyclone ) were used for growing Sf9 or High Five insect cells ( Invitrogen ) and SF+ cells ( Protein Science , Meriden , CT ) respectively . Sf9 or High Five cells were maintained at 1 million cells/ml and SF+ at 1 . 5 million cells/ml every 48 hrs at 27°C incubator shaking at 135 rpm . Sf9 cells in monolayer were used for serial passage of baculovirus stock production in T-flasks with complete TNM-FH media ( BD Biosciences ) . SF+ cells were used for gHgL expression and High Five cells for gp42 expression with corresponding 2% v/v P3 ( fourth generation ) baculovirus stock to cells at 1 . 5–1 . 8 million/ml at 27°C shaking at 135 rpm . Typically ∼4 L of SF+ cells expressing gHgL and 1–2 L of High Five cells expressing gp42 were processed . The expressed secreted protein was harvested three days ( 72 hrs ) post-infection . The secreted proteins were isolated by affinity chromatography . E1D1 antibody coupled to an Ultralink hydrazide resin column ( Pierce , Thermo Scientific ) was used for purifying untagged gHgL , and metal-affinity resin ( Talon , Clontech or Ni-NTA resin , Qiagen ) used for purifying six His-tagged gp42 wildtype and the gp42 mutants ( C114S single and I159C double mutant ) . EBV gHgL was eluted with 0 . 1 M glycine pH 2 . 5 and neutralized immediately with 1 M Tris pH 8 stock and sodium chloride to final concentration 150 mM . Alternatively , EBV gHgL was also eluted with gentle Ag/Ab elution buffer pH 6 . 6 ( Pierce , Thermo Scientific ) . Gp42 was eluted with 20 mM Tris , 150 mM NaCl , 300 mM Imidazole , pH 7 . 4 . Both gHgL and gp42 proteins were buffer exchanged to 20 mM Tris , 150 mM NaCl , pH 7 . 4 . E1D1 hybridoma was generously provided by L . Hutt-Fletcher ( Louisiana State University Health Sciences Center , Shreveport , LA ) . The hybridoma was expanded to get soluble E1D1 protein supernatant from the National Cell Culture Center ( NCCC , Minneapolis , MN ) . The gHgL and gp42 proteins were polished using a Superdex 200 10/300 GL column with 20 mM Tris , 150 mM NaCl , pH 7 . 4 as the final buffer . The construct details for HLA-DQ2 have been previously described [49] . Briefly , our present study uses the A1*0501/B1*0201 HLA-DQ2 allele with a Fos-Jun leucine zipper pair that replaced the transmembrane regions and stabilize the αβ dimer , along with deamidated α1 gliadin peptide ( QLQPFPQPELPY ) or CLIP1 ( PVSKMRMATPLLMQA ) peptide covalently linked to the N-terminal end of the β-chain by a thrombin cleavable , 15-aa linker . The C-terminal leucine zipper also could be cleaved post-expression using 3C protease and this was done for the protein used in EM analysis . The C-terminus of the β-chain following the zipper includes the eight amino acid FLAG tag sequence ( DYKDDDDK ) used as the purification tag , followed by a BirA enzyme site for site-directed biotinylation . This site was enzymatically biotinylated in vitro with Biotin-protein Ligase ( Avidity LLC , Aurora , CO ) to enable kinetic studies with streptavidin ( SA ) biosensor tips . Stably transfected S2 insect cells ( from Drosophila melanogaster ) were used for the production of aforementioned FLAG-tagged HLA-DQ2 protein with CLIP1 ( endogenous ) or α1 gliadin ( exogenous ) peptides . S2 cells were maintained between 4–10 million cells/ml using complete Schneider's media ( Invitrogen Gibco 21720 ) supplemented with 5% FBS ( Atlanta Biologicals S12450 , after heat-inactivation ) and 2 mM final glutamine concentration ( Gibco 25030 , 200 mM stock ) . Cells were amplified by gradually diluting the FBS out into the final Baculogold Max-XP serum-free insect cell media ( BD Pharmingen 551411 ) with added 2 mM glutamine . Cells were grown by shaking at 120 rpm in a 27°C incubator and induced for HLA-DQ2 expression with final concentration of 1 mM copper sulfate at a cell density of 6 million/ml . Media supernatants were passed through ANTI-FLAG M2 affinity gel resin ( A2220 , Sigma-Aldrich , St . Louis , MO ) and eluted by competing with free FLAG ( DYKDDDDK ) peptide ( F3290 , Sigma-Aldrich , St . Louis , MO or RP10586-1 , Genscript USA Inc . Piscataway , NJ ) . The eluted HLA-DQ2 protein was concentrated and passed through Superdex 200 ( GE Healthcare Life Sciences ) as the final purification step . Label-free binding interaction analyses between gp42 , gp42 C114S single mutant or gp42 I159C double mutant and soluble HLA-DQ2 ( CLIP1 ) in the presence or absence of gHgL were performed on the Octet RED96 ( ForteBio , Pall corporation ) . All interactions studies were performed with the Streptavidin ( SA ) dip-and-read biosensors ( ForteBio ) . Soluble HLA-DQ2 ( CLIP1 ) or EBV gHgL was biotinylated for the different assays . HLA-DQ2 was site-specifically labeled whereas EBV gHgL was labeled randomly through primary amine linkage with biotin at 1∶3 molar ratio using the EZ-link NHS-PEG4 biotin kit ( Thermo Scientific ) . Comparison of acquired KD estimates to previously obtained measurements from fluorescence polarization ( FP ) for gHgL binding to gp42 [29] suggest that the random biotinylation does not affect the binding interaction . Kinetic analysis by global fitting of model parameters ( kon , koff , KD , kobs representing on rate , off rate , dissociation constant and observed rate respectively of complex formation between immobilized ligand and mobile analyte ) across a range of analyte concentrations simultaneously were performed using the Octet Data Analysis software package version 7 . 0 . 1 . 3 ( ForteBio , Pall Corporation ) . The 1∶1 global fit was modeled by the differential equation giving values for kobs , kon from the association phase of the sensorgram and koff from the dissociation phase using binding data from a collective set of different analyte concentrations fit globally . Additionally , for true 1∶1 binding . Further , and equilibrium response or where Rmax is the maximal response achievable by saturating the active binding site of the immobilized ligands with the mobile analyte . The 2∶1 heterogeneous ligand model with two independent binding events to two different ligand states was modeled by and . Additionally , and with and . Total response , R = R1+R2 . Equilibrium analysis was performed with the same data sets and modeled by the equation taking the average response at steady state or corresponding to a rectangular hyperbola . Purified gp42 I159C was treated with 4 mM TCEP pH 7 . 0 briefly and incubated on ice for 10 minutes to reduce to the cysteine sulfhydrl ( –SH ) . This treated protein was mixed in 1∶100 w/w ratio with maleimidePEG 2000 or 10000 MW ( Nanocs Inc . ) and incubated at room temperature for one hour . The reaction was stopped by adding 50 mM iodoacetamide ( IAA ) in PBS pH 7 . 4 ( final ) to block any remaining reduced cysteine residues . Quantitative formation of new higher molecular weight species could be detected for gp42 modified on Superdex 200 elution profile ( Figure 6C and Table 2 ) and on SDS-PAGE ( data not shown ) . The PEGylated gp42 protein was used in complex formation with gHgL and HLA-DQ2 for EM studies and also in virus-free B cell fusion assays . B cell fusion assays were performed as previously described [7] . Briefly , effector CHO-K1 cells were transfected with plasmids expressing gB , gHgL and the T7 luciferase reporter gene . Twenty-four hours post transfection , the cells were detached , counted and mixed 1∶1 with target cells expressing T7 RNA polymerase ( Daudi 29 B cells ) , along with soluble wt gp42 , gp42 C114S or gp42 I159C proteins as indicated , in a 24-well plate in Ham's F-12 medium with 10% heat-inactivated FBS . Twenty-four hours later , the cells were washed once with PBS and lysed with 100 µL of passive lysis buffer ( Promega ) . Luciferase activity was quantified by transferring 20 µL of lysed cells to a 96-well plate and adding 100 µL of luciferase assay reagent ( Promega ) and luminescence was measured on a Perkin-Elmer Victor plate reader . For negative-stain EM , 2 µL sample was applied to a glow-discharged grid coated with carbon film . After 30-second incubation , the sample was blotted with filter paper and stained with 0 . 8% uranyl formate . EM micrographs were recorded on a TIETZ F415MP 16-megapixel CCD camera at 68 , 027× calibrated magnification in an FEI Tecnai F20 electron microscope operated at 200 kV . The micrographs were saved by 2× binning , yielding a pixel size of 4 . 41 Å . The image acquisition was performed with the assistance of Leginon automation software [50] , [51] . In total , 143 pairs of random conical tilt ( RCT ) images were collected for the sample of gHgL/gp42/HLA-DQ2 with the grids tilted at two angles successively ( 65° and 0° ) for each specimen area of interest . 148 , 701 pairs of particles from tilt pairs were picked using ApTiltPicker . py in Appion [52] . To avoid bias in particle picking , all possible particles were picked for the following image classification . The defocus values of 0°-tilted and 65°-tilted micrographs were calculated by CTFFIND and CTFTILT programs [53] , respectively . The phase-flipping was performed on particle images before classification and 3D reconstructions . The 0°-tilted particles were classified using the Correspondence Analysis method in SPIDER [44] . 3D RCT maps were reconstructed from 65°-tilted particles and iteratively refined with SPIDER by refinement of the center of 65°-tilted particles . Representative 3D RCT maps of the intact gHgL/gp42/HLA-DQ2 complex in the “closed” state , that in the “open” state , and the gHgL/gp42 subcomplex were reconstructed at 29 Å , 36 Å , and 36 Å resolution from 1 , 219 , 604 , and 657 particle images , respectively . These maps were used for fitting by known crystal structures of gHgL ( PDB ID: 3PHF ) and gp42/HLA-DR1 complex ( PDB ID: 1KG0 ) using UCSF Chimera [54] . For the comparison of the percentage of the “closed” and the “open” states , the samples of gHgL/gp42/HLA-DQ2 , IAA-treated gHgL/I159C/HLA-DQ2 , and gHgL/I159C-mPEG-2K/HLA-DQ2 were negatively stained using the same condition . The particles from 50 micrographs of each sample were classified using the aforementioned method . The class averages corresponding to the “closed” and the “open” states were isolated by visual inspection .
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The various steps by which lipid-enveloped viruses enter or ‘fuse’ with a host cell require the coordination of receptor recognition , viral protein activation and large protein conformational changes that can drive membrane bilayer fusion . Here we report biochemical , structural and functional experiments on the protein complex that triggers activation of the EBV fusion protein ( gB ) and entry of Epstein-Barr Virus ( EBV ) into B cells of the immune system . Three viral glycoproteins ( gH , gL and gp42 ) form a well-defined complex with host receptor ( HLA ) . We isolated the complex biochemically and studied its assembly by BLI biosensor and electron microscopy methods . Previous crystal structures revealed a hydrophobic pocket ( HP ) on the gp42 surface that when mutated disrupts fusion with B cells , but the critical binding ligand remained unknown . Our experiments show that the gp42 HP interacts with gHgL and that mutations of the predicted HP contact residues on gHgL are detrimental for fusion . Constraints imposed by the triggering complex architecture relative to its predicted membrane anchors highlight a close approach and potential deformation of both viral and host membranes affected by HLA receptor binding as a prerequisite to viral entry .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biochemistry",
"viral",
"attachment",
"medicine",
"and",
"health",
"sciences",
"viral",
"envelope",
"viral",
"entry",
"protein",
"interactions",
"pathology",
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"molecular",
"complexes",
"host-pathogen",
"interactions",
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"molecular",
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] |
2014
|
Assembly and Architecture of the EBV B Cell Entry Triggering Complex
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Zika virus ( ZIKV ) falls into two lineages: African ( ZIKVAF ) and Asian ( ZIKVAS ) . These lineages have not been tested comprehensively in parallel for disease progression using an animal model system . Here , using the established type-I interferon receptor knockout ( A129 ) mouse model , it is first demonstrated that ZIKVAF causes lethal infection , with different kinetics of disease manifestations according to the challenge dose . Animals challenged with a low dose of 10 plaque-forming units ( pfu ) developed more neurological symptoms than those challenged with 5-log higher doses . By contrast , animals challenged with ZIKVAS displayed no clinical signs or mortality , even at doses of 106 pfu . However , viral RNA was detected in the tissues of animals infected with ZIKV strains from both lineages and similar histological changes were observed . The present study highlights strain specific virulence differences between the African and Asian lineages in a ZIKV mouse model .
Zika virus ( ZIKV ) is a flavivirus which was first isolated from a sentinel rhesus macaque placed in the Zika forest in Uganda in 1947 [1] and later from African mosquitoes collected in the same forest in the early 1960’s [2] . The virus remained a local curiosity of the East African Virus Research Institute , Entebbe , being noted for its febrile , but mild and unproblematic self-limiting symptoms in humans [3] , for several years . Subsequent studies went on to show evidence of its wide circulation , notably without serious symptoms , in several African and Asian countries during the 1960s to 1980s [4–7] . However , in 2007 an outbreak on Yap Island , Micronesia , in the Pacific ocean , changed the ZIKV landscape with the first reports of infection outside Africa and Asia [8] . No further transmission was identified until 2013 when French Polynesia reported autochthonous cases [9] and a large outbreak [10] . The virus continued to spread rapidly throughout the Pacific region [11] before being detected in Brazil , from where it spread to other countries across South America [12 , 13] . With this spread into new territories came newly identified pathological changes attributed to ZIKV infection , including microcephaly [14 , 15] ( now recently recognised as congenital Zika syndrome ) and Guillain-Barre syndrome [16] . This increase in disease severity caused the World Health Organisation to declare ZIKV a Public Health Emergency of International Concern ( PHEIC ) in February , 2016 [17 , 18] which was subsequently removed in November 2016 . While several reports demonstrate sexual transmission of ZIKV [19] and blood/platelet transfusion [20] , the main route of infection is via mosquito bites . Ideally , in vivo models should be developed which closely mirror natural infection . Subcutaneous inoculation is a common method used for studying mosquito-transmitted pathogens as it mimics a natural route of infection , including local replication at the inoculation site . Whilst the tropism of ZIKV is not yet fully understood , it is likely that keratinocytes and dendritic cells in the skin represent early targets of infection [21] , as occurs for other flaviviruses such as Dengue 1–4 viruses [22 , 23] and West Nile virus [24] . Although NHP models for ZIKV are available , small animal models are valuable for the initial assessment of safety , immunogenicity , and protective efficacy of candidate vaccines prior to testing in NHPs and subsequent human clinical trials [25] . Small animal models for ZIKV infection have focused on mice with deficiencies in their IFN response , since the virus has been demonstrated to target human STAT2 proteins to suppress IFN signalling , but not mouse STAT2 [26] . Lethal models have been developed using mice with deficiencies in their type-I interferon receptor on a 129Sv/Ev background ( A129 ) [27 , 28] and with other parental background strains ( Ifnar1-/- ) [29–31] . To develop a wild-type ( WT ) mouse model of ZIKV infection , antibody treatment to block type-I IFN signalling has been used to replicate the phenotype of the A129/Ifnar1-/- mice . After challenge with an Asian strain ( H/PF/2013 ) of ZIKV , higher viral loads were observed in WT mice pre-treated with the antibody , but there was no lethality or loss in weight [30] . This mouse model has also been challenged with a mouse-adapted African strain ( Dakar ) of ZIKV with virus induced lethality being observed from days 10 to 15 post-challenge in some , but not all , of the control treated animals . This model was also used to assess the efficacy of monoclonal antibody therapy after subcutaneous challenge with 103 FFU ZIKV ( Dakar ) [32] . In a different study to assess ZIKV-induced damage to the testis , however , the same model infected with a 3 log higher dose of ZIKV ( Dakar ) reported no lethality [29] . Thus , while the WT mouse model has been useful it also appears to give inconsistent results with certain strains of ZIKV . Additionally , while virus adaptation to the mouse by serial passage of ZIKV was used in 1952 to develop the original murine model [33] , the approach has the potential to alter virulence and antigenicity of the virus , therefore compromising any model developed from it [25] . Since animal models need to be consistent and reproducible between laboratories , with the minimum of changes needed to replicate natural disease , the A129 mouse in conjunction with natural strains remains a valuable model for the study of ZIKV infection . ZIKV is phylogenetically divided into two lineages: African and Asian [34 , 35] . Differences in pathogenicity between ZIKV’s of the Africa ( ZIKVAF ) and Asia ( ZIKVAS ) lineages have not been reported in A129 mice . To this end , we have conducted a series of experiments to investigate the different disease outcomes and pathological changes in A129 mice challenged with ZIKVAF and ZIKVAS via the subcutaneous route , to mimic mosquito-bite infection .
Whilst it has been demonstrated that A129 mice are susceptible to a 106 plaque-forming unit ( pfu ) subcutaneous dose of ZIKVAF infection [27] , their susceptibility to lower challenge doses by this route is not known . A dose reduction study was conducted with challenge doses ranging from 106–10 pfu . Virus challenge was delivered subcutaneously in order to mimic natural infection via mosquito bite [36] , and included the range of 104–106 pfu which has been implicated for infection with West Nile virus , another mosquito-borne flavivirus [37] . All ZIKVAF-challenged mice lost weight , succumbed to infection and met humane clinical endpoints within 8 days ( Fig 1 ) . Clinical signs in the mice were recorded at least twice a day and given a numerical value according to severity . Both weight loss and lethality were dose dependent , with animals receiving the lower doses surviving longer and losing weight at later time points . Mice challenged with higher doses of ZIKVAF survived for less time and developed fewer clinical signs than those receiving lower concentrations ( Fig 1C ) . As a result of the increased length of the disease progression in mice challenged with 10 pfu ZIKVAF , clinical disease in these animals appeared more severe with neurological signs observed in several animals . To ascertain the differences between the two lineages of ZIKV , A129 mice were challenged with high and low doses ( 106 and 10 pfu , respectively ) of each strain . All animals challenged with ZIKVAF met humane endpoints , whereas those challenged with ZIKVAS survived the 14 days of the study ( Fig 2A ) . Weight loss in the ZIKVAF-challenged group was observed , whereas those which received ZIKVAS neither lost nor gained weight compared to unchallenged controls ( Fig 2B ) . Animals which received the highest dose of ZIKVAF demonstrated profound decreases in temperature prior to meeting humane endpoints ( Fig 2C ) . Similarly , only those animals challenged with ZIKVAF had substantial clinical signs ( Fig 2D ) . To follow up the clinical observations at days 1 , 3 , 5 , 7 and 14 post-challenge , a cohort of mice were culled and viral RNA levels were determined at local sites ( Fig 2E ) . In the spleen and liver , similar viral RNA levels were seen between the dose-matched groups . In the brain , both ZIKVAF-challenged groups showed viral loads detectable from day 1 , yet for the low dose ZIKVAS animals , viral RNA was only detectable at day 5 . The viral RNA levels in the brains of ZIKVAF-challenged groups were consistently higher than those in the brains of ZIKVAS-challenged groups . Evidence of viral RNA in the kidney and lung were observed with both lineages , although in both tissues , animals challenged with only the low dose having detectable concentrations 3 days post-challenge . In the testis , similar levels were observed between the two strains . The levels in the ZIKVAS-challenged group increased continually over the 14 day study period . In the heart and blood , similar kinetics were observed between the ZIKV strains with the levels peaking on days 3 and 5 , respectively , and then decreasing at later time points . These results demonstrate that both strains of ZIKV caused infection in the mice with evidence of systemic virus spread , most likely haematogenously . To monitor for virus shedding , saliva and rectal swabs were collected and viral RNA levels were assessed ( Fig 2F ) . Viral RNA was detectable in the saliva in all groups at day 5 , but at earlier time points only in animals challenged with the high dose inoculum . Observations with the rectal swabs were similar , although viral RNA was only observed on day 3 in the high dose ZIKVAS group . Viral RNA did not appear in the other groups until day 5 . Whilst the level of viral RNA in the secreted components was lower than those detected at the local sites , the data provide evidence that ZIKV is present in secretions . Brain lesions consistent with ZIKV infection were observed , variably , in animals from all challenged groups ( Table 1 ) . These comprised ( i ) nuclear fragmentation scattered diffusely within the grey and white matter ( Fig 3A ) ; ( ii ) perivascular inflammatory cell cuffing , mainly mononuclear cells ( Fig 3B ) ; ( iii ) widely distributed , scattered , occasional occurrence of polymorphonuclear leukocytes ( PMNs ) in the neuropil ( Fig 3C ) and perivascular location; ( iv ) the presence of scattered , partially degenerated cells in the neuron layer of the hippocampus ( Ammon’s horn ) , comprising hyper-eosinophilic cytoplasms and irregularly shaped , partially condensed nuclei ( Fig 3D ) ; and ( v ) patchy meningeal infiltration by mainly mononuclear inflammatory cells ( Fig 3E ) . Histological lesions were first observed in the ZIKVAF groups on day 5 ( high dose ) and day 7 ( low dose ) , ranging in severity from mild to moderate . By contrast , histological changes were not seen until 7 days post-challenge in the high dose ZIKVAS infection group , and remained present at the day 14 endpoint of the study . Minimal changes only were seen at the day 14 time point in animals which received a low dose of ZIKVAS . In addition , samples were stained for the presence of ZIKV RNA within the brain tissue ( Table 2 ) . Viral RNA was initially detected at day 3 post-challenge in animals infected with both ZIKV strains ( Fig 3F ) . In the ZIKVAF groups , viral RNA staining was more prominent ( Fig 3G and 3H ) with time post-challenge; however , in the ZIKVAS-challenged animals , low levels of staining were only observed in some animals ( Fig 3I ) . In addition to changes in the brain , histological changes were also assessed in the spleen , testis , heart , liver , lung and kidney ( Tables 2 and 3 ) . In the spleen , histological changes comprised ( i ) poorly defined areas comprising large mononuclear cells within the white pulp , with numerous apoptotic bodies and scattered mitotic figures ( Fig 4A ) ; ( ii ) prominent , extra-medullary haematopoiesis ( EMH ) in the red pulp with numerous precursor cells , apoptotic bodies and scattered megakaryocytes ( Fig 4B ) ; and ( iii ) numerous , mature PMNs within the red pulp sinuses ( Fig 4B ) . The changes observed in all animals sampled at day 1 post-challenge consisted of increased EMH , considered to be a non-specific response to the virus . Histological changes more likely related to the viral infection , namely the poorly defined area comprising large mononuclear cells within the white pulp , were first detected at day 3 . By day 14 post-challenge , reduced severity of changes and viral RNA staining was observed in ZIKVAS infected animals compared to the previous time points suggesting recovery in this organ . In the testis , in a proportion of ZIKV-challenged animals , the interstitial tissue was infiltrated by macrophages and sometimes PMNs . Homogeneous , eosinophilic material , interpreted as proteinaceous fluid was also observed expanding the interstitium variably ( Fig 4C ) . In some animals , necrosis of the seminiferous tubules was noted . After challenge with ZIKVAF , changes in the testis were first recorded on day 3 , concomitant with the detection of viral RNA . Virus was evident in the interstitial tissues ( Fig 4D ) . By day 7 , viral RNA was observed multifocally within the seminiferous tubules ( Fig 4E ) . In one animal euthanised at day 7 , epididymis was present , with prominent viral staining observed in the interstitium of the testis and epididymis , and focally in the tubular epithelium and lumena of the efferent tubules ( Fig 4F ) . In the groups infected with ZIKVAS , histological changes were noted in only one animal culled on day 14 . However , viral RNA was detected from day 5 in both low and high dose challenge groups . In the low dose group viral RNA was not detected at day 14 , but in those challenged with the high dose , viral RNA staining had increased substantially to day 14 . The virus was present in necrotic seminiferous tubules ( Fig 4G ) and intra-tubular cells as well as the interstitium ( Fig 4H ) . Therefore , following both ZIKVAF and ZIKVAS infection , there was clear evidence that the virus crossed the blood/testis barrier . In the heart , histological changes were observed in several animals challenged with ZIKVAF , but minimal effects were only observed after infection with ZIKVAS . These comprised macrophages and PMNs infiltrating the myocardium ( Fig 4I ) , occasionally associated with cardiomyocyte degeneration and/or nuclear debris . In addition , infiltration of the atrio-ventricular valves and connective tissue surrounding the epicardium , by similar inflammatory cells was observed ( Fig 4J ) . Viral staining was noted after challenge with both ZIKV strains from day 7 , but by day 14 , staining was present only in one of the animals that had been challenged with a high dose ZIKVAS . Changes considered to be directly attributable to ZIKV infection were not detected in the liver and lung; nevertheless viral RNA was detected in these organs . In the kidney , where histological changes were not detected , ZIKV RNA was found within the cortical and medullary interstitium . The observation that a contemporary strain of ZIKVAS ( PRVABC59 ) did not cause clinical disease in A129 mice , led us to test another strain from the same lineage . For this work , we used a strain ( ZIKVAS-PHE ) recently isolated from a returning UK traveller who had visited Guadeloupe [38] . Results from challenged A129 mice confirmed the previous finding with ZIKVAS; neither isolate caused lethality ( Fig 5A ) . Weight differences and temperatures were also similar between animals treated with the two ZIKVAS isolates ( Fig 5B and 5C , respectively ) , although with both strains there was a rapid weight loss of ≈5% over 2–3 days before weight stabilisation . Clinical signs were not observed in either of the challenged groups . At the end of the study , sera from culled animals were assessed for antibody levels to confirm seroreactivity . All of the ZIKVAS-challenged animals had detectable antibody responses ( Fig 6 ) . Histological lesions and in situ detection of viral RNA was conducted in the brain , testis and heart ( Table 4 ) . Microscopic changes referable to infection by ZIKV were observed in the brain and testis of a proportion of animals in both groups . Only minimal microscopic changes were observed in the heart of a single animal . Viral RNA was also detected in the brain and testis of a proportion of animals from both groups . In the brain , changes were mainly minimal with scant staining of cells in two animals from each group . Strong viral RNA staining was noted in the testis of animals in both groups . Generally , staining patterns comprised mild staining of interstitial cells or/and strong staining of cells within the seminiferous tubules , the latter supportive of virus crossing the blood:testis barrier . In the heart , viral RNA was detected only in samples collected on day 7 post-challenge . There did not appear to be prominent difference in the prevalence and severity of changes in animals between the groups infected with the different ZIKVAS strains .
In the present study and A129 mouse model was used to compare the virulence of 2 lineages of ZIKV; African ( ZIKVAF ) and Asian ( ZIKVAS ) . Infection with ZIKVAF was lethal in A129 mice whereas infection with ZIKVAS was well tolerated . For both lineages , viral RNA and pathological changes were detected mainly within the brain , spleen and testis . Using a similar mouse model , but from a different parental background ( Ifnar1-/- ) , ZIKV-challenged animals sustained high viral loads in the brain and testes [30] . However , unlike in the A129 model , after infection with 100 focus-forming units ( FFU ) of ZIKVAS via the subcutaneous route , all Ifnar1-/- animals perished within 10 days [30] . The lethality of ZIKV in this mouse model was further confirmed using different strains of ZIKVAF and ZIKVAS [30] . This difference might be attributable to the parental mouse strains used to generate Ifnar1-/- mice , since it is known for example that susceptibilities to viruses between laboratory strains vary [39] . A further related complication of using Ifnar1-/- mice is their genetic background . Whilst initial studies of the Ifnar1-/- model were set up in Balb/c mice [40] , work with ZIKV has been undertaken in mice with C57BL/6 backgrounds [30 , 31] . The parental background of Ifnar1-/- may subsequently affect results , particularly as C57BL/6 and Balb/c are prototypical Th1- and Th2-type mouse strains , respectively [41] . The challenge route of infection is also important , as the intraperitoneal route results in a different outcome to when virus is delivered subcutaneously [42]; the latter being the preferable route to resemble the natural route of transmission via mosquito bite . Although differences in lethality were observed between the present studies and those in Ifnar-/- mice [30] , the present studies confirmed the wide distribution of viral RNA in the tissues of ZIKVAS challenged mice . The finding of pathological changes in the brain is consistent with other reports , including those dating back to the 1970s [43] . The finding of neurotropism of the virus should enable research on brain effects to be undertaken in follow-up studies using subcutaneous inoculation instead of relying on direct , intracranial inoculations as used by others [44] . Evidence of ZIKV infection in the testis of mice , after challenge , has also been reported by others [28–30] . The data in A129 mice indicate damage to the seminiferous tubules , infiltration of inflammatory cells in the interstitium and breakdown of the blood:testis barrier as observed in Ifnar1-/- mice [29] and other similar mouse models where virus has been detected in seminal fluid [45] . In the interstitium , the observations support the finding that virus is present in semen after human ZIKV infection [46] . Mice with defective IFN signalling have also been shown to be highly susceptible to infection via the vaginal route [47] . Therefore , the A129 mouse might be considered for modelling the sexual transmission route of ZIKV , in addition to looking at mosquito-borne infection routes . Whilst A129 mice do have some form of immunological deficit , they are not as immunocompromised as AG129 mice which have also been shown to be highly susceptible to ZIKV infection [48] . In the AG129 model , tissue damage to the brain was observed but there was no obvious damage to other organs examined ( including the heart , liver , spleen , kidney and lung ) [48] . In contrast , in the present studies , A129 mice additionally demonstrated extensive damage to the spleen and changes in the heart . For testing of vaccines , the A129 model has value because it retains the type-II interferon ( IFN-γ ) response , and it has been used to demonstrate protective vaccine efficacy with other arboviruses [49–51] . Additionally , unlike Ifnar1-/- mice which are not widely obtainable and require breeding in specialised animal care facilities , A129 mice are commercially available with consistent standard genetic backgrounds . The use of different lineages of ZIKV will be important in the assessment of pathogenicities of disease and efficacies of interventions . ZIKVAF was widely available at the beginning of the recent outbreak , and was widely used for initial studies [27 , 44] . However , during the WHO-declared period of ZIKV being a Public Health Emergency of International Concern ( PHEIC ) , ZIKVAS strains were also made widely available . The strains of ZIKVAS used for our studies included PRVABC59 ( GenBank Accession number KU501215 ) , a virus derived from the US Centres for Disease Control [52] and widely distributed to other laboratories , including as part of the Zika response by the Global Health Security Action Group ( GHSAG ) . The strain has been used for demonstrating vaccine efficacy in mice [53] and NHPs [54] . PRVABC59 has also been used in NHP studies demonstrating the secretion of ZIKV in saliva [55] . Given that PRVABC59 has been used across mouse and NHP models , it is a strong candidate for use as the prototype ZIKVAS strain to ensure consistency across studies and eliminate variation between strains . The concordance of results between the isolated PRVABC59 strain and one recently isolated from a patient [38] increases confidence that the A129 model is not lethal after ZIKVAS challenge . Studies in NHPs have also demonstrated similar findings between the PRVABC59 strain [55] and virus stocks isolated from the same lineage [56 , 57] . Given that the percent nucleotide identity among all the Western hemisphere ZIKV strains is >99% [52] , the findings of similar pathogenicity to two ZIKVAS strains in A129 mice is not surprising . The stark difference in lethality and severity of disease between ZIKAAF and ZIKVAS infections warrants further investigation , including the effects of virus passage history on pathogenicity . However , the due to historic ZIKVAF strains being propagated in newborn mice the alternative approach of isolating ZIKVAS in newborn mice would be required to ascertain whether early events during virus isolation affect the virus characteristics . Indeed , the implications to human infection could be valuable and help with identifying future traits that may occur if the virus is skewed towards a particular lineage . Given that these viruses are approximately 88 . 8% identical / 97% amino acid ( Table 5 ) , further insights into the molecular determinants of disease should be investigated . This should be aided by recent development in reverse genetics platforms for ZIKV [58 , 59] .
All procedures with animals were undertaken according to the United Kingdom Animals ( Scientific Procedures ) Act 1986 . These studies were approved by the ethical review process of Public Health England , Porton Down , UK , and by the Home Office , UK via an Establishment Licence ( PEL PCD 70/1707 ) and project licence ( 30/3147 ) . A set of humane end points based on clinical manifestation of disease were defined in the protocol of the project licence and are described below . Vero cells ( African green monkey kidney epithelial cells ) ( European Collection of Cell Cultures , UK ) were maintained in Dulbecco’s Modified Eagle Medium containing GlutaMAX ( Invitrogen ) and supplemented with 2% heat-inactivated foetal bovine serum ( Sigma ) at 37°C with 5% CO2 . ZIKVAF strain MP1751 ( Uganda , 1962 ) isolated by up to 3 passages in newborn mouse brain from pools of Aedes africanus mosquitoes [2] was obtained from the National Collection of Pathogenic Viruses ( NCPV ) , UK . The passage history prior to deposit with NCPV included up to four passages between 1962–1972 , by an unknown method . This was followed by one passage in Vero cells in 2011 . ZIKVAS strain PRVABC59 ( Puerto Rico , 2016 ) was obtained from the US Centres for Disease Control , and had been passaged 4 times in Vero cells . ZIKVAS-PHE was isolated at Public Health England [38] in C6/36 cells ( an Aedes Albopictus-derived cell line ) and made available via NCPV and European Virus Archive goes Global ( EVAg ) collections . ZIKV stocks were propagated in Vero cells after inoculating at a multiplicity of infection ( pfu/ml ) of 0 . 01 and harvesting supernatant after 72 hr . Virus stocks were titrated by plaque assay on Vero cells . Foci of plaques were detected at 72 hr , following fixation with 10% formalin solution and staining with 2% crystal violet . Male mice ( aged 6–8 weeks ) with deficiencies in their type-I IFN receptor [60] were purchased from B&K Universal ( A129 ) . Mice were subcutaneously inoculated with 40 μl of virus suspension into each of the hind legs towards the ankle . Virus contained in the 80 μl inoculum volume equated to 10 , 102 , 103 , 104 , 105 or 106 pfu for the dose reduction study , and 10 or 106 pfu for the pathogenicity studies . Virus suspension was back-titrated in Vero cells to confirm dose concentration . Survival , temperature , weights and clinical signs were monitored for up to 14 days post-challenge . For clinical signs numerical scores were assigned ( 0 , normal; 2 , ruffled fur; 3 , lethargy , pinched , hunched , wasp-waisted; 5 , laboured breathing , rapid breathing , inactive , neurological; and 10 , immobile ) . Temperatures were recorded by indwelling temperature chips . Animals reaching a clinical score >10 were terminated immediately and a weight loss of 20% or 10% in combination with any clinical sign was also used to indicate a humane end-point . At days 1 , 3 , 5 and 7 post-challenge , 3 mice from each group in the pathogenicity study were culled to assess local responses . All surviving animals were culled at day 14 post-challenge . Group sizes are stated in the relevant figure legends and the data representative of a single biological replicate . At necropsy , samples of spleen , liver , brain , kidney , lung , testis , heart and saliva were collected and immediately frozen at -80°C for virological analysis . Blood was collected into RNAprotect tubes ( Qiagen ) and rectal swabs were placed in 0 . 5 ml DMEM media ( Sigma ) . Tissue samples were weighed and homogenised in phosphate buffered saline ( PBS ) using ceramic beads and an automated homogeniser ( PreCellys ) . Tissue samples and biological fluids ( blood , rectal swabs and saliva ) were extracted using the RNeasy mini extraction kit ( Qiagen ) . A ZIKV specific real-time RT-PCR assay was utilised for the detected of viral RNA using a published primer set [61] . Reactions were run and analysed on the 7500 Fast platform ( Life Technologies ) . Quantification of viral load in samples was performed using a dilution series of quantified RNA oligonucleotide ( Integrated DNA Technologies ) . Viral burden was expressed as genome copies per gram or per ml . Samples of brain , spleen , liver , heart , testis , kidney and lung were fixed in 10% neutral buffered saline and processed routinely to paraffin wax . Sections were cut at 3–5 μm , stained with haematoxylin and eosin ( H&E ) and examined microscopically . Lesions referable to infection were scored subjectively using the following scale: within normal limits , minimal , moderate and marked . The pathologist was blinded to the groups in order to prevent bias . RNA ISH was performed with an RNAscope 2 . 5 ( Advanced Cell Diagnostics ) according to the manufacturer’s instructions . In brief , formalin-fixed paraffin-embedded tissue sections were deparaffinised by incubation for 60 min at 60°C . Hydrogen peroxide treatment for 10 min at room temperature quenched endogenous peroxidases . Slides were then boiled for 15 min in RNAscope Target Retrieval Reagents and incubated for 30 min in RNAscope Protease Plus before hybridisation . For probes , V-ZIKA-pp-O1-sense ( Advanced Cell Diagnostics , catalogue no . 463791 ) and V-ZIKA-pp-O2-sense ( Advanced Cell Diagnostics , catalogue no . 464541 ) were used for studies with ZIKVAF and ZIKVAS with 99% and 100% specificities , respectively . Tissues were counterstained with Gill’s haematoxylin and visualised with standard bright-field microscopy . For the brain , between 4–5 sections were examined . For the remaining tissues , 1 section of each was examined . Each slide was scanned systematically so all areas of the tissue were assessed . A commercial ELISA kit was used to assess antibody responses against ZIKV ( EI 2668–960; EuroImmun , Germany ) . Manufacturers guidelines were followed with the exception that due to the kit being developed for human samples , the detector antibody was changed to a goat anti-mouse IgM+IgG+IgA ( AP501A; Millipore , UK ) . Following completion of staining , absorbance reading were read at a wavelength of 450nm using a plate spectrophotometer . Differences in RNA levels between the groups were statistically compared using Minitab ( version 16 . 2 . 2 ) . Due to the small group sizes ( n = 3/group ) and data not being normally-distributed , the nonparametric Mann-Whitney statistical test was used . Statistical significance was where P = 0 . 0801 ( the lowest P-value obtainable using the conditions of n = 3/group ) .
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Since first being recognised in 1947 , Zika virus ( ZIKV ) has mainly been associated with a mild illness with symptoms including a limited fever and rash . In 2007 the virus spread from Africa and Asia into Micronesia , then in 2013 into French Polynesia and then onwards across Pacific regions and into South America . In these new regions , ZIKV has been associated with more severe clinical conditions including Gullain-Barre syndrome ( GBS ) and congenital Zika syndrome . Using a mouse strain with a deficiency in the type-I interferon receptor ( A129 ) , after challenge with ZIKV using a route that resembles the natural route of infection via mosquito bite we compared the two major lineages of ZIKV: African ( ZIKAAF ) and Asian ( ZIKVAS ) . Whilst it was known that ZIKVAF causes a lethal disease in A129 mice , we observed a non-lethal infection with ZIKVAS . To confirm the finding , a recent isolate of ZIKVAS was additionally assessed and demonstrated the same observations . Our studies provide new insights into the mechanisms of ZIKV infection in a small animal model; and may help to elucidate the different pathologies caused by this virus .
|
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2017
|
Lineage-dependent differences in the disease progression of Zika virus infection in type-I interferon receptor knockout (A129) mice
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Our overall hypothesis is that host population immunity directed at multiple antigens will influence the prevalence , diversity and evolution of influenza A virus ( IAV ) in avian populations where the vast subtype diversity is maintained . To investigate how initial infection influences the outcome of later infections with homologous or heterologous IAV subtypes and how viruses interact through host immune responses , we carried out experimental infections in mallard ducks ( Anas platyrhynchos ) . Mallards were pre-challenged with an H3N8 low-pathogenic IAV and were divided into six groups . At five weeks post H3N8 inoculation , each group was challenged with a different IAV subtype ( H4N5 , H10N7 , H6N2 , H12N5 ) or the same H3N8 . Two additional pre-challenged groups were inoculated with the homologous H3N8 virus at weeks 11 and 15 after pre-challenge to evaluate the duration of protection . The results showed that mallards were still resistant to re-infection after 15 weeks . There was a significant reduction in shedding for all pre-challenged groups compared to controls and the outcome of the heterologous challenges varied according to hemagglutinin ( HA ) phylogenetic relatedness between the viruses used . There was a boost in the H3 antibody titer after re-infection with H4N5 , which is consistent with original antigenic sin or antigenic seniority and suggest a putative strategy of virus evasion . These results imply competition between related subtypes that could regulate IAV subtype population dynamics in nature . Collectively , we provide new insights into within-host IAV complex interactions as drivers of IAV antigenic diversity that could allow the circulation of multiple subtypes in wild ducks .
Diversification is a common feature in pathogen populations and this often involves the evolution of antigenic variants [1] . Examples of this exist in various pathogen systems: viruses ( influenza A virus , Dengue virus , Bluetongue virus , and rotaviruses ) , bacteria ( Borrelia spp , Neisseria meningitis , and Pneumococcus ) and protozoan parasites ( Plasmodium spp . and trypanosomes ) . Antigenic variation within specific hemagglutinin ( HA ) and neuraminidase ( NA ) subtypes is well described with influenza A viruses ( IAVs ) as this is an important consideration in developing vaccines and vaccination strategies associated with seasonal influenza viruses in humans and IAV affecting domestic livestock and poultry . Though multiple IAV subtypes circulate in these host systems , antigenic interactions between these subtypes are equally important but less understood . Shared epitopes between different HA subtypes associated with the HA stalk have been described and these may be important target epitopes for universal IAV vaccines . Immunity to these shared epitopes also may provide partial protection in subsequent infections with heterologous IAV [2] and could potentially influence clinical outcome and regulate subtype diversity in host populations through competition [3] . IAV have the capacity to infect many different hosts , from birds to mammals; however the vast majority of influenza subtype diversity is found in wild birds , especially waterfowl , gull , and shorebird populations where low-pathogenic IAV ( LPIAV ) representing 16 HA and 9 NA subtypes are maintained [4] . In these wild bird populations , many HA/NA subtype combinations co-circulate and it is notable that their abundance and relative diversity appears to vary over time and space [5–7] . In addition , individual birds are often co-infected or sequentially infected with multiple IAV subtypes in a given season or year [8 , 9] and naturally infected mallards ( Anas platyrhynchos ) in the wild exhibit patterns of re-infections indicating heterosubtypic cross-immunity [9] . Experimental infections have demonstrated that initial infection with a specific IAV induces immune responses and protection against infection with homologous strains [10] . Additionally , initial viral infections could induce a partial protection to heterologous subtypes in experimental settings [10–13] and several studies have shown that LPIAV pre-exposure protects against a lethal highly pathogenic IAV ( HPIAV ) challenge [11 , 14 , 15] . Despite these observations little is known about the mechanisms , extent ( strength and specificity ) and persistence of these immune responses and the outcomes of re-exposures that have critical significance to understanding the maintenance of IAV antigenic diversity in multi-strain/subtype-pathogen systems such as occur with IAV and ducks . In this study , mallards were pre-challenged with a strain of H3N8 LPIAV , one of the most common subtypes in waterfowl , and pre-challenged ducks were subsequently re-challenged with the same strain ( homologous challenge ) or with different strains representing various subtypes ( heterologous challenges ) . Groups of ducks in the homologous challenge were exposed at different time intervals to evaluate long-term antibody responses and potential protection as well as to further investigate the potential influence of age [16] . The different strains used in the re-challenge were chosen to represented a gradient in the degree of phylogenetic relatedness between the HA and belonged to subtypes commonly found in waterfowl populations . The objective of the present study was to experimentally mimic re-exposures that commonly occur in mallards and other ducks in nature to determine the effects of subsequent challenge on susceptibility , duration and intensity of viral shedding and to characterize the humoral immune response .
Here , we studied the persistence of protection to homologous IAV challenge by initially infecting mallards with H3N8 and re-challenging three groups with the same virus at different time intervals . The initial H3N8 induced long-term protection against homologous re-infection for up to 15 weeks post-challenge , which is much longer than expected . None of the re-challenged birds , regardless of time interval , shed virus as determined by virus isolation and that was true for all age groups and time intervals . Although some RRT-PCR positives were detected in re-challenged individuals , the Ct-values were high and significantly higher than in control groups indicating a low number of RNA copies possibly associated with non-infective virus . The observed protection and detectable antibody response is inconsistent with results from previous studies that concluded that infection conferred no protection against re-infection and that detectable antibody responses in ducks were short-lived [19 , 20] . It was considered that antibody responses may not be detectable or protective due to the truncated structure of some of the IgY forms . A possible explanation of this inconsistency is that these truncated antibodies neutralize IAV but lack Hemagglutinin Inhibitory ( HI ) activity and the proportion between these forms vary over time being the truncated from most prevalent later in the immune response [21] . Our results are in agreement with other studies in both ducks and gulls that reported partial to complete protection against re-infection depending on the viruses , time between infections , host species and age and detection method [10 , 22 , 23] . The long-term persistence of antibodies after natural infection , artificial challenge or vaccination has been reported in captive birds up to 6 to 9 months post-exposure [24–26] though it is not know which parameters ( i . e . HI or MN titers ) are correlated to protection . When examining patterns in H3N8 infections for the different age classes in the naïve controls , all individuals were susceptible and competent to infection . The duration of infection and viral load in controls varied according to age and birds of 5 and 9 weeks of age experienced the highest replication based on AUC and duration of infection . This is consistent with earlier findings and indicative of older birds being more resistant to infection and having shorter infections [16] . This age effect could influence subsequent transmission risk and subtype diversity in cases were subtypes display seasonal patterns . Results from IAV heterologous challenge experiments of ducks , geese and gulls have reported varying levels of partial protection upon re-challenge ( 11 , 29 , 30 , 35 ) . Such protection has also been reported from field-based studies [9 , 27] and from experimental work where birds were challenged with HPIAV and effects could be measured by morbidity/mortality responses[11 , 14] . It has also been reported that the order in which IAV subtypes are used to challenge the host is important . For instance , H3 appears to be poorly immunogenic and less protective as a primary infection compared to H4 that induced a stronger protective response [13] . The variation in reported protection from previous studies may be the result of asymmetric responses associated with strain or subtype specific variation that may be dependent on the virus causing primary infections [12 , 13 , 28] . We observed a partial protection to re-challenge in the individuals subsequently infected with heterologous subtypes indicating development of heterosubtypic cross-immunity by H3N8 . When assessing the outcome of re-challenge there was a significantly lower viral load and shorter duration of infection in CL samples for all the groups pre-challenged with H3N8 compared to the naïve controls . This shorter period of detectable viral shedding is in agreement with estimates from the field [29 , 30] and was observed for all re-challenged viruses . The extent of partial protection , as measured by a decrease in duration of detectable viral shedding following re-infection was influenced by the genetic relatedness of the HA . This suggests immune mediated competition through cross-reactive responses [31 , 32] and this could be related to both acquired humoral and cell mediated mechanisms [33] . Broadly neutralizing antibodies have been defined across HA groups [34] and within HA group [35 , 36] that target conserved epitopes in the stalk [37 , 38] . HA stalk antibodies are boosted upon re-infection and it is therefore thought that cross-reactive anti-stalk as well as anti-NA antibodies [39] can diminish the severity of disease in re-infections . Studies in humans have found consistent patterns of cross-immunity within HA group with increased severity of the disease in cohorts exposed with an HA of the opposite group during childhood when studying age-specific mortality caused by 1918 H1N1 and by HPIAV H5 and H7 [40 , 41] . It has been proposed that extinction of influenza strains in humans could be driven by population immunity by HA stalk and NA antibodies [39] through competitive exclusion between strains [3] . Moreover , initial infections with a specific virus increases the probability of later infections by viruses from a different HA clade and group in wild mallards [9] . Comparable processes are likely acting in the avian IAV system where lineage replacement with strains from different continents has been reported [42 , 43] . Theory predicts that antigenic variants tend to organize as discrete non-overlapping strains [1] in populations where cross-reactivity between viruses induces competition ( i . e . case of H13 and H16 ) contrasting to the situation where cross-reactivity induces enhancement or facilitation , like Dengue , and variants may be antigenically clustered [44] . Here , after the heterologous re-challenge mallards shed viruses even if they were able to clear infections rapidly . An implication of this is the potential for selective processes like viral escape and antigenic drift to act in the same way as leaky vaccines [45 , 46] which in turn could drive antigenic evolution as observed for H5N1 HPIAV [47] or similarly for seasonal H3N2 in humans [48] . Indeed , the estimates of divergence for some HA subtype indicate that divergence is relatively recent [40] . Isolation success from RRT-PCR positive samples from secondary infections ( re-challenge ) , as expected , was correlated with Ct-value , but was significantly lower compared to isolation results from primary infections in controls . This discrepancy has implications when interpreting field data and assigning infection states based on molecular diagnostics rather than virus isolation as uncertainty needs to be incorporated [49] and may explain why isolation rates from PCR positive samples often vary . This relationship also corroborates findings from the field where samples from adults have a lower isolation success than samples from juveniles [50] and where RRT-PCR positive samples used in experimental trials have not infected ducks [51] . It is therefore prudent to be cautious when using transformations of Ct-values ( proxy for RNA copies ) to EID50/ml or TCID50/ml by a standard curve based on a virus grown at optimal conditions such as cell culture or embryonated eggs as infectivity in hosts varies according to several parameters ( age of host , previous exposure , specific virus and infection dose… ) . Antibody levels ( NP and H3 MN ) after H3N8 infection decreased over time but most individuals remained positive for the duration of the experiment ( 15 weeks ) ; all birds remained protected against homologous challenge . There was a significant boost in the antibody responses after homologous challenge for the long-term groups ( 11 and 15 weeks ) but not for the group re-infected after 5 weeks where a rapid blocking of the infection may not have activated antibody recall . Additionally , the hyperimmune sera after homologous challenge did not cross-react with other IAV subtypes , except for one individual positive by H1 at a low titer of 20 . The heterologous re-infections resulted in a boost in the NP-antibody responses in all H3 pre-challenged groups . Interestingly most of the individuals had neutralizing antibodies against the HA antigens they had been exposed to . This also includes the H12N5 that replicated poorly but resulted in serological imprinting in three of the five individuals per group . Additionally no cross-reactivity to other subtypes was observed when tested by MN with a panel of HA ( H1-H12 and H14-H15 ) . H3-specific antibodies were detected by MN after H3N8 primary infection and persisted in the majority of individuals until the end of the experiment . There were interesting H3 patterns of antibody dynamics for the H3N8 X H4N5 group that showed a boost in H3 titer ( Fig 7 ) consistent with original antigenic sin ( OAS ) ; H4 antibodies were also detected in this challenge group ( Fig 8 ) . This phenomenon of interference was first described after sequential influenza re-infections in humans [52 , 53] . Older individuals can have a broader immunity through repeated exposure with highest titers to the strains individuals were exposed to early in life . These “senior strains” , are a singularity known as antigenic seniority or imprinting [40 , 54] . Currently , we report OAS in avian hosts and between different HA subtypes ( H3/H4 and possibly for H3/H10 ) , however we expect that phenomenon could also arise between other IAV subtypes . Thus the order in which individuals are challenged with a specific virus could influence the future recognition of viruses and the specificity of the responses that ultimately shapes the outcome of later exposures in life . Since population immunity influences the emergence and spread of new strains and can influence vaccination success , it is critical to have a better understanding of these processes in different host species . We need to increase our understanding of cross-reactivity patterns and boost dynamics in re-infections to ultimately predict risks of IAV spread into different host populations in a context of non-naïve populations , for instance by using antibody landscapes [55] . The high degree of heterosubtypic immunity and subsequent competition found between common HA subtypes from ducks indicates that the transmission success and perpetuation of any subtype is dependent on the other viruses in the population and may explain the cyclic or chaotic nature in the prevalence of some subtypes . Partial immunity or complete immunity induced by pre-infection may reduce transmission potential in subsequent infections but also may promote the high degree of IAV antigenic diversity observed in wild avian populations . This competition may also result in subtype succession over time , like the predominance of H3 and H4 in fall migration [5–7] and spring blooming of other subtypes within Group 2 such as H7 or H10 [56] . With equal strength of HA immunity to all subtypes the present antigenic diversity found in wild birds would be unlikely . Surprisingly , the results from H13 and H16 experimental infections in black-headed gulls showed limited cross-immunity between subtypes and suggest independent cycles for these viruses [22] . However , for some strains or subtypes , cross-immunity may not be the only factor explaining their dynamics in the pathogen population . Pathogen fitness or success is usually measured by transmission risk that could be based on different transmission parameters such as the duration of infection and pathogen load [57] as well as how long viruses could remain infectious in the environment [58 , 59] . We believe that virus fitness linked to host-specificity and functionality of IAV proteins [60] is also playing an important role as evidenced by the fact that the H12N5 IAV used in this study did not successfully replicate in mallards even though it was isolated from that host . A possible interpretation of these results is that population immunity can reduce the probability of transmission and potential introduction success of exotic antigenic variants [61] . Previous studies have reported protection to HPIAV induced by pre-exposure to LPIAV strains in different bird species [11 , 14] . In the context of H5N8 HPIAV clade 2 . 3 . 4 . 4 or other HPIAV the present results suggest that cross-immunity could also reduce viral shedding and contribute to stopping the spread of specific virus in wild ducks that have naturally been exposed to LPIAV [62–64] . We believe that the competitive processes described here and in other studies occur in nature; however , in natural host populations the complexity of the system increases due to the extensive subtype diversity of co-circulating viruses that these birds are continuously exposed to . Based on our results we propose that the extent of competition at individual level through host immunity could be determined by many different interacting parameters: the strains involved in infections , the exposure history ( or boost responses like OAS ) and likely time between exposure/s ( assuming that immune memory decreases over time ) . These are many of the same factors that are considered in evaluating immune responses and protection against influenza in humans and domestic animals .
All LPIAV viruses used in these trials represented common North American subtypes and were originally isolated from wild mallards in Minnesota , USA [6] . Viruses included: A/Mallard/MN/AI07-4724/2007 ( H3N8 ) , A/Mallard/MN/AI11-4213/2011 ( H4N5 ) , A/Mallard/MN/AI11-4982/2011 ( H6N2 ) , A/Mallard/MN/AI11-4412/2011 ( H10N7 ) and A/Mallard/MN/AI11-3866/2011 ( H12N5 ) . Virus stocks were propagated by a second passage in specific pathogen free ( SPF ) embryonated chicken eggs ( ECE ) . Stocks were endpoint titrated using the Reed and Muench Method [65] in ECE to determine the median egg infectious dose ( EID50/0 . 1 ml ) . Ducks were inoculated with 0 . 1ml at an approximate dose/dilution of 106 . 0 EID50/0 . 1ml; the inoculum was split between intranasal and oropharyngeal routes . Previous work has shown that different routes of infection ( intranasal , intratracheal , intraocular , intracloacal , or intra-ingluvial ) result in 100% infection and similar shedding patterns [66] . Based on back titration , the titers of the inocula , expressed in EID50/ 0 . 1 ml , were 105 . 8 for H3N8 pre-challenge , 106 . 5 for H12N8 and H6N2 , 106 . 4 for H3N8 ( 5 week challenge ) , 106 . 8 for H10N7 , 106 . 0 for H3N8 ( 11 week challenge ) , 106 . 2 for H4N5 and 106 . 3 for H3N8 ( 15 week challenge ) . The HA and NA segments of the isolates were amplified [67] and later sequenced using Sanger method . Geneious ( version mac6_4_8_0_4 ) was used for sequence alignments and to calculate amino acid distances . Sequences are publicly available in GenBank ( KX814369-KX814375 ) . All procedures were in accordance with the Animal Welfare Act and US regulations . All protocols for raising , infecting , and sampling mallards were approved by the Institutional Animal Care and Use Committee at the University of Georgia ( UGA; AUP#: A2013 05-021-Y1-A1 ) . One-day-old mallards ( Murray McMurray Hatchery , Webster City , IA , USA ) were raised in captivity at the animal resources facilities of the College of Veterinary Medicine , UGA . Food and water were supplied ad libitum . All individuals were identified with bands and unique ID numbers . Individuals did not exhibit behavioral changes or overt symptoms of disease during the duration of the trial . All birds gained weight before and throughout the challenge studies . At four weeks of age , 40 ducks were moved to Biosafety Level ( BSL ) 2+ facilities and were pre-challenged with the H3N8 LPIAV . The study design included re-challenges with homologous and heterologous viruses ( S1 Table ) . Prior to the re-challenges , ducks were randomly divided into groups ( five individuals per group , approximate ratio 1:1 females: males ) and were moved into BSL 2+ poultry isolators at the Poultry Diagnostic Research Center , Athens , GA , USA that are intended to house up to five adult size mallards . After 2 days of acclimation , the re-challenge was conducted as previously described [12] . Each pre-challenged group had a control group ( i . e . age matched naïve individuals ) . Ducks were humanely euthanized at the end of all challenge trials at 14 dpi following protocols approved at the UGA . For the homologous challenge , one group of five ducks was challenged with the same H3N8 at five weeks after the initial H3N8 pre-challenge and two additional groups were re-challenged with the H3N8 virus at weeks 11 and 15 post-H3N8 inoculation . Groups in the heterologous challenges were inoculated five weeks after initial H3N8 infection with subtypes representing different levels of relatedness between the key antigenic proteins HA and NA of the H3N8 used in the pre-challenge: H4N5 , H10N7 , H6N2 and H12N5 . Swabs were collected from the cloaca ( CL ) and oropharynx , ( OP ) and were placed in separate tubes containing 2 ml BHI transport media supplemented with antimicrobials [12] . Swab samples were collected on 0–8 , 10 , 12 and 14 and 21 ( only after H3N8 pre-challenge ) days post infection ( dpi ) and kept cold until transfer to the laboratory where they were stored at -80 C° until processing . Virus isolations was performed on swabs from all birds during all challenges on 0 , 2 , 4 , 6 , 8 , 10 , 12 , 14 dpi as previously described [68] using two 9 to 11 days-old SPF ECE . To insure that all birds were IAV negative before any bird movement or subsequent challenge , all birds were tested by virus isolation on 21 dpi after the H3N8 pre-challenge and on 0 dpi prior to all subsequent viral challenges . Viral RNA from samples collected on 1 to 14 dpi were extracted using the MagMAX-96 AI/ND Viral RNA Isolation kit ( Ambion , Austin , TX , USA ) on the Thermo Electron KingFisher magnetic particle processor ( Thermo Electron Corporation , Waltham , MA , USA ) [68] . Negative ( BHI media ) and positive ( diluted isolate ) controls were included in each extraction . Molecular detection of the IAV Matrix gene by Real-time Reverse Transcriptase PCR ( RRT-PCR ) [17] was conducted with the Qiagen OneStep RT-PCR kit ( Qiagen , Valencia , CA , USA ) and the Cepheid SmartCycler System ( Cepheid , Sunnyvale , CA , USA ) [68] . Negative and positive controls were used on the extraction step and an IAV Matrix gene transcript ( National Veterinary Services Laboratory , Ames , IA , USA ) was included as positive control in the RRT-PCR . Ct–value stands for the cycle threshold when there is initial detection of the fluorescence signal at the beginning of the exponential phase of DNA duplication and is proportional to the initial number of RNA copies in a sample . Ct-values were therefore used as a proxy for viral load ( S4 Fig ) . By using the same detection protocol and stock of the Matrix gene transcript ( NVSL , Ames , IA , USA ) it was previously established that Ct-values significantly correlate with the number of Matrix RNA gene copies when diluting the Matrix gene RNA transcript to generate a standard curve [57] . Samples with cycle ( Ct ) values lower than 40 were considered positive Serum samples were collected from the right jugular vein and transferred into serum separator tubes ( Becton , Dickinson and Company , Franklin Lakes , NJ , USA ) , centrifuged upon arrival to the lab and serum was stored at -20 C° until analysis . Serum samples were taken immediately prior to H3N8 pre-challenge and prior to all subsequent challenges as well as at 14 dpi and between challenges for the homosubtypic long-term groups . Serum samples were tested with a commercially available nucleoprotein ( NP ) -ELISA kit ( bELISA; FlockChek AI MultiS-Screen antibody test kit; IDEXX Laboratories , Westbrook , ME , USA ) . Specific antibodies against the different HA subtypes used in the trial were detected using a virus microneutralization ( MN ) assay in Madin Darby Canine Kidney cells ( MDCK; ATCC , Manassas , VA , USA ) as described previously [61] . Sera were additionally tested using the same viruses used for inoculation in the challenge and with prototype strains for H1-H12 and H14-H15 [69] ( S1 Appendix ) to detect cross-reactivity . These antigens also were prepared in MDCK and tests were run using an antigen concentration of 100 TCID50 /25 μl . All analyses were run on the R software [70] using the GAMM , nlme and lme4 packages . Model selection was done using AIC [18] corrected for small sample size ( AICc ) within the package AICcmodavg . To evaluate the viral shedding or load , Ct-values from the Matrix RRT-PCR runs that are proportional to the RNA copy numbers from original samples were used . First , we analyzed the variation in Ct-values in pre-challenged and naïve controls for the different treatment groups with different time periods between infections . The strategy was to use linear models and include individuals as random effect using mixed models ( package GAMM ) due to repeated sampling of the same individuals over the course of infection . The models that were evaluated included the factors: dpi , treatment group , both factors and the interaction . Ct-values from 1 dpi were not included in the analysis as they likely represented residual inoculum rather than true virus replication ( at 1 dpi the Ct-values were close to 40 ) . Models including the random effect of individual and the additive effect of dpi were tested but the increased complexity of the models was penalized based on the AICc and some of them had convergence problems . In the same way , the variation of Ct-values from all H3N8 control groups including age as factor was assessed . For the heterologous re-challenge , the variation in Ct-values from control and pre-challenged groups for each of the different viruses was evaluated as described before . The Area Under the Curve ( AUC ) was calculated by using the Ct-values after subtracting them from the cut-off value of 40 as previously done [22] . The total duration of infection or shedding was estimated by counting the days between inoculation and last positive virus isolations in CL swabs ( which also includes cases of intermittent shedding ) . Birds that died or were euthanized before 14 dpi were not included in analysis . AUC and duration of infection between groups was compared using Krustal-Wallis test . To study the correlation between AUC and duration of infection the Pearson correlation test was performed followed by a linear regression . To explore the relation between HA similarity and the degree of protection , we estimated the amino acid distance between the H3 and the different HA of re-challenges and the relative reduction in the duration of infection within groups . The amino acid distance was then correlated with the reduction in shedding per group where the mean and SE were estimated using a bootstrapping approach . Next , to explore the influence of different variables on isolation success ( binomial response: successful or unsuccessful ) we used Generalized Linear Mixed Models ( GLMM ) as in [50] . The explanatory variables included in the models were: Ct-value , sample type ( OP and CL swabs ) , dpi and treatment ( pre-challenged or naïve individuals which means samples from a primary or secondary infection ) . Additionally , since individual birds were re-sampled and samples are not independent we added the individual as a random effect in the models . Interactions were not included to avoid convergence problems . Last , MN antibody titers ( as log2 transformed ) at a single day of sampling were compared between groups using the Krustal-Wallis test and the paired t-test was used to compare values in two different days within groups . Samples with an MN titer lower than 20 were arbitrarily given a titer of log2 ( 2 . 5 ) for the model testing . The variation in antibody titers to H3 by MN was explored based on the different sampling occasions ( dpi ) and groups also using Generalized Additive Mixed Models in the package GAMM . One duck died ( 12 dpi in group H6N2 ) and swabs were already IAV negative; necropsy showed no gross lesions caused by LPIAV . Another duck was euthanized due to difficulty walking which was associated with husbandry in captivity ( 6 dpi in group H3N8 X H6N8 ) . These birds were not included in the analysis . The H3N8 x H12N5 group was not included in the analysis because of poor replication of this virus in the pre-exposed and naïve groups . All relevant data are available as Supporting Information files ( Supplementary tables and S1 Dataset ) .
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Many features of pathogen diversification remain poorly explored although host immunity is recognized as a major driver of pathogen evolution . Influenza A viruses ( IAVs ) can infect many avian and mammalian hosts , but while few IAV subtypes circulate in human populations , subtype diversity is extensive in wild bird populations . How do these subtypes coexist in wild avian populations and do they compete within these natural host populations ? Here we experimentally challenged mallard ducks with different IAVs to study how an initial infection with H3N8 determines the outcome of later infections ( duration of infection and virus load ) and antibody responses . There was complete protection to re-infection with the same H3N8 virus based on virus isolation . In addition , there was partial protection induced by H3N8 pre-challenge to other subtypes and development of heterosubtypic immunity indicated by significantly shorter infections and reduction in viral load compared to controls . This indicates that subtype dynamics in the host population are not independent . Amongst H3N8 pre-challenged groups , the highest protection was conferred to the H4N5 subtype which was most genetically related to H3N8 . The H4N5 challenge also induced an increase in H3 antibody levels in that challenge group and evidence for original antigenic sin or antigenic seniority . Thus , previous infections with IAV can influence the outcome of subsequent challenge with different IAV subtypes . These results not only have relevance to understanding naturally occurring subtype diversity in wild avian populations but also in understanding potential outcomes associated with introduction of novel viruses such as highly pathogenic IAV H5 viruses in wild bird populations .
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2017
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Competition between influenza A virus subtypes through heterosubtypic immunity modulates re-infection and antibody dynamics in the mallard duck
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Serological tests have long been established as rapid , simple and inexpensive tools for the diagnosis and follow-up of PCM . However , different protocols and antigen preparations are used and the few attempts to standardize the routine serological methods have not succeeded . We compared the performance of six Brazilian reference centers for serological diagnosis of PCM . Each center provided 30 sera of PCM patients , with positive high , intermediate and low titers , which were defined as the “reference” titers . Each center then applied its own antigen preparation and serological routine test , either semiquantitative double immunodifusion or counterimmmunoelectrophoresis , in the 150 sera from the other five centers blindly as regard to the “reference” titers . Titers were transformed into scores: 0 ( negative ) , 1 ( healing titers ) , 2 ( active disease , low titers ) and 3 ( active disease , high titers ) according to each center's criteria . Major discordances were considered between scores indicating active disease and scores indicating negative or healing titers; such discordance when associated with proper clinical and other laboratorial data , may correspond to different approaches to the patient's treatment . Surprisingly , all centers exhibited a high rate of “major” discordances with a mean of 31 ( 20% ) discordant scores . Alternatively , when the scores given by one center to their own sera were compared with the scores given to their sera by the remaining five other centers , a high rate of major discordances was also found , with a mean number of 14 . 8 sera in 30 presenting a discordance with at least one other center . The data also suggest that centers that used CIE and pool of isolates for antigen preparation performed better . There are inconsistencies among the laboratories that are strong enough to result in conflicting information regarding the patients' treatment . Renewed efforts should be promoted to improve standardization of the serological diagnosis of PCM .
Paracoccidioidomycosis ( PCM ) is a neglected systemic fungal infection prevalent mostly in South America . Despite the significant progress in several areas of knowledge since it was described by Adolpho Lutz , in 1908 , it still shows high rates of morbidity and mortality and low visibility [1] . In rural areas of Brazil there are approximately four new cases per million inhabitants , making it the third cause of death from chronic infections , with 1 . 65 cases per million [2] . The gold standard of PCM diagnosis is the visualization of yeast cells with typical multiple budding aspect in a clinical sample or isolation of the fungus in culture medium [3] . The latter has either low sensitivity when samples obtained from non-sterile sites ( e . g . , sputum ) are used , or is more sensitive in sterile , deep-seated site samples , which , however , are not frequently available . In addition , the growth of P . brasiliensis can take several weeks [3] , [4] . Serological tests have been established since the 70's contributing to the rapid , simple and inexpensive diagnosis of the mycosis [5]–[8] . Several antigenic preparations , including sonicated extracts and filtered phase concentrated of cultures of the yeast form of the fungus , have so far been used for the serological diagnosis of PCM [9] . Early on some authors reported on the issue of variability in the antigen preparations [10] , [11] . The growth of yeast cells is performed in culture media and conditions such as incubation time , temperature , size of inoculum , with or without agitation , can lead to differences in the antigens produced in different diagnostic centers . In fact , different protocols and antigen preparations are currently used by these centers for the serological diagnosis and follow up of patients with PCM . Most centers use semiquantitative immunoprecipitation techniques , either the double immunodiffusion ( DID ) or counterimmunoelectrophoresis ( CIE ) , or both [7] , [12]–[14] . However , their performance is not routinely checked , in part due to the lack of external standards . Only an internal positive control is used , which in most centers is a patient's serum with a known positive titer . Moreover , the few attempts put forward to standardize the routine serological methods used in PCM patients have not succeeded [15] . One major reason is that the reference centers have been carrying out in house methodologies for many years with apparently satisfactory performances [11] , [15] , [16] . However , unfortunately in most instances these centers do not have feedback regarding the clinical correlation from the physicians assisting the patients . To address this issue , we compared the performance of laboratories from six medical mycology reference centers in Brazil that carry out routine serological diagnosis of PCM . The results show that there are inconsistencies among the laboratories , strong enough to result in conflicting information regarding the patient's treatment , and that renewed efforts should be promoted to improve standardization of the serological diagnosis of PCM .
Six reference centers that traditionally and routinely perform serological diagnosis of PCM participated in this study . They all have made significant scientific contributions to the improvement of the serological diagnosis of this mycosis and for that reason were invited to participate in the study: Mycoses Immunodiagnosis Laboratory , Adolfo Lutz Institute , São Paulo ( IALSP ) ; Myco-serology Laboratory , Department of Microbiology , Immunology and Parasitology , Federal University of São Paulo ( UNIFESP ) ; Clinical Mycology Laboratory , Pharmaceutical Sciences School , São Paulo State University ( UNESP ) , Araraquara , SP; Serology Laboratory , Clinics Hospital , Ribeirão Preto School of Medicine of the University of São Paulo ( FMRPUSP ) ; Medical Mycology Laboratory , Laboratory of Teaching and Research in Clinical Analysis from Maringá State ( LEPAC ) ; and Medical Mycology Laboratory Clinics Hospital of the Medical School ( LIM53 ) and Tropical Medicine Institute , University of São Paulo ( IMTSP ) . Each center was requested to provide 30 sera of PCM patients from their repository , with positive high , intermediate and low titers according to their own criteria . The anonymized sera were numbered 1–30 and aliquots of 120 µl were sent to the remaining five centers to perform their own serological assays . Thus each center performed their usual serological assays in 150 sera from 5 different centers blindly with regard to the “reference” titer of the sera . The results were then sent directly to the coordinating center ( IMTSP ) , which analyzed the data . In addition , the coordinating center also provided all centers with aliquots of 6 healthy donor sera , as negative controls . These donors did not have previous history of tuberculosis or any other significant infectious disease , and the sera were non-reactive for PCM and histoplasmosis . The study was approved by the Human Research Analysis Ethics Committee of the Hospital das Clínicas da Faculdade de Medicina da USP , accession number #7915 . All centers employed an immunoprecipitating technique , either semiquantitative DID [17] or CIE [18] . The isolates used for antigen preparation are shown in Table 1 . Two antigens were used: ( a ) the somatic antigen , obtained through sonication ( 100–150 W for 30′ ) of the cells grown for 15 days in Fava Netto's medium at 35°C [19] and ( b ) the culture filtrate ( metabolic antigen ) , obtained from yeast cells grown in Negroni's medium for 7–10 days ( log phase growth ) at 37°C [11] . The sonicated antigen is kept frozen ( −20°C ) while the culture filtrate is stocked at 4°C [11] , [19] . Under these conditions , they are stable for several years . Reactivity of each new batch is tested comparatively with the previous one using patients' sera with high , intermediate and low titers , as well as with a control negative sera and sera from patients with other fungal infections . Briefly , for the DID , glass slides ( 25×75 mm ) were covered with melted purified agar gel punched according to a pattern ( a central well surrounded by six wells ) . The antigen solution was placed in the central well while the peripheral wells were filled with the patient's sera and , as a positive control , either a patient's serum with a known positive titer or rabbit hyperimmune serum . Slides were incubated in a moist chamber at room temperature ( 20–25°C ) and washed with 5% sodium citrate followed by 0 . 9% saline . They were dried and stained with Coomassie Brilliant Blue R ( Sigma , USA ) . The CIE is also based on the diffusion of proteins but an electric current is applied through a buffered diffusion medium to accelerate the migration of antibody and antigen , with formation of the precipitation lines after around one hour . For the CIE , the glass slides were covered with 1% buffered agarose gel ( pH 8 . 2 ) and two parallel rows of wells were punched in the gel . The patient's serum samples and positive control were applied to the anodic side and the antigens to the cathodic side of the slides . All sera were diluted two-fold in 0 . 9% saline and were tested from the undiluted sample . After electrophoresis , the slides were washed in 0 . 9% saline , dried and stained with Coomassie Brilliant Blue R . The differences in the protocols used by each center are detailed in Table 1 . All centers provided , as requested , 30 sera of PCM patients from their repository , collected within the last five years . These sera were then assayed blindly with regard to their titers by the other 5 centers . For this , the centers were randomly assigned A to F and the sera were numbered 1 to 30 by three of the authors ( GMBDN , CPT , GB ) who did not participate in the serological assays . To allow comparison among the centers' results , titers were transformed in scores ranging from 0 ( negative ) to 3 ( high titers ) according to each center's criteria as described in Table 2 . Scores of the sera provided for this study ranged from 1 to 3 , with score 1 corresponding to healing titers , and scores 2 and 3 corresponding to active disease with , respectively , low and high titers . Each center's set of sera was assayed by the other five centers . The results from the donor center , arbitrarily defined as the reference score for their own sera , were then compared with the results of the other five centers . Discordance was defined as a different score , which could be minor , i . e . , without a putative clinical consequence for the patient , or major , when the discordance could potentially lead to conflicting decisions regarding the patient's treatment . Minor discordances were between ( a ) scores 0 and 1: in both cases , either a negative serological result , or a low ( healing ) titer , would suggest inactive disease and both , in association with clinical and other data , eventually indicate treatment cessation; or ( b ) scores 2 and 3 , both of which are associated with active disease . Major discordances were between scores 2 and 0 or 1 , and between 3 and 0 or 1 , which , when associated with proper clinical and other laboratorial data , may have led to a different treatment of the patient . Comparisons among laboratories were done using the Chi-square and Fischer exact test . Differences were considered significant when p<0 . 05 .
All centers exhibited a surprisingly high rate of “major” discordances when the scores given by each center to the sera provided by the other 5 centers were compared with the “reference” scores ( Table 3 ) . There was some variability in the rate of discordances among the centers , ranging from 22 ( 15% ) to 45 ( 30% ) “major” discordant scores out of 150 scores given , and a mean number of discordant scores of 31 ( 20% ) . In fact , the rates of discordances differed significantly among the centers ( p = . 0007 , Chi-square ) . Minor discordances were also highly frequent , ranging from 16 to 52 out of 150 scores given and a mean of 36 ( 24% ) ( Table S1 ) . Analysis of the performance using the scores given by one center to their own sera ( reference score ) and comparing them to the scores given to their sera by the remaining five other centers , showed a high rate of major discordances as well ( Table 4 ) . For example , 15 out of the 30 ( 50% ) center A's reference scores were discordant with at least one of the remaining centers' scores , and , eight out of the 30 reference scores ( 27% ) were discordant with two or more of the remaining centers . Again variability in the rates of discordance was detected among the centers: in the first comparison , it ranged from 9 ( 30% ) to 23 ( 77% ) scores ( p = . 0157 , chi-square ) and for the second comparison it ranged from 2 ( 7% ) to 10 ( 33% ) scores ( p>0 . 05 , Chi-square ) . The mean numbers of sera presenting a “major” discordance were respectively 14 . 8 and 7 . 1 . In all , considering the 180 references scores provided by the 6 centers to their own sera , 79 ( 44% ) of them presented a major discordance with at least one of the other center's score , and 43 ( 24% ) presented a major discordance with at least two other centers' scores ( Table 4 ) . Minor discordances were also frequent when this other analysis was used: for the reference center A , “minor” discordances with at least one other center were found for 19 of their sera ( Table S2 ) . In all , 95 of the 180 sera ( 53% ) presented a “minor” discordance with at least one other laboratory result ( Table S2 ) . The 6 control negative sera provided by one of the centers were also negative ( score 0 ) when assayed by the other 5 centers , with the exception of one serum that received a score 1 ( titer 1∶2 ) by laboratory C . This titer is consistent with a healing titer or a non-specific reaction according to this laboratory criterion . Since each lab has its own , in house , assay for detection of anti-P . brasiliensis antibodies , we anticipated that “minor” discordances ( i . e . , slight and clinically not relevant differences in the titers of antibodies ) would occur with some frequency . Unexpectedly , we found a high rate of “major” discordances ( i . e . differences in scores that may have led to different clinical managements: maintenance or interruption of the treatment ) . In an attempt to understand the reasons for these discrepancies , the influence of two main variables that discriminated the centers with regard to their protocols were evaluated , namely the technique employed ( DID [n = 3 centers] vs . CIE [n = 3] ) and type of the antigen ( single P . brasiliensis isolate [n = 4 centers] vs . pool of isolates [n = 2] ) . Gathering the 150 scores given by each one of the 3 centers performing the DID technique to the 5 other centers' sera , of a total of 450 scores , in 343 instances there was agreement and in 107 major discordance; the same analysis for the 3 centers using the CIE technique showed more concordant scores ( n = 369 ) and less discordant scores ( n = 81 , p = 0 . 04 , Fischer exact test ) . Among the 300 scores given by the 2 centers using a pool of isolates , the proportion was 47 discordant and 253 concordant scores . This proportion was significantly higher than that obtained with the 4 centers using only one isolate: 141 discordant and 459 concordant scores ( p = 0 . 007 , Fischer exact test ) . Thus , the type of the reaction and antigen preparation may be factors that influence the accuracy of the serological result . Regarding the antigen preparation , not only gp43 , but several other components in both the somatic and culture filtrate antigens react with the patients' sera [20] , [21] , [22] . The amount of these components in the antigen preparations not only varies among the strains , but also in a single strain depending on the number of repeated subculturing , medium used , log phase of growth when the fungus is harvested , among other factors . This is probably a major factor in the inconsistencies among centers . Other particularities that likely influenced the accuracy of the serological results ( such as duration of reaction , incubation time , expertise and background of the technician responsible for performing the assay , etc . ) , could not be assessed in the present study because it was not designed to evaluate these factors . The present study demonstrated a high rate of discordance among centers that are considered to be reference centers for the diagnosis and serological follow up of PCM patients . Due to the fact that , per request , only sera from PCM patients were provided by these centers , we could not analyze the performance of the serological tests for the diagnosis of PCM . For this , sera of patients with other mycoses and infectious diseases would also be required . However , the high rate of discordances found certainly raises some suspicion with regard to this issue . We illustrate this possibility with one of the sera from center B , whose donor was a 56 year-old patient with chronic non-specific respiratory symptoms , initially and presumptively diagnosed as pulmonary tuberculosis ( TB ) at a community health care unit . TB treatment was ineffective , the pulmonary symptoms worsened and he developed a pneumothorax . Microbiological evaluation was negative on both sputum and bronchoalveolar lavage . Diagnosis of PCM was made based on the history of having lived in an endemic area , a suggestive chest X-ray , and a 1∶32 titer on the CIE test for PCM ( score 2 , active disease ) . A similar active disease score was given by centers E and F , but centers A , C and D gave titers corresponding to score 1 ( 1∶1 , 1∶8 and 1∶2 , respectively ) , suggestive of healing disease , which could potentially delay the diagnosis and the beginning of antifungal treatment . Relapses and recrudescence are commonly reported during the prolonged ( usually >1 year ) antifungal therapy of this mycosis . In Argentina , Negroni et al [23] reported that 14 . 3% of the patients relapsed after a follow up of 15 months . In Brazil , Marques reported 13 . 8% of relapses after 10 years of follow up , although almost half of the relapses occurred in the first 3 years , when the patients were still on or just off antifungal therapy [24] . Serological follow up has been shown to be an important tool in the early diagnosis of relapses [13] , [14] , [25] . The factor most commonly reported as contributing to the failure of the anti-fungal treatment is poor compliance due to socio-economical factors such as alcoholism , unemployment and/or long distance from the local drug provider [26] . Although decisions regarding the interruption or prolongation of drug therapy are not made solely based on the serological result , we speculate that in certain cases the relapses would be related to inadvertent therapy discontinuation due to misleading serological monitoring . On the other hand , some patients may undergo unnecessary prolongation of the antifungal therapy . In any case , it is clear from the present study that an effort from the medical mycology community must be undertaken ( or re-undertaken ) to improve better standardization of the serological diagnosis of this mycosis . Our results suggest that particularly the type of antigen ( pool vs . single isolate ) and technique ( DID vs . CIE ) should be addressed . Efforts should also be made at the same time to develop and standardize P . lutzii serological diagnosis tests . This is a new species in the Paracoccidioides genera recently described that is endemic in some areas of South America where the patients' sera were reported to not recognize the P . brasiliensis' antigens in conventional serological tests [27]–[29] . This issue could not be addressed here since the 6 reference centers participating in the study were located in P . brasiliensis endemic areas and provided sera only with positive serological results . However , occasionally reference centers outside P . lutzii endemic areas may handle sera from PCM due to P . lutzii and release false negative serological results . This has already been documented [30] and will certainly be more common owing to the increasing migration rates in South American countries , particularly Brazil . Finally , high discordance rates may well occur in the diagnosis of other endemic mycoses such as histoplasmosis , coccidioidomycosis and blastomycosis , all of which are endemic in some areas of South America and that are covered by the reference centers involved in this study or by other reference centers . The efforts to improve the serological diagnosis should also be addressed for these mycoses that , like PCM , remain among the most neglected diseases in South America .
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Paracoccidioidomycosis ( PCM ) is a neglected systemic fungal infection prevalent mostly in South America . Serological tests have long been established as rapid , simple and inexpensive tools for the diagnosis and follow-up of PCM . However , different protocols and reagents are used . We compared here the performance of six Brazilian reference centers for serological diagnosis of PCM . Each center provided 30 sera of PCM patients , with positive high , intermediate and low titers , which were defined as the “reference” titers . Each center then applied its serological routine test to the 150 sera from the other five centers blindly as regards to the “reference” titers . Surprisingly , all centers exhibited a high rate of discordances ( mean of 31 discordant scores in 150 sera tested ) . When the scores given by one center to their own sera were compared with the scores given to their sera by the other centers , a high rate of major discordances was found ( a mean of 14 . 8 sera in 30 presented a discordance with at least one other center ) . We concluded that there are inconsistencies among the laboratories that can potentially result in conflicting information regarding the patient's treatment . Renewed efforts should be promoted to improve standardization of the serological diagnosis of PCM .
|
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2014
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Serological Diagnosis of Paracoccidioidomycosis: High Rate of Inter-laboratorial Variability among Medical Mycology Reference Centers
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The relative role of drift versus selection underlying the evolution of bacterial species within the gut microbiota remains poorly understood . The large sizes of bacterial populations in this environment suggest that even adaptive mutations with weak effects , thought to be the most frequently occurring , could substantially contribute to a rapid pace of evolutionary change in the gut . We followed the emergence of intra-species diversity in a commensal Escherichia coli strain that previously acquired an adaptive mutation with strong effect during one week of colonization of the mouse gut . Following this first step , which consisted of inactivating a metabolic operon , one third of the subsequent adaptive mutations were found to have a selective effect as high as the first . Nevertheless , the order of the adaptive steps was strongly affected by a mutational hotspot with an exceptionally high mutation rate of 10−5 . The pattern of polymorphism emerging in the populations evolving within different hosts was characterized by periodic selection , which reduced diversity , but also frequency-dependent selection , actively maintaining genetic diversity . Furthermore , the continuous emergence of similar phenotypes due to distinct mutations , known as clonal interference , was pervasive . Evolutionary change within the gut is therefore highly repeatable within and across hosts , with adaptive mutations of selection coefficients as strong as 12% accumulating without strong constraints on genetic background . In vivo competitive assays showed that one of the second steps ( focA ) exhibited positive epistasis with the first , while another ( dcuB ) exhibited negative epistasis . The data shows that strong effect adaptive mutations continuously recur in gut commensal bacterial species .
The composition of the gut microbiota can exert a strong influence on host physiology , behavior and health . Time series data have shown that the gut microbiota typically comprises a diverse community of species and that reduction of such diversity is frequently associated with illness [1] . Less studied , but potentially as important , is the diversity at the level of each species [2 , 3] . In fact , studies providing an understanding on how intraspecific variation in the microbiota emerges and changes over time are lacking [4] . Therefore , important questions such as whether the extant intra-species diversity is mainly due to migration and genetic drift or the result of natural selection on new mutations remain unanswered [5] . While some mutations segregating in natural populations may be neutral , the large size of bacterial communities inhabiting the mammalian gut suggests that here , polymorphism is more likely to result from deterministic forces [6] . The importance of natural selection and its strength versus other evolutionary processes in shaping intra-species variation in the guts of hosts living in their natural environments is hard to dissect . Direct measurements of selective effects of spontaneously emerging mutations in this environment are extremely rare due to its complex nature . Nevertheless , even mutations with very weak adaptive effects , thought occur most frequently [7] , could substantially contribute to a rapid pace of bacterial evolutionary change in the gut . Here , using experimental evolution of E . coli colonizing a natural environment , coupled with whole genome sequencing and in vivo competitive assays , we unravel some of the targets of adaptive evolution and the strength of the effects of beneficial mutations in vivo . Mouse colonization models offer a great opportunity to observe the emergence of diversity , test its repeatability amongst different hosts and measure the strength of natural selection in bacterial populations comprising the gut microbiota . Using a common model of gut colonization , we show that all classical forms of natural selection , i . e . periodic selection , balancing selection and clonal interference are ubiquitous , and contribute to strain variation within the gut . Furthermore , we were able to i ) quantify important evolutionary parameters in this system , such as the effect of second step mutations , ii ) evaluate the repeatability of evolution and iii ) assess possible constrains on the order of the observed adaptive events .
By following the evolution of a commensal E . coli strain inhabiting the guts of streptomycin treated mice , we previously observed the emergence and spread of adaptive mutations within only 3 days of colonization [8] . The first step of adaptation targeted a single locus and consisted of the selective inactivation of the gat operon , which enables E . coli to metabolize galactitol [8] . Distinct alleles conferring this phenotype , with similar selective effects ( 8 ±0 . 01% ( 2SE ) benefit ) , recurrently emerged in all independently evolving E . coli populations recovered from different hosts . Using this experimental system , we have now studied the subsequent steps of adaptation . We colonized 15 mice with a clonal population of E . coli carrying the first beneficial phenotype ( inability to metabolize galactitol ) . This phenotype is conferred by a single base pair insertion into the coding region of the gatC gene , which codes for a subunit of the galactitol transporter , to thus prevent galactitol uptake . The colonizing population was also made dimorphic for a fluorescent marker to enable the emergence of further adaptive changes to be determined and their effects measured by competitive fitness assays [9] . To ask whether single or multiple genetic targets underlie the second step of adaptation , and to determine how repeatable evolution is , we performed whole genome sequencing ( WGS ) of 15 independently evolved clones ( sampled at day 24 post colonization , see Fig 1 and S1 Table ) . A total of 30 mutations were detected , including 12 mutations in coding regions , 13 mutations in intergenic regions , as well as 3 large duplications and 2 large deletions . From the mutations observed in coding regions , 3 were non-synonymous , 1 was a nonsense mutation , and 6 mutations involved insertion sequence ( IS ) insertions . Finally , 8 IS insertions and 5 small insertion deletion mutations occurred in intergenic regions . The average number of mutations per sequenced clone was 2 , a number similar to that observed in clones sampled from the first colonization ( 2 . 3 [8] , n = 14 , Fig 1 ) . To determine the likely targets of adaptation we compared the mutations occurring in the clones sampled from both colonizations . As shown in Fig 1 , some mutational targets were similar between the first and second colonizations . Pooling all the clones , 7 new targets of mutation ( srlR , arcA , yjjP , oppB , radA-dup , dcuB and focA ) were recurrently detected in different mice , revealing their adaptive nature [10] . The most frequent was srlR , a locus that codes for the repressor of the sorbitol operon; followed by insertions in the intergenic region upstream of dcuB and focA , which code for membrane transporters of C4-dicarboxylates ( e . g . fumarate ) and formate , respectively [11 , 12] . The regulatory region of arcA , a dual transcriptional regulator predominantly involved in controlling the respiratory flexibility of E . coli [13 , 14] , was also highly targeted . Another frequent mutation involved a large duplication ( ranging from 34Kb to 157Kb ) . Two additional targets were observed less frequently: the promoter region of yjjP , which codes for a membrane protein of unknown function [15] , and the coding region of oppB , which codes for a component of the oligopeptide ABC transporter [16] . These two loci were targeted by IS element insertions . Half of the mutations identified in the sequenced clones ( n = 29 ) were caused by IS insertions ( Fig 1 and S1 Table ) , specifically IS5 , IS1 , IS2 and IS186 , which have a high rate of transposition , between 10−6 and 10−5 per element per generation , in E . coli [17] . In fact , IS insertions occurred in 6 out of the 7 targets of adaptation identified through parallelism . Among these insertions , 78% were located in regulatory regions ( S1 Fig ) , suggesting that mutations altering gene regulation are an important driver of adaptation in these populations [18] . Only one of the second step mutations involving an IS insertion likely caused a loss of function ( insertion of an IS element in the coding region of oppB ) . The phenotypic effects of IS adaptive insertions in the regulatory regions of focA , dcuB , arcA , yjjY and yjjP were determined by comparing gene expression of representative mutants evolved during gut colonization with that of the ancestral clone ( see Material and Methods ) . Since E . coli in the gut experiences variation in the amount of oxygen conditions [13 , 14] , gene expression was assayed both in the presence and absence of oxygen . In anaerobic conditions the transcription level of arcA , focA and dcuB was higher in the evolved clones than in the ancestor . In aerobic conditions the effect of IS insertions on expression was more variable , ranging from a 1-fold reduction of dcuB expression to a 2-fold increase in the expression of yjjP ( Fig 2A ) . Mutational targets provide clues on the traits under selection in a given environment . Three of the targets identified ( arcA , focA and dcuB ) are related with microaerobic/anaerobic metabolism . For example , fumarate ( transported by DcuB ) has been described as the most important anaerobic electron acceptor in the intestine of streptomycin-treated mice model [14] , whereas formate ( transported by FocA ) is the signature compound of anaerobic metabolism for E . coli [20] . The gut is described as a microaerobic environment [13 , 14] where E coli can take profit of its respiratory flexibility . ArcA is known to be an important regulator of this trait and to play an important role in the ability of E . coli to colonize [13 , 14] . Specifically , ArcA is a global repressor of carbon oxidation pathways [21] , repressing the expression of 74 operons and inducing the expression of 11 operons ( including focA ) under anaerobic conditions . Interestingly , the 4 large duplications , observed in different hosts , involved a genomic region that includes arcA . Since duplications can raise gene dosage we hypothesize that increased expression of this gene constitutes a major beneficial phenotype . In sum , the mutational targets indicate that E . coli is adapting to the intestinal tract by tuning the expression of a global respiratory regulator ( arcA ) and changing its carbohydrate metabolism and transport ( srlR , dcuB , focA and oppB ) . Consistent with this metabolic adaptation , several gut-adapted clones have an increased fitness when competing with the ancestral in media containing carbon sources known to be present in the intestine [22] . This increased competitive ability occurs both in the presence and absence of oxygen ( S2 Table ) . Another interesting example of gain-of-function mutations , not involving transpositions , occurred at the srlR locus ( which codes for the transcriptional repressor of the sorbitol operon ) . Around half of the mutations observed in this gene ( considering the 29 clones from the two colonizations ) were either single base pair insertions or nonsense mutations , which thus presumably inactivated this gene . Indeed the expression of srlA ( the first gene of the sorbitol operon ) was increased 12-fold and 8-fold in the absence and in the presence of oxygen , respectively ( Fig 2B ) , in the gut-adapted clones , supporting the hypothesis that mutations in srlR abrogated expression of this repressor and lead to increased expression of the sorbitol operon . Consistently , the mutants carrying the srlR mutation have an advantage when competing for sorbitol ( S2 Table ) . The order of the mutational steps along an adaptive walk is influenced by the mutation rate , the fitness effects of mutations and/or possible epistatic interactions as well as the stability of the environment [23 , 24] . We investigated whether these processes play a role in E . coli adaptation to the mouse gut . We measured the spontaneous mutation rate to the functional inactivation of the gat operon ( μ ) , using a fluctuation assay in rich medium ( LB ) , and found a μ of 10−5 ( 95% CI , [6 . 7x10-6 , 4x10-4] ) per locus per generation ( Fig 3 ) . We compared this rate with the rate of inactivating mutations occurring at another locus of the genome ( nfsA ) , which results in resistance to furazolidone ( furazR ) . For this locus we found a rate of 4x10-8 ( 95% CI , [4 . 6x10-8 , 2 . 7x10-8] ) , far closer to the expected rate for loss of function mutations ( 10−8 per gene per cell division [26] ) . After correcting for locus size differences , the inactivation rate of the gat operon is one order of magnitude higher than that of nsfA , which is taken as a random locus . The estimation of the mutation rate in the fluctuation test performed above assumes that the mutations causing the phenotype for which μ is being determined are neutral . To account for a potential bias in μ estimate , which might have resulted from a lack of neutrality of the gat-negative phenotype in LB , we determined its fitness effect when in direct competition with the gat-positive ancestor in this environment . The gat-negative phenotype was found to be deleterious in vitro , with an estimated selection coefficient of -0 . 06 ±0 . 01 ( 2SE ) . A similar competition between a nfsA knock mutant and the wild type bacterium resulted in a selection coefficient of -0 . 007±0 . 005 for the mutant , thus the knock out of this gene is very close to neutrality . Our estimate of μ is therefore conservative , since the strong deleterious effect of the phenotype being scored can only cause an underestimation of μ . These results support the conclusion that the high rate of spontaneous inactivation of the gat-operon could have been an important contributory factor for the emergence of the gat-negative phenotype as the first adaptive event to occur . Heterogeneity in the mutation rate does not exclude a possible contribution of direct or epistatic selection to the order of the adaptive events . To test for this , and to determine the selective effects of beneficial mutations in the gut , we estimated the selection coefficients of 6 out of the 7 second step mutations when each occurs either in a wild-type ( gat-positive ) or in an evolved ( gat-negative ) background . This was done through in vivo competitive fitness assays against the ancestors of the first ( gat-positive ) ( Figs 4 and 5 ) and second colonizations ( gat-negative ) ( Fig 5 ) . The radA-dup mutation was not tested , as this duplication was highly unstable during in vitro manipulation . We note that despite the allelic variation within each target of adaptation , only one allele representative of each locus was tested . While for the gat locus we have previously shown that different alleles have equivalent selective effects [8 , 27] , we acknowledge that for the second targets of adaptation , different alleles may have different phenotypes . We found substantial variation for the selective effects of these mutations in both backgrounds ( P = 6 x 10−6 for the gat-positive background and P = 6 x 10−6 for gat-negative background , ANOVA ) . This contrasts with the effect of the mutations responsible for the first step of adaptation ( for which a selective effect , sgat-neg = 0 . 08 ± 0 . 01 , was estimated [8] , Fig 5 ) . Most mutations showed a weaker competitive advantage on the gat-positive background than those causing the first adaptive step ( ANOVA with Tukey post-hoc correction for multiple testing , Fig 5 and S3 Table ) . Interestingly the effect of the arcA mutation was significantly larger than the first step mutations ( P = 0 . 01 , ANOVA Contrasts ) . These results indicate that alongside differences in the mutation rate , selection might also have contributed to the order of the adaptive steps . Remarkably , the mutation inactivating the srlR locus , leading to constitutive expression of the sorbitol operon ( Fig 2B ) , steadily decreased in frequency ( ssrlR = -0 . 03 ) during the first days of competition ( Fig 4F ) . It was subsequently maintained at low frequency , an observation that would not be expected for a strictly deleterious allele . This mutation also emerged in multiple , independent populations ( Fig 1 ) , all of which suggests that srlR is a likely target for balancing selection , a hypothesis further investigated below . Importantly , we observed the appearance of the gat-negative phenotype on the ancestral background , and in all backgrounds carrying a single second step mutation ( Fig 4 ) . This strongly suggests that high repeatability and little historical contingency on genetic background , accompany the evolutionary path taken by E . coli during gut colonization . Here we refer to historical contingency as the situation where the beneficial effect of a focal mutation is contingent on the ancestral background since it becomes deleterious in an evolved background , thus limiting the number of evolutionary paths . [28] . To determine whether epistatic interactions occur between the mutations , and how the first and second steps differ in terms of magnitude , we further measured the effects of these second step mutations in the gat-negative background . We observed that mutations in arcA , yjjP and oppB had a similar effect in gat-negative and gat-positive backgrounds , whereas the fitness effect of mutations in dcuB and focA differed ( P = 0 . 029 for both of the mutations . Mann-Whitney-Wilcoxon ) . dcuB showed negative epistasis , since its effect is undistinguishable from neutrality on the more adapted background ( the gat-negative ) while it is beneficial ( 0 . 02 ± 0 . 01 ) in the less adapted background ( gat-positive ) . The high frequency of emergence of mutations in dcuB during the evolution experiment , however , strongly support its having a beneficial effect in the gat-negative background , suggesting that dcuB is , in fact , a likely target for frequency-dependent selection . Conversely , the mutation in focA had a significantly higher effect in the more adapted , gat-negative , background . This provides an interesting example of positive epistasis , a phenomenon which , though documented , occurs much less frequently than that of negative epistasis [29–31] . Consistent with theoretical models of adaptation towards a fitness optimum , several empirical studies have found that the effects of beneficial mutations tend to decrease as adaptation proceeds [32–34] . Interestingly , we have found that two of the second step mutations ( sarcA = 0 . 09 ± 0 . 02 and sfocA = 0 . 08 ± 0 . 02 ) were as large as the first step ( sgat-neg = 0 . 08 ± 0 . 01 ) ( P = 0 . 01 and P = 0 . 05 , respectively . ANOVA Contrasts ) . This observation suggests that the gut maybe more complex than a more strictly constant in vitro environment or that there are multiple discrete traits to optimize even in a constant environment . A key distinction between a fixed and a moving landscape is that in the first the adaptive walk is short and strong diminishing returns are expected . In a moving landscape [35–37] , adaptation is continuous and the step sizes may not necessarily decrease with time . Multispecies communities of microbes are a likely scenario for the emergence of important ecological interactions . One possible outcome of these interactions is frequency-dependent selection ( FDS ) , a situation where the fitness of a phenotype changes according to its frequency [5] . Negative FDS occurs if a phenotype is deleterious when common but beneficial when rare: this form of selection is capable of maintaining genetic diversity . The results of the competition assays with the srlR mutation ( Fig 4F ) indicate that negative FDS may have played a role in the evolution of the populations . To directly test this hypothesis , we performed new competitive fitness assays starting with different frequencies of the wild type and srlR mutant strains , ( same genotypes as in Fig 4F ) . Indeed , srlR mutants show a strong disadvantage when competing with the ancestor at high frequency but were maintained at lower frequencies ( Fig 6A ) . When plotting the change in frequency of this mutant as a function of its initial frequency , a significant linear correlation is observed ( Fig 6B—Linear regression Adjusted R2 = 0 . 66 , P<0 . 0001; slope = -0 . 52 ( 0 . 06 SE ) , intercept = 0 . 04 ( 0 . 02 SE ) , which supports FDS . Interestingly , the effect of the srlR mutation was so strong that frequency dependence was maintained , irrespective of the background in which it occurred . As shown in Fig 6C and 6E , two different genotypes ( gatC oppB srlR and gatC focA srlR ) , carrying a srlR mutation , had a competitive advantage when rare , but a disadvantage when at high frequency ( Fig 6D—Linear regression , Adjusted R2 = 0 . 41 P<0 . 0001; slope = -0 . 32 ( 0 . 07 SE ) , intercept = 0 . 13 ( 0 . 04 SE ) and Fig 6F—Linear regression Adjusted R2 = 0 . 30 P = 0 . 0015; slope = -0 . 35 ( 0 . 1 SE ) , intercept = 0 . 13 ( 0 . 05 SE ) ) . This shows that positive Darwinian selection on focA ( Figs 4B and 5 ) , previously shown to drive this mutation rapidly to fixation , can be highly influenced by negative FDS if the srlR mutation co-occurs with this mutation . The strength of selection on oppB ( Fig 5 ) is much weaker than that of focA , yet when combined with srlR it shows a similar dynamics ( Fig 6C ) . This suggests either an epistatic interaction between srlR and oppB or that oppB is also under FDS . Overall , these observations support the hypothesis that the srlR mutation per se is able to maintain diversity for long periods of time . To monitor adaptation at the genome-wide scale , we analyzed levels of polymorphism across time in two of the evolving populations colonizing the mouse gut . We used both WGS of samples of the populations and typing of selected mutations in clones to unravel the pervasiveness of clonal interference . Distinct alleles at each locus were found segregating in the two populations assayed ( Fig 7A and 7B and S3 Table ) : two alleles for focA , yjjP , srlR , three for dcuB and four for arcA , further supporting the adaptive nature of the identified targets and showing that the evolutionary process is not limited by mutation . Importantly , population 2 . 10 is remarkable in that it shows evidence of co-occurrence of all classical forms of natural selection ( periodic selection , clonal interference and FDS ) , within less than a month . First , there is an example of a molecular pattern close to that expected under periodic selection , where a strong haplotype involving an IS insertion in radA and a large duplication ( radA-dup ) rapidly sweeps close to fixation ( Fig 6B ) . Second , after day 11 a signature of clonal interference between different alleles of focA and yjjP is observed . Finally , towards the end of the experiment , clones carrying mutations in srlR emerge , which should result in negative FDS ( as shown in Fig 6 ) . The selection coefficient associated with the radA-dup mutant can be estimated from its change in frequency while sweeping through the population , but before other mutations can be detected . The estimate for the effect of this mutation is srad dup = 0 . 14 ( Fig 7C ) . This is the highest selection coefficient for all of the mutations of the second steps of adaptation ( Fig 5 ) , though it is based on a single observation . In the same manner , a selective benefit for the arcA mutation can also be estimated ( Fig 7C ) . A selection coefficient of 0 . 09 is estimated between day 7 and 8 when the mutation was first detected and before day 11 when another mutation ( pphB ) was also observed ( S4 Table ) . Even though this is a crude estimate , it is remarkably consistent to that measured in direct competition between a mutant carrying the same IS5 insertion in the arcA regulatory region and the ancestor of the second colonization ( Fig 6 and S3 Table sarcA = 0 . 09±0 . 02 ) .
The rules governing the adaptive process of microbial populations in natural communities are far from understood . Numerous aspects of host physiology [38] and even behavior are influenced by the microbiota [39] , making this a priority environment to appreciate how much microbial evolution occurs within the lifetime of a host . Nevertheless , the amount of evolution taking place in this environment , as well as its contribution to the overall diversity resulting from de novo mutation is currently underappreciated . We followed evolution in a natural environment under controlled conditions and studied two consecutive bouts of adaptation . The first bout was caused by the emergence of multiple mutations ( both within and among hosts ) causing a similar phenotype , loss of function of the operon encoding galactitol metabolism ( gat-negative phenotype ) , and an associated selective effect of 0 . 08 ± 0 . 01 per generation [8] . The ability to use galactitol is a polymorphic trait in wild strains of E . coli [40] . The second bout of adaptation targeted at least seven different loci . No strong signals of deceleration in adaptation rate were detected , judging from the strong effect of the second step mutations estimated either from their frequency increase in the evolving populations ( Fig 6C , srad-dup = 0 . 14 and sarcA = 0 . 09 ) or from direct in vivo competition assays against the ancestral strain of the second colonization , which had a gat-negative phenotype ( Fig 5 ) . This observation contrasts with the pattern of diminishing returns epistasis previously found in E . coli adapting to a glucose limiting environment [29] and in Methylobacterium extorquens AM1 evolved in batch culture with methanol as the sole carbon source [41] . The extent to which the fitness effects of further mutations that accumulate in vivo may become smaller as adaptation proceeds remains to be determined in future work . By discretizing the adaptive steps we aimed to understand not only the rhythm but also the repeatability of adaptation in the gut environment . WGS of clones isolated from different mice revealed 7 parallel targets comprising: three membrane transporters , one repressor of a metabolic operon , one major regulator involved in the aerobiosis/anaerobiosis transition , one large duplication , and a protein of unknown function . Interestingly all mutations tested , which occurred in regulatory regions , either up-regulated the targeted gene directly , or downstream genes in the regulatory cascade . The fact that E . coli adapted to the gut environment by up-regulating ( directly or indirectly ) nutrient transport functions bears an interesting resemblance to the observations of microbes adapting to limiting nutrient conditions in chemostats [42] . For instance , E . coli adapted to low lactulose chemostats by duplicating the lac operon or abolishing its regulation [43] and Sacharomyces cerevisae adapted to glucose-limited conditions by amplifying a region including high affinity transporters [44] . Similarities between adaptation in the gut and in chemostats maybe expected to some extent , given that the later were built for continuous culture , where the flow is controlled by a peristaltic pump mimicking digestive transit . Furthermore , chemostats enable conditions to be set to ensure a slow growth rate by manipulating the concentration of the limiting nutrient , perhaps simulating the conditions experienced in the gut by E . coli [22] . Remarkably , in chemostats with limiting glucose concentrations , a great amount of clonal interference and mutations exhibiting frequency dependence was shown to occur [45 , 46] . Epistasis is an important factor that can impose strong constraints on the adaptive process of bacterial populations [47 , 48] . This has been extensively studied in vitro [29 , 30 , 49 , 50] but only rarely in vivo [51] . In the context of bacterial evolution in the gut we evaluated whether epistasis contributed to the order of the adaptive steps that characterized E . coli evolution during colonization of the mouse intestine . Our setup provides an ideal situation to address this question , since the order of adaptive events was absolutely conserved with a single universal first adaptive phenotype . One possible explanation for the order of events could result from the second mutations being weaker or even deleterious in the ancestral genotype . In fact we estimated that at least one third of the second step mutations were as strong as those responsible for the first step , showing that strong effect mutations were still available for adaptation . However , many showed a smaller average effect ( mean s ( oppB , yjjP , , dcuB , focA ) = 0 . 03 ) . Therefore , it is possible that the large effect size of the first-step may have contributed for the order observed . A much more likely explanation can , however , be provided by differences in mutation rate . Indeed , we found that the gat operon is inactivated at a much higher rate than a random locus ( 10−5 versus 3 . 6x10-8 ) . This fact very likely contributed to the 100% parallelism observed at the phenotypic level , inactivation of the galactitol metabolism , despite the much reduced parallelism at the genetic level [8] . Besides the gat , shown here , other metabolic operons have been previously found to be mutational hotspots . One example is the ribose operon which was the most common target in one experiment of E . coli adaptation to glucose minimal medium [52] . Inactivation of this operon occurred at a high rate ( ~5 x 10−5 per genome , per duplication ) , similar to that of gat . However it conferred a 1–2% benefit to E . coli growing on glucose , considerable smaller than the 8% benefit conferred by gat inactivation in E . coli colonizing the mouse gut . Overall we present some of the first quantitative estimates of the fitness effects of beneficial mutations occurring in bacteria colonizing a natural ecosystem , where both ecological and evolutionary processes occur at fast time scales . The data obtained establish that strong effect beneficial mutations exhibiting all classical forms of natural selection shape the genetic diversity of a commensal species inhabiting the mouse gut microbiota . They show that , even in mammalian hosts with identical genetic backgrounds and diets , reproducible adaptations emerge albeit with a significant level of host individuality .
All experiments involving animals were approved by the Institutional Ethics Committee at the Instituto Gulbenkian de Ciencia ( project nr . A009/2010 with approval date 2010/10/15 ) , following the Portuguese legislation ( PORT 1005/92 ) , which complies with the European Directive 86/609/EEC of the European Council . All strains used were derived from MG1655 , a K12 commensal strain of Escherichia coli [53] . Strains JB19-YFP and JB18-CFP ( MG1655 , galK::YFP/CFP cmR , strR ( rpsL150 ) , ΔlacIZYA , Ins ( 1bp ) gatC ) were used for the evolution experiment here reported . These strains differ from the ancestral MG1655 fluorescent strains DM08-YFP and DM09-CFP used in a previous evolution experiment ( MG1655 , galK::YFP/CFP ampR , strR ( rpsL150 ) , ΔlacIZYA ) [9] ) by a mutation in the gatC gene ( 1bp insertion in the coding region ) . To construct these strains the ampicillin resistance cassette in the ancestral strains DM08-YFP and DM09-CFP was replaced with a chloramphenicol resistant cassette using the Datsenko and Wanner method . The yellow ( yfp ) and cyan ( cfp ) fluorescent genes linked to cmR were then transferred by P1 transduction to a derivative of clone 12YFP [8] , an evolved clone of DM08-YFP , isolated after 24 days of adaptation in the gut of WT mice , that carried an insertion of 1bp in gatC and a large duplication . During the genetic manipulations the large duplication was lost , confirmed by whole genome sequencing , leaving this clone with a single mutation , 1bp insertion in gatC . To measure the effect of the identified parallel mutations in gene expression we tested the following clones isolated from the evolution experiment reported here ( see S1 Table ) : 18YFP ( focA srlR ) , 22YFP ( dcuB ) , 25YFP ( yjjP/yjjQ radA insX-insA ) , 29CFP ( arcA ) and the ancestral strains DM08-YFP and DM09-CFP . Five biological replicates from each clone were performed for both aerobic and anaerobic conditions . To directly measure the effects of the mutations involved in the 2nd step of adaptation ( in the gat-positive background ) through in vivo fitness assays we used several single mutant clones isolated from a previous evolution experiment [8] ( 5YFP ( srlR ) and 6YFP ( dcuB ) ) or the experiment here reported ( 17YFP ( arcA ) , and 2 clones isolated from population 2 . 14 screened by PCR for mutation at focA and yjjP locus , respectively ) . The oppB single mutant was obtained by transducing the knockout of this gene ( oppB::Kan ) from the KEIO collection [54] to the ancestral YFP background . These clones , initially carrying an additional mutation in one of gat operon genes ( gatA or gatC ) , were made single mutants by P1 transduction from JW2074 ( gatR::Kan ) , a gatR knockout mutant from the KEIO collection . gatR is already interrupted by a transposable element in the ancestral MG1655 strain and therefore transduction with P1 from a mutant strain gatR , results in the effective replacement of the neighbor genes of the gat operon to their wild type status , while maintaining a knockout mutation in gatR . As reference strains , we used derivatives of the ancestral DM08-YFP and DM09-CFP strains in which the gatR gene was replaced with gatR::Kan . All mutants used to test the effect of the 2nd step mutations in the gat-negative background were derived from the collection of mutants constructed to test the effect of the same mutations in the gat-positive background . These mutants , except for srlR , were made gat-negative by P1 transduction of the gatC allele present in the ancestor of the 2nd colonization . Selection of gat-negative mutants was achieved by incorporating 10mM of D-Arabitol in the selection plates . All clones were confirmed to have the intended gatC allele by target PCR and restriction analysis as described in [8] . The srlR mutant was obtained by transduction of the gatC::Kan from the KEIO collection and was competed against a reference strain with the same gatC::Kan . To test for FDS we competed the single srlR mutant previously mentioned ( derived from clone 5YFP but with a wild type gat operon ) , against the ancestral strain DM09 ( MG1655-CFP ) . We also competed the clones 18YFP ( gatC focA srlR ) and 27CFP ( gatC oppB srlR ) against the reference strains JB18-CFP ( gatC ) and JB19-YFP ( gatC ) , respectively . In order to test if clones evolved in vivo had a different growth ability we performed in vitro competitions against the ancestral strains DM08-YFP and DM09-CFP of clones isolated from 14 independent populations from a previous evolution experiment ( sequenced clones 1 to 14 from populations 1 . 1 to 1 . 14 ) and this evolution experiment ( sequenced clones 16 to 30 , from populations 2 . 1 to 2 . 15 , S1 Table ) . In vitro competitions to measure the effect of the gat operon and nfsA gene inactivation were performed using the clones 4YFP [8] and a clone obtained by P1 transduction of nfsA::Kan ( from the KEIO collection ) , DM08-YFP and DM09-CFP . Mutant clones were constructed in the two fluorescent backgrounds and competed against the ancestors DM08-YFP or DM09-CFP , depending on the fluorescent background . To distinguish between gat-negative and gat-positive bacteria we used the differential medium MacConkey agar supplemented with galactitol 1% and streptomycin ( 100μg/ml ) . Plates were incubated at 30°C . The frequency of galactitol mutants was estimated by counting the number of white ( auxotrophic for galactitol ) and red colonies . To perform the fluctuation test for the gat-negative phenotype we used the selective M9 Minimal Medium ( MM ) agar , supplemented with D-arabitol ( 10mM ) and glycerol ( 0 . 4% ) or Luria Broth ( LB ) agar supplemented with furazolidone ( 1 . 25 μg/ml ) . For the in vitro competition assays we used MM supplemented with 3mM of MgSO4 and either sorbitol , ribose , mannose , gluconate or glucuronate at a concentration of 0 . 02% . Additionally a mixture with the different carbon sources ( composed of 0 . 1% from each of the five carbon sources ) was also used . In order to study E . coli´s adaptation to the gut we used the classical streptomycin-treated mouse colonization model [55] and performed the evolution experiment using the same conditions as before [8] . Briefly , 6- to 8-week old C57BL/6 male mice raised in specific pathogen free ( SPF ) conditions were given autoclaved drinking water containing streptomycin ( 5g/L ) for one day . After 4 hours of starvation for water and food , the animals were gavaged with 100μl of a suspension of 108 colony forming units ( CFUs ) of a mixture of MG1655-YFP-gatC and MG1655-YFP-gatC bacteria ( ratio 1:1 ) grown at 37°C in brain heart infusion medium to OD600 of 2 . After the gavage , all the animals were housed separately and both the water with streptomycin and the food were returned to them . Mice fecal samples were collected for 24 days and diluted in PBS , from which a sample was stored in 15% glycerol at -80°C and the remaining was plated in Luria Broth agar supplemented with streptomycin ( LB plates ) . Plates were incubated overnight at 37°C and then with the help of a fluorescent stereoscope ( SteREO Lumar , Carl Ziess ) the fluorescent colonies were counted to assess the frequencies of CFP- and YFP-labelled bacteria . These fluorescent proteins are used as neutral markers with which we can follow the appearance of beneficial mutations , since these markers hitchhike with the beneficial mutations that spread in the populations [9] . To test the in vivo advantage of 12 clones carrying the 2nd step mutations in ( n = 4 per clone ) ( Figs 4 and 5 ) and 3 clones carrying the srlR mutation ( n = 3 per clone ) we performed competitive assays against the respective ancestor labelled with the opposite fluorescent marker . In vivo competitions were performed at a ratio of 1 to 1 for all clones except the ones where we tested for FDS . To test for FDS we performed ratios of 1:9 and 9:1 , following the same procedure described above for the evolution experiment . Mice fecal pellets were collected daily , diluted in PBS and frozen in 15% glycerol at -80°C . Total numbers and relative proportions of YFP- and CFP-labeled E . coli were subsequently determined by plating appropriate dilutions in either LB agar supplemented with streptomycin ( 100 μg/ml ) or MacConkey supplemented with streptomycin ( 100 μg/ml ) and galactitol 1% . After overnight incubation at 30°C , the colonies were screened for the gat phenotype , based on their white or red color . In addition , CFP- and YFP-labelled bacteria were counted with a fluorescent stereoscope ( SteREO Lumar , Carl Ziess ) . The selection coefficient ( fitness gain ) of the clones in vivo ( presented in Fig 1 ) was estimated as: sb=ln ( Rfev/ancRiev/anc ) /t , where sb is the selective advantage of the evolved clone , Rfev/anc and Riev/anc are the ratios of evolved to ancestral bacteria in the end ( f ) or in the beginning ( i ) of the competition and t is the number of generation per day . We assumed t = 18 , in accordance with the 80 minute generation time estimated in previous studies on E . coli colonization of streptomycin-treated mouse [56 , 57] . To test whether E . coli clones evolved in vivo had a different nutritional profile when growing in vitro , we performed competitions between each of the sequenced clones ( obtained both from [8] and from the present work ) and the ancestor of the first colonization of the opposite fluorescence . Competitions were performed in triplicate , in MM supplemented with different carbon sources and in two different oxygen conditions . All competitions were conducted in 96-well plates incubated at 37°C ( Thermoshaker PHMP-4 , Grant ) under aerobiosis or in an anaerobic chamber ( anaerobiosis ) . The competitor and reference strains were initially acclimated to the growth media for two overnights in MM supplemented with glycerol ( 0 . 02% ) and then 105−106 cells of both strains inoculated in MM containing 0 . 02% of either sorbitol , ribose , mannose , gluconate or glucuronate or a mixture of all five carbon sources at individual concentrations of 0 . 01% . The strains were allowed to compete for 24 hours and the initial and final ratios of both strains were determined by flow cytometry , using a BD LSRFortessa ( BD Biosciences ) cytometer . The relative fitness of the evolved clones ( S2 Table ) was estimated as previously described ( see “In vivo competitive assays” above ) . Competitions in anaerobic conditions were performed for each of the evolved clones following the protocol above described but with the following alterations: after an initial aerobic growth overnight in MM with glycerol ( 0 . 02% ) , the cultures were diluted 10-fold , inoculated in MM with glycerol and acclimated overnight by incubation in an anaerobic chamber ( 5% H2 , 15% CO2 , 80% N2 ) ( Plas Labs , Lansing , MI , USA ) , at 37°C . After acclimatization , the competitor and reference strain were inoculated in MM supplemented with individual or a mixture of carbon sources and allowed to compete for 48 hours . To determine the initial and final ratios of the competing strains , serial dilutions of the mixtures were plated in LB supplemented with streptomycin ( 100μg/ml ) and the resulting CFUs counted in a stereoscope ( SteREO Lumar , Carl Zeiss ) . Clone analysis: After 24 days of colonization one clone from each independently evolving populations ( 2 . 1 to 2 . 15 ) was isolated and grown in 10 mL of LB at 37°C with agitation for DNA extraction ( following a previously described protocol [58] ) . The DNA library construction and sequencing was carried out by BGI . Each sample was pair-end sequenced on an Illumina HiSeq 2000 . Standard procedures produced data sets of Illumina paired-end 90 bp read pairs with insert size ( including read length ) of 470 bp . Mutations were identified using the BRESEQ pipeline [59] . To detect potential duplication events we used ssaha2 [60] with the paired end information . This is a stringent analysis that maps reads only to their unique match ( with less than 3 mismatches ) on the reference genome . Sequence coverage along the genome was assessed with a 250 bp window and corrected for GC% composition by normalizing by the mean coverage of regions with the same GC% . We then looked for regions with high differences ( >1 . 4 ) in coverage . Large deletions were identified based on the absence of coverage . For additional verification of mutations predicted by BRESEQ , we also used the software IGV ( version 2 . 1 ) [61] . Data presented in S1 Table . Population analysis: DNA isolation was obtained in the same way as described above for the clone analysis except that now it derived from a mixture of >1000 clones per population grown in LB agar . Two populations , from the evolution experiment , were sequenced: 2 . 7 and 2 . 10 . Those were sequenced for three time points during the adaptive period ( generation 198 ( day11 ) , generation 306 ( day17 ) and generation 432 ( day24 ) ) . The DNA library construction and sequencing was carried out by the IGC genomics facility . Each sample was pair-end sequenced on an Illumina MiSeq Benchtop Sequencer . Standard procedures produced data sets of Illumina paired-end 250 bp read pairs . The mean coverage per sample was between ~90x and ~150x for population 2 . 7 and between ~100x and ~120x for population 2 . 10 . Mutations were identified using the BRESEQ pipeline ( version 0 . 26 ) with the polymorphism option on . The default settings were used except for: a ) requirement of a minimum coverage of 3 reads on each strand per polymorphism; b ) eliminating polymorphism predictions occurring in homopolymers of length greater than 3; c ) polymorphism predictions with significant ( P = 0 . 05 ) strand or base quality score bias were discarded . Data presented in S3 Table . To determine the effects of the IS insertions identified during the 2nd steps of adaptation we measured the expression of focA , dcuB , arcA , yjjY and yjjP by RT-qPCR in two environments with different levels of oxygen . Five biological replicates and three technical replicates per clone were performed . Aerobic Conditions: The clones were initially grown for 24h at 37°C with aeration in MM with glycerol ( 0 . 02% ) . The cultures were diluted 10-fold and 100 μl of the dilution were inoculated in in 10ml of M9 minimal medium ( MM ) supplemented with a mixture of the following carbon sources: sorbitol , ribose , mannose , gluconate and glucoronate , at individual concentration of 0 . 01% . The cultures were grown at 37°C , with aeration , until an OD600 of 0 . 5 . Five milliliters of the bacterial culture were then harvested by centrifugation at 4°C for 5 minutes at the maximum speed . The resulting pellet was ressuspended in lysozyme solution ( 5 mg lysozyme /ml DEPC treated water , Sigma protocol ) and incubated at 37°C for 30 minutes , promoting disruption of the bacterial cell wall and allowing for RNA extraction ( see below ) . Anaerobic conditions: The protocol used was the same as in the aerobic conditions with the following alterations: the second overnight growth was performed at 37°C in an anaerobic chamber with the atmosphere of 5% H2 , 15% CO2 , 80% N2 ( Plas Labs , Lansing , MI , USA ) , and at approximately OD600 of 0 . 2 the cultures were placed in dry ice to prevent their growth and the cells were harvested by centrifugation from 10 ml of bacterial culture . RNA extraction , DNAse treatment , RT-PCR and qPCR: The RNA extraction was performed with the Qiagen RNeasy Mini Kit . RNA concentration and quality were evaluated with Nanodrop 2000 . DNase treatment was performed with the RQ1 DNase ( Promega ) , 0 . 5μl of DNase and 1μl buffer were added to 1μg of RNA and incubated for 30 minutes at 37°C . After this , 1μl stop solution was added and then incubated for 15 minutes at 65°C to inactivate the DNase . The resulting RNA was used for the reverse transcription which consisted in mixing with 1μg of RNA , with 0 . 5μl random primers and DEPC-water ( final volume of 15μl ) and then incubated at 70°C for 5min . Afterwards the M-MLV Reverse Transcriptase Protocol ( Promega ) were performed , to the first mix was added 5 μl of RT buffer , 0 . 5μl RT enzyme and 2μl dNTP mix , and then incubated 10 min at 25°C , 50min at 50°C and 10 min at 70°C . We used a relative quantification method of analysis with normalization against a reference gene . qPCR was executed in BioRad CFX 384 with itaq universal sybr green supermix ( BioRad ) . cDNA was diluted 100-fold before used in the qPCR . The qPCR reaction conditions were as follows: one cycle of 2 min at 50°C and then 39 cycles of 10 min at 95°C , 30 sec at 95°C , 1 min at 57°C and finally 30 s at 72°C . Primers used are listed in S5 Table . Melting curve analysis was performed to verify product homogeneity . All reactions included three replicates for each sample . Data were normalized by the Pfaffl method [19] using the hfq housekeeping gene of E . coli as a reference . To test for the possibility of a difference in the mutation rate of the galactitol operon , we determined the frequency of spontaneous gat-negative phenotype mutants when plated on D-arabitol . D-arabitol is known to be toxic for bacteria that are able to metabolize galactitol ( gat-positive phenotype ) [62] and so the growth of gat-positive bacteria is much slower , allowing to differentiate between gat-positive and gat-negative clones . The ancestral strains DM08-YFP and DM09-CFP were grown overnight in 10 ml of LB at 37°C with aeration . After growth , the total number of cells in the cultures was measured using BD LSR Fortessa ( BD Biosciences ) and approximately 1000 cells were used to inoculate 1 ml of LB ( 10 replicates of each strain ) and incubated overnight . Aliquots of each replicate tube were plated in LB agar and MM agar supplemented with D-arabitol ( 10 mM ) and glycerol ( 0 . 4% ) and incubated overnight at 37°C . The number of spontaneous gat-negative mutants and total number of cells grown on LB were used to estimate the mutation rate using the maximum likelihood approach as implemented in FALCOR [25] . Similarly , a fluctuation assay for measuring the spontaneous rate of emergence of furazolidone resistant mutants was used as proxis for the spontaneous rate of random gene inactivation . We then used this number to compare with the rate for gat-negative phenotype . The experiment was performed in the same conditions as described above except that the cultures were plated in LB supplemented with furazolidone ( 1 . 25μg/ml ) . In order to estimate the haplotype frequencies depicted in Fig 6A and 6B two complementary strategies were employed . In addition to the WGS of the populations , targeted PCR of the identified parallel mutations was performed . For the targeted PCR , 20 to 80 clones from different time points were screened ( from populations 2 . 7 and 2 . 10 ) using the same primers and PCR conditions as in [8] . Because all target mutations correspond to IS insertions an increase in the PCR band is indicative of the presence of an IS . Frequencies are depicted in S3 Table . To determine significant differences in gene expression between mutant and ancestral strain , the unpaired t test was used , with a significance defined as P value of <0 . 05 . Differences in the selective advantage of clones competed in vitro against the ancestral were evaluated with paired one-tailed distribution t test , with a significance defined as P value of <0 . 05 . All statistical analysis were conducted with the statistical software R [63] . Genome sequencing data have been deposited in the NCBI Read Archive , http://www . ncbi . nlm . nih . gov/sra ( accession no . SRP063701 ) .
|
The relative contribution of random loss and migration versus de novo mutation to the overall diversity of the gut microbiota is far from understood . Population sizes of bacterial communities inhabiting the gut can be very large and therefore , both weak and strong effect beneficial mutations theoretically have the opportunity to contribute to adaptation . Here , by discretizing the adaptive steps that occur during colonization by a gut commensal , we uncover a mutational hotspot , large effect mutations and different forms of natural selection as the core ingredients to the emergence of diversity in this environment . We show that , on occasion , strong periodic selection can create and reduce diversity but then recurrent mutation generates new adaptive variants that compete for fixation . Selection for keeping particular adaptive variants at low frequency , balancing selection , was also shown to be pervasive . Unexpectedly , given the complexity of the gut ecosystem , we find a highly repeatable evolutionary process , motivated by a mutational hotspot and strong effect adaptive mutations continuously recurring in commensal bacterial species .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
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"organismal",
"evolution",
"deletion",
"mutation",
"insertion",
"mutation",
"population",
"genetics",
"microbiology",
"operons",
"cloning",
"mutation",
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"techniques",
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] |
2016
|
A Mutational Hotspot and Strong Selection Contribute to the Order of Mutations Selected for during Escherichia coli Adaptation to the Gut
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Over 112 million people worldwide are infected with Schistosoma haematobium , one of the most prevalent schistosome species affecting humans . Female genital schistosomiasis ( FGS ) occurs when S . haematobium eggs are deposited into the female reproductive tract by adult worms , which can lead to pelvic pain , vaginal bleeding , genital disfigurement and infertility . Recent evidence suggests co-infection with S . haematobium increases the risks of contracting sexually transmitted diseases such as HIV . The associated mechanisms remain unclear due to the lack of a tractable animal model . We sought to create a mouse model conducive to the study of immune modulation and genitourinary changes that occur with FGS . To model FGS in mice , we injected S . haematobium eggs into the posterior vaginal walls of 30 female BALB/c mice . A control group of 20 female BALB/c mice were injected with uninfected LVG hamster tissue extract . Histology , flow cytometry and serum cytokine levels were assessed at 2 , 4 , 6 , and 8 weeks post egg injection . Voiding studies were performed at 1 week post egg injection . Vaginal wall injection with S . haematobium eggs resulted in synchronous vaginal granuloma development within 2 weeks post-egg injection that persisted for at least 6 additional weeks . Flow cytometric analysis of vaginal granulomata revealed infiltration by CD4+ T cells with variable expression of the HIV co-receptors CXCR4 and CCR5 . Granulomata also contained CD11b+F4/80+ cells ( macrophages and eosinophils ) as well as CXCR4+MerTK+ macrophages . Strikingly , vaginal wall-injected mice featured significant urinary frequency despite the posterior vagina being anatomically distant from the bladder . This may represent a previously unrecognized overactive bladder response to deposition of schistosome eggs in the vagina . We have established a new mouse model that could potentially enable novel studies of genital schistosomiasis in females . Ongoing studies will further explore the mechanisms by which HIV target cells may be drawn into FGS-associated vaginal granulomata .
An estimated 240 million humans worldwide have schistosomiasis , an infection by Schistosoma worms of various species [1] . Human infection begins when aquatic cercariae found in contaminated water penetrate intact skin . Once in the human host , these cercariae migrate into the circulation as schistosomula where in the portal vein they mature into adult worm mating pairs and then migrate to various venous plexi [2] . Three species of Schistosoma are primarily responsible for human disease , and Schistosoma haematobium contributes to over half of all cases of schistosomiasis [3] . With S . haematobium infection , worms can live and lay eggs for an average of 3 . 4 years [4] . When S . haematobium eggs deposit along the female genitourinary tract such as the urinary bladder , lower ureters , cervix and vagina , girls and women can experience hematuria , dysuria , urinary frequency , and an increased risk of bladder cancer [5] . However , S . haematobium infection is postulated to also cause dyspareunia , vaginal bleeding , pruritis , and giant granulomata that appear as tumors in the female genital tract [6] . Collectively , these signs and symptoms are termed female genital schistosomiasis ( FGS ) [7] . Recent studies suggest that FGS may cause women to be more susceptible to human immunodeficiency virus ( HIV ) infection [8]–[10] and those girls and women with FGS may have a 3-fold increased risk of contracting HIV [11] . Unfortunately , the pathophysiology of this co-infection is not well understood . Several studies have indicated , however , that other female genital infections , such as syphilis , human papilloma virus , and chlamydia , may increase the risk of HIV transmission [12] , [13] . Genital infections that produce ulcers or vaginal discharge likely have the greatest impact on HIV shedding . This may be due to high concentrations of leukocytes in the genital tract , for example , during gonorrheal or chlamydial infections , that thereby lead to greater HIV shedding [14] . Syphilis is also associated with increased HIV shedding in the blood as well as genital tract [15] . Clinical features of FGS , including vascularized , “sandy patches” of disrupted vaginal mucosa which are susceptible to contact bleeding , likely promote viral transmission through sexual contact [9] , [16] , [17] . These lesions arise from an inflammatory response to deposited S . haematobium eggs , and contain inflammatory infiltrates , which may provide the optimal milieu for HIV transmission [18] . Eggs can trigger a significant immune response , including primarily Th2-skewed systemic immune deviation [19] , [20] as well as the formation of egg-based granulomata . [19] , [20] . Accordingly , an additional hypothesis for the increased HIV susceptibility of S . haematobium-infected girls and women ( besides contact bleeding of genital lesions ) postulates that S . haematobium infection results in systemic immune deviation which renders affected individuals more vulnerable to HIV infection . A third hypothesis for the enhanced HIV susceptibility of girls and women with FGS is that the close proximity of large numbers of granuloma-associated CD4+ T cells , macrophages , and dendritic cells ( so-called HIV target cells ) to infected genital tissues creates a convenient portal for HIV entry [21] . Currently there are no relevant animal models to study FGS-related pathology . Many questions remain regarding the mechanisms responsible for the genitourinary symptoms and possible increased rates of HIV transmission associated with FGS . Knowing the kinetics of how rapidly HIV target cells accumulate in FGS lesions is directly relevant to HIV prevention strategies for women and girls at risk of HIV . This in turn may drive the development of therapeutic interventions capable of limiting the immune and tissue pathology responsible for FGS-related sequelae . Unfortunately , natural transdermal infection of mice with S . haematobium cercariae results in hepatoenteric disease and very little if any pelvic organ pathology [22] , [23] . Since the immune response is primarily directed against S . haematobium eggs , and not as prominently to other stages of the parasite lifecycle , we previously developed a mouse model of S . haematobium egg-induced bladder disease by direct injection of S . haematobium eggs into the mouse bladder wall [24] . This model recapitulates multiple aspects of human urinary schistosomiasis-associated bladder disease , including urinary frequency , hematuria , granuloma formation , and systemic immune responses . Although , akin to oviposition in the bladder wall , the morbidity associated with FGS infection is strongly associated with egg deposition into the vagina and cervix , it is currently unclear whether oviposition alone , in the absence of adult worms , is sufficient to induce vaginal pathology . This is relevant to girls and women who have cleared S . haematobium infections through drug therapy or natural immunity and yet still have parasite eggs in their reproductive tracts . To address this issue , we directly microinjected viable S . haematobium eggs into the vaginal walls of female BALB/c mice . Our overall aim was to create a mouse model to study FGS .
All animal work was conducted according to relevant U . S . and international guidelines . Specifically , all experimental procedures were carried out in accordance with the Administrative Panel on Laboratory Animal Care ( APLAC ) protocol and the institutional guidelines set by the Veterinary Service Center at Stanford University ( Animal Welfare Assurance A3213-01 and SDA License 93-4R-00 ) . Stanford APLAC and institutional guidelines are in compliance with the U . S . Public Health Service Policy on Humane Care and Use of Laboratory Animals . The Stanford APLAC approved the animal protocol associated with the work described in this publication . A total of 50 mice were used for the experiment ( 30 egg- and 20 vehicle-injected controls ) . Seven to eight week old female BALB/c mice were purchased from Jackson Laboratories and housed in the Veterinary Service Center at Stanford University . S . haematobium-infected LVG hamsters were obtained from the National Institute of Allergy and Infectious Diseases Schistosomiasis Resource Center of the National Institutes of Health . The hamsters were sacrificed at the point of maximal liver and intestinal Schistosoma egg levels ( 18 weeks post-egg injection [25] , at which time livers and intestines were minced , homogenized in a Waring blender , resuspended in 1 . 2% NaCl containing antibiotic-antimycotic solution ( 100 units penicillin , 100 µg/mL streptomycin and 0 . 25 µg/mL amphotericin B , Sigma-Aldrich ) , passed through a series of stainless steel sieves with sequentially decreasing pore sizes ( 450 µm , 180 µm , and 100 µm ) , and finally retained on a 45 µm sieve . Control injections were performed using similarly prepared liver and intestine lysates from age-matched , uninfected LVG hamsters ( Charles River Laboratories ) . Seven to eight week old female BALB/c mice were anesthetized with isoflurane . Freshly prepared S . haematobium eggs ( 1 , 000 eggs in 50 µl of phosphate-buffered saline , experimental group ) or uninfected hamster liver and intestinal extract ( in 50 µl of phosphate-buffered saline , control group ) was injected submucosally into the mouse posterior vaginal wall at 6 o'clock over 5 seconds ( Figure 1 ) . For mice undergoing sacrifice for flow cytometry and Luminex experiments , additional eggs ( 1 , 000 eggs in 50 µl of phosphate-buffered saline , experimental group ) were injected at the 3 , 6 , and 9 o'clock positions into the mouse posterior vaginal wall . By both observation of miracidial activity and hatch testing a high proportion of viable eggs was confirmed for each batch of eggs injected . All egg injections were performed within 8–10 hours of egg isolation . ( >70% of eggs from by each batch were confirmed to be viable through observation of motile miracidia within eggs and successful hatch tests ) . Voided spot on paper analysis was performed as previously described [26] . In brief , mice underwent vaginal injection with either eggs ( n = 15 ) or control vehicle ( n = 5 ) . One week later , mice were housed singly and acclimated for one hour in cages lined with filter paper laid underneath a wire floor bottom . Animals were given ad libitum access to food and water-soaked sponges placed on wire cage covers . After 8 hours , each piece of filter paper was photographed under ultraviolet light to localize voided urine spots . Total spots were counted for each mouse and the average number of voids were compared between the egg- and vehicle-injected mice using two-tailed T-tests . Mice were sacrificed at serial time points 2 , 4 , 6 and 8 weeks after vaginal injection and the vaginas , cervices , and bladders processed for routine histology . Step sectioning was performed by alternating between discarding and analyzing 10 sequential 5 micron sections . Morphologic analyses were conducted on hematoxylin and eosin- stained sections . The entire vagina , bladder , and cervix of each mouse was processed , sectioned , and examined for pathology . Each mouse vagina was dissected in its entirety from the introitus up to the level of the cervix . The posterior cul de sac was separated from adjacent adipose tissue and skin with sharp dissection . The pubic bone was split and the vagina was gently removed from the pelvis by transecting it 5 mm below the cervix . Freshly excised vaginas were minced and incubated with agitation in 0 . 5% heat-inactivated FBS ( Thermo Scientific Hyclone , IL ) , 20 mM HEPES pH 7 , 125 U/ml ( 1 mg/mL ) collagenase VIII ( Sigma-Aldrich , Saint Louis , MO ) in RPMI 1640 medium for 1 hr at 37°C [27] . The tissue was then passed through a 70 µm nylon cell strainer to remove undigested tissue and macrocellular debris . A total of 106 cells/sample were treated with mouse anti-CD16/CD32 ( clone 2 . 4G2 , BioLegend , San Diego , CA ) for 20 min and stained with surface markers of lymphocyte lineages [mouse anti–CD3-APC-Cy7 ( clone 17A2 , BD Pharmingen , San Diego , CA ) , anti–CD4-Pacific Blue ( clone RM4-4 , BioLegend ) , anti–CD8a-Alexa Fluor 647 ( clone 53-6 . 7 , BioLegend ) , anti-CCR5-PE ( clone HM-CCR5 , BioLegend ) and anti-CXCR4-PerCP efluor 710 ( clone 2B11 , eBioscience , San Diego , CA ) ]; or surface markers of myeloid lineage [anti-F4/80-FITC ( clone BM8 , Biolegend ) , anti–CD11b-APC-Cy7 ( clone M1/70 , BioLegend ) , anti–CD11c- Pacific Blue ( clone N418 , BioLegend ) , anti-CD64 ( clone X54-5/7 . 1 , BioLegend ) , anti-CXCR4-PerCP efluor 710 and anti-MerTK ( clone AF591 , R&D Systems , Minneapolis , MN ) with anti-goat-IgG-APC ( R&D systems ) ] for 30 minutes at 4°C . Flow cytometry was performed using a BD LSRII flow cytometer and BD FACS Diva software . To ascertain whether the presence of S . haematobium eggs would induce a systemic immune response we performed serum cytokine assays . Serum samples were assayed using a mouse 26-plex cytokine kit ( Affymetrix , Santa Clara , CA ) according to the manufacturer's instructions . Samples were read using a Luminex 200 ( Luminex , Austin , TX ) with a lower cut off of 100 beads per sample ( Human Immune Monitoring Core , Stanford University ) . Assayed proteins analyzed included: IL-1α , IL-1 β , IL-2 , IL-3 , IP10 , IL-4 , IL-5 , IL-6 , IL-10 , TGF-β , IL-12p40 , IL-12p70 , IL-17 , IL-13 , KC , IL-23 , RANTES , IFN-γ , GM-CSF , TNF-α , G-CSF , MIP-1α , MCP-3 , eotaxin , MCP-1 , and VEGF . Flow cytometric data were analyzed using FlowJo v7 . 2 . 4 ( Tree Star , Ashland , OR ) . An unpaired Mann-Whitney U test was used to analyze flow cytometric data and Luminex analysis between control- and egg-injected mice at each time point . Data were expressed as medians . A p value of <0 . 05 was considered statistically significant .
Over 8 weeks , the egg-associated mixed inflammatory infiltrate expanded and organized into well-defined granulomata surrounded by peripheral eosinophils and neutrophils and containing a diffuse , peripheral lymphocytic infiltrate ( Figures 2–5 ) . This is consistent with our flow cytometry data , which demonstrated an initial increase in numbers of T-cells followed by a later expansion of the macrophage pool . Intact granulomata were still present 8 weeks after egg injection . Interestingly , disruption of the vaginal mucosa , was not observed in our model . We also did not appreciate any pathology in the mouse cervix on H&E ( data not shown ) . Accordingly , levels of keratinization and the thickness and integrity of the vaginal mucosa showed no difference in egg-injected mice compared to controls ( Figure 6 ) . Histologically we have identified intact miracidia within eggs at least two weeks after injection into mouse tissue ( data not shown ) . This suggests that eggs remain viable for a period after injection into mouse vaginal submucosal tissues . Vaginal submucosal S . haematobium egg injection induced urinary frequency with an increase in the number of urinary voids ( median number = 5 ) relative to vehicle-injected animals ( median number = 2; p = 0 . 0423 ) ( Figure 7 ) . Given the association between FGS and HIV transmission we sought to characterize potential HIV target cell populations and their HIV co-receptor ( CCR5 and CXCR4 ) surface expression in vaginal tissue from S . haematobium egg-injected mice . Total T-cell subsets were defined by surface expression of CD3 , and then further categorized by the surface expression of CD4 , CD8 , CCR5 , and CXCR4 . Macrophage populations were defined by the surface markers CD11b , F4/80 , MerTK , and CD64 , and further characterized by CXCR4 expression . Egg-injected vaginal tissue contained significantly higher numbers of both CD4+CCR5+ T cells ( p = 0 . 0079 ) and CD4+CXCR4+ T cell ( p = 0 . 0079 ) populations by week two post-egg injection ( Figure 8A ) . Egg-injected vaginal tissue also had greater numbers of T cells , CD4+CXCR4+ T cells , and CD4+CCR5+ T cells throughout the 8 week time course , though these trends were not statistically significant . An increased number of macrophages expressed the HIV co-receptor CXCR4 in egg-injected mice at week 6 post-egg injection ( p = 0 . 0173 ) , compared to vehicle-injected mice ( Figure 8B ) . Macrophage numbers were increased in egg-injected vaginal tissue at week 4 ( p = 0 . 043 ) and 6 ( p = 0 . 0303 ) compared to vehicle-injected tissue ( Figure 8C ) . RANTES protein levels were increased at 2 weeks post-egg injection ( median 23 . 01 pg/ml ) compared to vehicle-injected ( median 11 . 23 pg/ml , p = NS ) . RANTES protein levels decreased at 4 weeks post egg-injection ( median 13 . 08 pg/ml ) compared to vehicle controls ( median 10 . 5 pg/ml ) p = NS . There were no differences in any other assayed cytokine in egg- versus control-injected mice at 2 and 4 weeks post-egg injectionnjection ( data not shown ) .
We present a mouse model of female genital schistosomiasis amenable to the study of immune modulation and genitourinary changes that occur with S . haematobium egg exposure . This model did result in an increase in numbers of potential HIV target cells in egg-injected mice . The presence of S . haematobium eggs in the vagina did not induce significant shifts in the overall systemic immune response . We also detected an increase in urinary frequency in S . haematobium egg-injected mice . Besides identifying increased urinary frequency , we also found vaginal granuloma formation in mice after S . haematobium egg injection as early as 2 weeks post-egg injection . The vaginal lesions we describe herein feature cellular infiltrates that differ in composition from those seen in the mouse bladder wall egg injection mouse model . In our model there is an increase in numbers of T cells at 2 weeks whereas in the bladder model there is an increase in numbers of eosinophils and B cells [24] . At 4 weeks , our model demonstrated an increase in numbers of macrophages whereas the bladder model found an increase in numbers of T cells , B cells and neutrophils [24] . We speculate that these differences exist because the resident leukocyte populations and lymphatic tissue organization of the vaginal submucosa is distinct from that of the bladder lamina propria . These differences likely guide any resulting leukocyte responses to S . haematobium egg exposure . Natural infection of experimental animals with S . haematobium cercariae can be inefficient and slow to evolve , often taking greater than 15 weeks and yielding low worm returns [28] . Bladder pathology in mice is infrequent and often is not seen until 20 weeks post-egg injection [29] . In contrast , non-human primate models of S . haematobium worm-based oviposition in the pelvic organs are more consistent . One study of S . haematobium-infected African baboons reported that their internal genitalia possessed tan , firm polypoid patches with diffuse infiltrate of eosinophils , macrophages , plasma cells and lymphocytes seen after infections of greater than 30 weeks of duration [30] . However , compared to use of experimental mice , the utilization of non-human primates in research is more expensive , fraught with more ethical concerns , and suffers from a lack of species-specific tools . To our knowledge , the work presented herein is the first mouse model to describe vaginal immune modulation by the presence of S . haematobium eggs . The granulomas we report are similar to those seen in human immunopathology , with recruitment of lymphocytes , macrophages , and eosinophils to egg-containing sites [31] , [32] . These inflammatory cells include CD4+ T-cells , which are the primary cellular targets for HIV . Given that HIV primarily infects CD4+ T cells and macrophages bearing the co-receptors CCR5 [33] and CXCR4 [34] , we sought to characterize potential HIV target cell populations in vaginal tissue from S . haematobium egg-injected mice by studying these specific co-receptors . Indeed , it has been previously demonstrated that schistosomal infection elevates expression levels of CCR5 and CXCR4 on peripheral CD4+ T-cells in Schistosoma mansoni-infected individuals , and biopsies of FGS lesions demonstrate increased numbers of both CD4+ T cells and macrophages [18] , [35] . Relative to controls , egg-injected vaginal tissue featured increased numbers of CD4+CCR5+ T cells and CD4+CXCR4+ T-cells out to 8 weeks . This could represent a shift from acute to chronic inflammation , induced in this synchronous model; however it is difficult to say with certainty that this is a chronic phenomenon . Nevertheless , the purported causal link between FGS and increased susceptibility to HIV transmission is a hypothesis and believed to be mechanistically multifactorial . Studies of other co-infections with HIV suggest other mechanisms for an increased susceptibility to HIV transmission [36]–[39] . One study in humans co-infected with chlamydia and HIV-1 reported that HIV replication increases in association with granulocyte generation of reactive oxygen species and increases in cytokine production ( based on in vitro assays ) may impact numbers of HIV-receptive cells [36] , [37] . Another study found that both native lipoprotein and synthetic lipopeptides derived from Treponema pallidum induced the production of HIV in a chronically infected cell line [38] . HIV-1 has also been found to utilize the host transcription factor NF-κB to drive viral gene expression in T . pallidum infected cells [39] . It is likely that schistosome-HIV co-infection may induce similar host inflammatory signaling cascades , and these additional mechanisms of enhanced viral replication and transmission warrant future exploration . In addition to co-infection associations , urogenital schistosomiasis is well-known to induce genitourinary symptoms . A recent study in an S . haematobium endemic area of South Africa found 35% of young girls between the ages of 10–12 reported urogenital symptoms associated with urinary schistosomiasis [40] . Symptoms included increased dysuria , burning sensation in the genitals , as well as stress and urge urinary incontinence [40] . While not all symptoms were statistically significant compared to girls without urinary schistosomiasis , infected girls reported increased episodes overall [40] . In our model , S . haematobium-injected mice were found to show signs of urinary frequency more often than control-injected mice . Step sectioning of pelvic organs by alternating between discarding and H&E staining 10 sequential 5 micron sections demonstrated that granulomas were restricted to the vaginal submucosa and did not migrate beyond to perivesical tissues . To our knowledge , this is the first report of FGS inducing urinary frequency in the absence of S . haematobium eggs in the bladder . Although egg injections were administered to the posterior vaginal wall ( 6 o'clock ) of infected mice , they were found to have an increase in the number of voids compared to controls . Several animal models have confirmed cross-organ sensitization among the lower urinary tract and gynecologic structures [41] , [42] . In an induced model of endometriosis , female rats were found to have bladder inflammation and urinary bladder hypersensitivity , reflected as a decrease in micturition thresholds [41] . A different study reported that uterine inflammation in female rats causes plasma extravasation , suggesting the existence of cross-organ inflammation [42] . Viscero-visceral referral and sensitization ( termed cross-organ sensitization ) has recently been described to include peripheral mechanisms [43] . This is likely due to neurons from the peripheral nervous system ( PNS ) that converge centrally in the spinal cord with input from the viscera , skin , muscles and blood vessels . A large number of spinal neurons are receptive to visceral afferents . There are no second order spinal neurons that specifically transmit visceral signals , thus leading to convergence of both somatic and visceral inputs into the same second order neurons [44] . Besides inducing genitourinary symptoms , FGS is widely regarded as an immunomodulating infection . We assessed a large cytokine panel and did not find that FGS induced broad , systemic immunomodulation in this mouse model . RANTES was the only chemokine found to be increased in S . haematobium egg-injected versus control- injected vaginal tissue , however this was a non-significant trend . We believe that RANTES could possible be elevated as an acute response to S . haematobium eggs . RANTES has previously been described to aid in immunity against HIV-1 by competing to bind to CCR5 . Sustained RANTES binding has been reported to chronically reduce cell surface levels of CCR5 [45] . A recent meta-analysis suggested that Asians with the RANTES -28G allele may have decreased susceptibility to HIV-1 infection [46] . Few studies have described the role of RANTES in schistosomiasis infection . One study reported a classification tree created from both factor analysis and risk analysis that showed high levels of TNF-α and low levels of RANTES in men were associated with a high risk of schistosomal liver fibrosis [47] . In our mouse FGS model , RANTES was found to have fallen by week 4 , which coincides with the post-granuloma formation period . A decrease in RANTES could potentially cause an increase risk of HIV transmission due to weakened immunity . To our knowledge , no study to date has reported on the relationship between RANTES and FGS . Although there are few animal models for S . haematobium infection in general , most existing models are of urinary schistosomiasis [30] , [48] . Given the large number of available mouse-specific tools , our model may aid the further investigation of FGS . FGS results from a very complex natural history that is challenging to replicate via transdermal infection of experimental animals with cercariae , the route of infection for humans . Instead , we have injected live eggs into the vagina and have confirmed similar granulomatous pathology seen in humans . We recognize there are limitations to this model , as it does not reproduce true disease in which ova migrate from the lumens of host blood vessels to the epithelial surface . Instead , our model generated oviposition in the vaginal submucosa , below the vaginal epithelium . Eggs were injected below the epithelial surface and did not migrate as seen in natural infection . We therefore did not find any vaginal mucosal lesions . Sandy patches on the cervix or vaginal mucosa are pathognomonic lesions associated with human FGS and are indicative of mucosal abnormalities [16] . Thus , our model is unsuitable for examining the pathobiology of sandy patches and contact bleeding associated with FGS . Another consideration is that much of human FGS pathology is seen in the human cervix [16] . Due to the technical challenge of injecting eggs into the mouse cervix we were not able to incorporate this into our model . All mice were injected at one time point and to the same depth likely because a consistent vaginal submucosal tissue plane naturally developed during the injections . We also believe this to be a limitation of our model as we were not able to study migrating eggs at various depths within the tissue . Given that our model features synchronous progression of egg-based inflammatory lesions by virtue of a single bolus injection , all lesions evolve at the same rate and as a result appear similar to each other . In this important sense the lesions that result in our model do not resemble human FGS , in which lesions are of varying chronicity depending on when oviposition has occurred . We have also injected Sepharose beads into mouse tissues and this foreign body control also does not result in epithelial alterations ( data not shown ) . Ultimately , a humanized animal model of FGS ( including HIV co-infection ) may be informative . The exact natural history of the local immune reactions to S . haematobium eggs in different phases of FGS is not known , but should be explored because it will have implications for treatment schedules and in choosing the best target populations ( i . e . , schoolgirls versus women ) . The results presented herein suggest that our novel model of FGS may give insights regarding the evolution of FGS lesions . Finally , it may be amenable to the study of S . haematobium-induced female reproductive tract inflammation and HIV susceptibility .
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Over 112 million people worldwide are infected with Schistosoma haematobium worms . S . haematobium eggs tend to be deposited in the tissue of pelvic organs such as the urinary bladder , lower ureters , cervix and vagina . Key sequelae include hematuria , dysuria , urinary frequency , and an increased risk of bladder cancer . This form of schistosomiasis can also cause dyspareunia , vaginal bleeding , pruritis , and granulomata that appear as tumors in the female genital tract . Collectively , these signs and symptoms are termed female genital schistosomiasis ( FGS ) . Recent studies suggest that FGS occurs more commonly in girls and women with HIV , suggesting that it may be a risk factor for becoming HIV-infected . Unfortunately , the pathophysiology of this co-infection is not well understood . A lack of an experimentally manipulable model has contributed to the paucity of research focusing on this parasite . We have circumvented the barriers to natural S . haematobium oviposition in the mouse by directly microinjecting parasite eggs into the vaginal mucosa . The injection of S . haematobium ova appears to trigger vaginal inflammation and scarring infiltration by leukocytes expressing HIV co-receptors , and increased urinary frequency . Our approach may provide a representative animal model that could contribute to new opportunities to better understand the basic molecular and cellular immunology of female genital schistosomiasis .
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[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases",
"medicine",
"and",
"health",
"sciences",
"women's",
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"obstetrics",
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"gynecology",
"gynecologic",
"infections",
"parasitic",
"diseases"
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2014
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A New Mouse Model for Female Genital Schistosomiasis
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Regulation of tissue and development specific gene expression patterns underlies the functional specialization of organs in multi-cellular organisms . In the viviparous tsetse fly ( Glossina ) , the female accessory gland is specialized to generate nutrients in the form of a milk-like secretion to support growth of intrauterine larva . Multiple milk protein genes are expressed specifically in the female accessory gland and are tightly linked with larval development . Disruption of milk protein synthesis deprives developing larvae of nutrients and results in extended larval development and/or in abortion . The ability to cause such a disruption could be utilized as a tsetse control strategy . Here we identify and delineate the regulatory sequence of a major milk protein gene ( milk gland protein 1:mgp1 ) by utilizing a combination of molecular techniques in tsetse , Drosophila transgenics , transcriptomics and in silico sequence analyses . The function of this promoter is conserved between tsetse and Drosophila . In transgenic Drosophila the mgp1 promoter directs reporter gene expression in a tissue and stage specific manner orthologous to that of Glossina . Analysis of the minimal required regulatory region of mgp1 , and the regulatory regions of other Glossina milk proteins identified putative homeodomain protein binding sites as the sole common feature . Annotation and expression analysis of Glossina homeodomain proteins identified ladybird late ( lbl ) as being accessory gland/fat body specific and differentially expressed between lactating/non-lactating flies . Knockdown of lbl in tsetse resulted in a significant reduction in transcript abundance of multiple milk protein genes and in a significant loss of fecundity . The role of Lbl in adult reproductive physiology is previously unknown . These results suggest that Lbl is part of a conserved reproductive regulatory system that could have implications beyond tsetse to other vector insects such as mosquitoes . This system is critical for tsetse fecundity and provides a potential target for development of a reproductive inhibitor .
Tsetse flies ( Glossina sp . ) are the exclusive vectors of Human African Trypanosomiasis ( HAT ) and nagana in Sub-Saharan Africa . HAT transmission can be prevented through tsetse population control by baited targets and trapping [1] . These control methods are effective due to the unusual nature of tsetse's reproductive physiology . Tsetse females reproduce via adenotrophic viviparity ( the intrauterine retention and nourishment of larval offspring ) . Each female is capable of producing only 8–10 progeny in their 2–3 month life span [2] . As a result , vector control strategies targeting population reduction can have a significant and quick impact upon population density and disease transmission dynamics . Viviparity in tsetse entails the protection and nourishment of offspring for the duration of immature ( larval ) development by the mother [3] . Larval nourishment is supplied as a “milk” secretion that is generated by the accessory gland ( milk gland ) that empties into the uterus . The tsetse milk gland is orthologous to accessory glands found in association with the female reproductive tract of other insects . Female accessory glands ( also referred to as paraovaria or colleterial glands ) in insects are adapted in a variety of novel ways to accommodate different reproductive strategies and life histories . In tsetse , milk gland morphology differs from the accessory glands of other insects . The milk gland expands from its connection to the uterus throughout the abdomen as a series of bifurcating tubules that intertwine with the abdominal fat body tissue [4] . The secretory cells of the tubules synthesize and secrete large volumes of milk [5] . At the time of parturition , larvae are almost equivalent in weight to the mother , illustrating the tremendous maternal nutritional investment in each gonotrophic cycle . Development of a control strategy to disrupt lactation in tsetse would be an effective way to reduce the vector population . Molecular analysis of milk gland secretions has identified Milk Gland Protein 1 ( MGP1 - VectorBase accession:GMOY009745 ) as one of the primary protein components of tsetse milk [6] . MGP1 is a member of the lipocalin protein family , which are known for their ability to bind small hydrophobic molecules [7] . Lipocalins are common constituents of lactation secretions in marsupials [8] , mammals [9] and are a primary component of the secretion generated by the viviparous cockroach species Diplotera punctata [10] . Synthesis of MGP1 in tsetse is exclusive to the secretory cells of the milk gland . Transcript/protein levels correlate with the demand for nutrients by the intrauterine larvae . Prior to larval development there is little to no expression of this gene . Beginning at late embryogenesis/early larvigenesis , mgp1 transcript and protein levels begin to increase and reach their maximum when the larva reaches its 3rd instar ( the time of highest nutritional demand ) [6] . Knockdown of mgp1 by dsRNA treatment reduces female fecundity suggesting that this protein is required for larval development [11] . Transcriptomic analysis of pregnant females and proteomic analysis of milk gland secretions have provided knowledge on the protein constituents of tsetse milk [12] . These proteins include Transferrin ( trf ) [13] , Acid Sphingomyelinase ( asmase1 ) [14] and nine unique proteins ( mgp2-10 ) the function of which is to be determined [12] , [15] ( see Table S11 in the Glossina genome paper ) [16] . Milk associated proteins are conserved in tissue and stage specific expression in a manner similar to MGP1 [12] . Identification of the signals and mechanisms coordinately regulating milk protein expression is an important goal in the search for novel vector control targets . The recent sequencing of the tsetse genome [16] and the advent of next generation high throughput technologies have allowed us to study the pregnancy and tissue specific nature of tsetse's most abundant milk protein gene ( mgp1 ) , focusing on the role of specific transcription factors that may be associated with the upstream promoter region . We performed our analysis of milk protein gene regulation in tsetse using a combination of classic molecular techniques and genetic analysis in transgenic Drosophila . We used in silico analysis of genomic data and multiple high throughput datasets coupled with functional studies in Glossina and in Drosophila to identify candidate homeodomain factors that are apparently involved in the regulation of multiple tsetse milk protein genes . Our results suggest conservation of a reproduction associated gene regulatory mechanism across different taxa ( Glossina and Drosophila ) in the context of differing reproductive physiologies . We discuss our findings in light of potential methods to disrupt the reproductive capacity of tsetse as a vector control tool .
qPCR primer sequences are found in Table S1 . All cloning primer sequences are presented in Table S2 and siRNA sequences in Table S3 . The Glossina morsitans morsitans colony maintained in the insectary at Yale University was originally established with puparia from fly populations in Zimbabwe . Newly emerged flies are separated by sex and mated at three to four days post-eclosion . Flies are maintained at 24±1°C with 50–55% relative humidity , and receive defibrinated bovine blood every 48 h using an artificial membrane system [17] . Samples for gene expression analysis during parturition were collected as follows . Female flies pregnant with 3rd instar larvae were collected . Flies were checked every 24 hours and individuals that had undergone parturition during the 24 hour period were collected to stage them in sample groups . Three flies were collected at the following time points , pregnant ( 3rd instar larva ) , 0–24 , 24–48 , 48–72 , 72–96 , 96–120 , 120–144 , 144–168 , and 168–192 hrs post parturition ( pp ) . RNA and protein were isolated from flash frozen female flies and tissue samples utilizing the standard Trizol ( Invitrogen , Carlsbad CA ) protocol with a modification to the final step in which the isolated protein pellets are dissolved in cracking buffer ( 8M urea , 3M thiourea , 1% dithiothreitol ( DTT ) and 4% CHAPS ) [18] . cDNA was prepared from total RNA using the Invitrogen Superscript III kit ( Invitrogen ) . Levels of mgp1 were determined by CFX Connect Real Time PCR Detection System with SYBR Green Supermix ( Bio-Rad , Hercules , CA ) . The data were analyzed with software version 3 . 1 ( Bio-Rad ) . All samples were normalized according to Glossina tubulin ( tub ) expression levels . Western blotting was performed utilizing the protein from the samples described above using previously described antisera and protocols [6] , [19] . Genomic sequences and automated annotations were derived from the recently completed G . m . morsitans genome [16] and are available at Vectorbase ( http://gmorsitans . vectorbase . org/Glossina_morsitans/Info/Index ) . Two constructs were created using 2 kB and 0 . 5 kB of the 5′ upstream from the mgp1 predicted transcription start site . These fragments were PCR amplified from G . m . morsitans genomic DNA using Platinum Taq DNA Polymerase High Fidelity ( Invitrogen ) and sub-cloned into the T-vector cloning vector ( Promega , Madison WI ) . Inserts were excised from the T-vector using SphI and NotI and ligated into the p-element vector pPelican [20] between the SphI and NotI sites . This vector includes a lacZ reporter gene downstream of the regulatory sequence . The enhancer/reporter constructs were used to transform w1118 flies by p-element mediated transformation via a commercial transformation service ( Best Gene: Drosophila Injection Services ( http://www . thebestgene . com ) . The transformations generated 4 lines carrying the 2 . 0 kB construct mgp1-Bgal-2 . 0-1-4 and 3 lines carrying the 0 . 5 kB construct mgp1-Bgal-0 . 5-1-3 . Drosophila lines were maintained according to standard protocols [21] . Tissues from male and female Drosophila were dissected in 1× PBS . Dissected tissues were fixed and stained for β-galactosidase activity with the ß-galactosidase Staining Kit ( Mirus , Madison WI ) following the manufacturer's protocols . Flies expressing an eGFP reporter with 509 ( mgp1-egfp-509 ) , 236 ( mgp1-egfp-236 ) , 112 ( mgp1-egfp-112 ) or 13 bp ( mgp1-egfp-13 ) nucleotide versions of the mgp1 upstream sequence were generated utilizing the PhiC homologous recombination transformation system [22] , [23] . The 509 bp mgp1 upstream fragment was PCR amplified from Glossina morsitans genomic DNA using Platinum Taq DNA Polymerase High Fidelity ( Invitrogen ) and sub-cloned into the T-vector cloning vector ( Promega ) . The fragment was excised with SphI and SpeI then ligated into the nuclear EGFP enhancer analysis vector pStinger [20] between the SphI and NheI sites by standard cloning methods . The mgp1-pStinger construct was used as a template to amplify the 509 , 236 , 112 and 13 base pair variants of the mgp1 promoter-egfp fusion constructs . The forward and reverse primers for the constructs were tailed with the AttB40 PhiC recombination site sequence . Amplified constructs were cloned into T-vector and sent for transformation via a commercial transformation service ( Best Gene: Drosophila Injection Services ( http://www . thebestgene . com ) . Constructs were injected into the Drosophila line genotype y1 w*; P[attP . w+ . attP]JB53F ( Bloomington Stock Center #27386 ) . The AttB integration site is 53F8 , 2R:12985015 [22] . Surviving injected flies were crossed with CyO/Sco 2nd chromosome balancer flies and screened for loss of eye color as a negative marker of transgene insertion . Drosophila lines were maintained according to standard protocols [21] . RNA was isolated from flies using Trizol ( Invitrogen ) . cDNA was prepared from total RNA using the Invitrogen Superscript III kit ( Invitrogen ) . Transgene transcript levels were quantified by qPCR with iQ SYBR Green Supermix using egfp specific primers and the CFX Connect Real Time PCR Detection System ( Bio-Rad ) . Data was analyzed using software version 3 . 1 ( Bio-Rad ) . All treatments were normalized according to Drosophila beta-tubulin expression levels using gene specific primers and carried out in triplicate . Visual inspection of transgene expression was performed by tissue dissection in 1× PBS followed by fluorescent microscopy using a Axio Cam dissecting scope equipped with a X-cite series Q fluorescence system ( Zeiss Microscopy & Image Analysis , Thornwood , New York ) . Stage specific quantification of egfp transgene expression was performed as described above with total RNA from 3 independent groups of larval , pupal , and 3–5 day old male and female flies from the mgp1-egfp-509 line . 3–5 day old adult females from the mgp1-egfp-236 , mgp1-egfp-112 and mgp1-egfp-13 lines were also collected and analyzed in the same manner to compare transgene expression between the lines . Nutritional deprivation experiments were performed by maintaining the mgp1-egfp-509 line on either complete ( yeast extract 100 g/L , sucrose 100 g/L , Agar 27 g/L , Methlyparaben: 30 ml of 100 g/L , Proprionic acid 3 mL/L ) or minimal media ( yeast extract 12 . 5 g/L , sucrose 12 . 5 g/L , Agar 27 g/L , Methlyparaben: 30 ml of 100 g/L , Proprionic acid 3 mL/L ) in 25×95 mm polystyrene vials . Egg deposition was monitored daily for each group . Parallel treatments were run to quantify egfp levels in stressed flies using the qPCR methods described above . Putative transcription factor binding sites were predicted within the mgp upstream regulatory region using the “MatInspector” program from Genomatix ( http://www . genomatix . de/ ) [24] . Upstream sequences from other characterized and predicted milk proteins were derived from the draft Glossina genome ( www . vectorbase . org ) [16] . The sequences used were from the 124 bp mgp1 ( GMOY009745 ) critical region , trf ( GMOY004228 ) , asmase1 ( GMOY002246 ) , mgp2-10 ( GMOY012368 , GMOY012125 , GMOY001342 , GMOY012016 , GMOY001343 , GMOY012016 , GMOY012369 ) genes . 500 bp from the predicted transcription start site of each gene was used in a comparative analysis . This analysis was also performed using MatInspector . Predicted Glossina homeodomain sequences were identified from the Glossina genome [16] . Protein sequences from the 104 known Drosophila homeodomain factors were used to search the Glossina genomic assembly and the de novo assembly from [12] by tBLASTn search . All resulting hits with scores lower than 1×10−10 were collected and searched against the NCBI database by BLASTx search . Sequences were then annotated with nomenclature from the highest scoring hit result . We have recently completed a transcriptome study focusing on differences between pregnant/lactating or dry flies ( non-lactating ) [12] . Raw sequence data are available from the sequence read archive at NCBI . We utilized the RNA-seq reads from that study to compare the transcript abundance of Glossina homeodomain proteins in lactating and dry flies . RNA-seq data were mapped directly to the Glossina homeodomain genes utilizing CLC Genomics Workbench ( CLC bio , Cambridge , Massachusetts ) . Normalized gene expression was calculated as RPKM [25] . Statistical differences of RPKM values between samples were determined by Kal's test following Bonferroni correction [26] . Statistical significance was determined at P<0 . 05 and a two-fold higher or lower transcript abundance in lactating flies was utilized to determine genes of interest [27] . Five candidate Glossina homeodomain genes were analyzed by qPCR based tissue and stage specific expression analysis . Tissue-specific samples were acquired from females harboring 2nd instar larva . qPCR was conducted as before and transcript levels were normalized to tubulin . Functional analysis of the ladybird late homeodomain factor was performed using synthesized siRNAs homologous to the Glossina ladybird late ortholog . siRNAs were designed by web-based tools ( IDT , Coralville , IA ) and ordered commercially ( IDT ) . The siRNA consists of two Duplex sequences for lbl . Control siRNA molecules were designed as complementary to green fluorescent protein ( GFP ) mRNA ( IDT , Cordville , IA ) . Concentration of each siRNA was adjusted to 700–750 ng/µl in PBS with a Nanodrop spectrophotometer . Mated female flies were injected with 1 . 5 µl siRNA 6–8 d after adult emergence . siRNA was shown to have no discernible effect on larval transcripts [28] . Expression levels were determined by qPCR ( as described; normalized to tubulin ) 5 d after injection for lbl and 8 d after injection for mgp1 .
Transcript abundance of the mgp1 gene is related to intrauterine larval development . Previous expression data for mgp1 was based upon samples staged by female reproductive physiology and larval development [15] . Based upon these data , mgp1 transcripts appear to decrease after larval deposition ( parturition ) and increase again at the onset of intrauterine larval development in the next pregnancy cycle . To obtain a high resolution temporal analysis of milk protein gene expression during this period , pregnant flies were collected and synchronized by time of parturition in 24 hour intervals rather than by pregnancy status . Transcript levels from these samples were quantified by qPCR analysis , and protein expression was determined by Western analysis . The results show that mgp1 transcript abundance and protein levels undergo a precipitous drop after parturition and reach their lowest levels at 24–48 hours post-parturition ( Figure 1A and 1B ) . Transcript and protein levels begin to increase again between 48–72 hours post parturition and continue to increase to the last time point in the series at 168–192 hours post parturition when there is a 2nd instar intrauterine larva in the uterus . Analysis of the regulation of mgp1 expression required the development of genetic tools that facilitate the observation of Glossina promoter function in vivo . To accomplish this , we leveraged the genetic tools available in D . melanogaster to perform an in vivo analysis of the mgp1 promoter . Drosophila is related to Glossina as both are members of the dipteran “Higher Flies” ( Brachycera ) suborder . The transformation constructs were developed using the pPelican enhancer/reporter plasmid [20] . The plasmid contains a β-galactosidase ( β-gal ) reporter gene and the promoter/reporter fusion is flanked by gypsy insulator sequences to prevent the influence of local regulatory elements . Transformation was accomplished by p-element transposition . Reporter expression was visualized by staining for β-gal activity in dissected transgenic Drosophila tissues . Two constructs were created including either 2 kB or 0 . 5 kB sequence of the 5′ upstream from the predicted mgp1 transcription start site ( Figure S1A ) . Transformation of these constructs resulted in the production of four transgenic lines for the 2 . 0 kB construct ( mgp1-β-gal-2 . 0-1 to -4 ) and three transgenic lines for the 0 . 5 kB construct ( mgp1-β-gal-0 . 5-1 to -3 ) . In tsetse , mgp1 expression is specific to the milk gland of adult female flies [11] . We analyzed different tissues from both mgp1-β-gal-2 . 0 and mgp1- β-gal-0 . 5 lines with β-gal staining to understand the sex and tissue specific nature of the reporter gene expression . Transgene expression was exclusive to the accessory glands ( paraovaria ) of the female reproductive tract in both transgenic lines ( Figure 2A ) while this staining pattern was not observed in control Drosophila . Background staining was observed in the uterus , midgut and in specific cells along the midline of the dorsal abdominal surface in both sexes . This background staining pattern was also observed in untransformed control flies suggesting that this endogenous β-gal activity may result from populations of bacteria resident within the fly . The specific staining observed in the accessory glands of females suggests that regulatory mechanisms and factors driving sex and tissue specific expression of mgp1 in tsetse are conserved in Drosophila . It also demonstrates that the transcriptional regulatory elements required for sex and tissue specific expression are contained within the 0 . 5 kB upstream region of the mgp1 gene . To identify the minimal region required for tissue and stage specific gene expression by the mgp1 promoter , additional transgenic Drosophila lines were generated . To eliminate the confounding issues of background staining with endogenous β-gal activity and variation of transgene expression due to position effect , an alternative transformation strategy was utilized . The new transgenic lines were generated using the PhiC recombination system [22] , [23] . This system allows for the stable incorporation of transgenes into a specific genomic locus via homologous recombination , thereby eliminating position effect derived variation in expression between lines . To enhance transgene detection and reduce background , the β-gal reporter gene was replaced with the nuclear specific enhanced green fluorescent protein ( EGFP ) reporter gene from the pStinger p-element transformation vector . We generated four transgenic lines , which include either 509 ( mgp1-egfp-509 ) , 236 ( mgp1-egfp-236 ) , 112 ( mgp1-egfp-112 ) or 13 bp ( mgp1-egfp-13 ) segments corresponding to the mgp1 regulatory region upstream of the start site ( Figure S1B ) . All life stages and tissues from these lines were examined by florescent microscopy for nuclear specific EGFP expression . Microscopic analysis of EGFP florescence revealed reporter gene expression is only detectable within the accessory glands of reproductively active ( undergoing oogenesis and ovulation ) females from the mgp1-egfp-509 line . This is almost identical to the observations in the β-gal expressing lines ( Figure 2B ) . Of the four lines , the mgp1-egfp-509 and mgp1-egfp-236 lines were positive for female accessory specific EGFP expression by visual screening , while EGFP expression in mgp1-egfp-112 and mgp1-egfp-13 bp constructs was undetectable by visual observation . These results suggest that the minimal region for tissue specific expression and basic promoter function lies within the 236 bp upstream of the mgp1 transcription start site . The 124 bp differential region between the mgp1-egfp-236 and mgp1-egfp-112 lines appears to carry a transcriptional response element critical to the function of this promoter as a whole . Quantitative analysis of egfp expression during different developmental and sexual stages in mgp1-egfp-509 reveals that the transgene is only significantly expressed within sexually mature adult females with only background levels present in larvae and males ( Figure 2C ) . Comparison of egfp expression between the four lines confirms the results we obtained by microscopy analysis . Expression of the transgene is highest in mgp1-egfp-509 ( Figure 2D ) . Transgene expression in mgp1-egfp-236 shows that the overall transcript level is significantly lower than that observed in mgp1-egfp-509 . However , mgp1-egfp-236 maintains the sex and tissue specific characteristics of the mgp1-egfp-509 . Transgene expression in mgp1-egfp-13 and mgp1-egfp-112 was equivalent to levels observed in male flies suggesting that regulatory function is compromised in these lines . The reproductive cycle in Drosophila differs from that of tsetse in that oogenesis and embryo deposition occur at a steady rate once the female is sexually mature and mated as opposed to the defined cycles of oogenesis , embryogenesis and larvigenesis observed in tsetse . Nutritional stress in Drosophila results in reduction or termination of oogenesis . In the absence of appropriate nutritional stimulation yolk protein gene expression and oocyte development cease . In some cases developing oocytes may undergo apoptosis and reabsorption [29] , [30] . To determine if a nutritionally induced reduction in oogenesis and ovulation would influence transgene expression , 3 day old mated female mgp1-egfp-509 flies were put on a minimal media diet followed by daily observation of egg deposition and egfp transcript levels . Maintenance on minimal media relative to complete media resulted in a significant reduction in egg production in the mgp1-egfp-509 flies within 5 days ( Figure 2E ) . Quantitative analysis of reporter egfp expression in flies maintained on minimal media during the 5 day time period shows a direct correlation between transgene expression and egg deposition over time ( Figure 2F ) . These results correlate the down regulation of gene expression activity within the accessory gland with reduced nutritional status/oogenesis . Whether the gland is responding to reduced nutritional stimuli or reduced reproductive stimuli is not yet understood . We next set out to predict transcription factor binding sites that may lie within the 0 . 5 kb promoter region of mgp1 responsible for the sex and tissue specific expression profile we observed in both taxa . Analysis of the 0 . 5 kB regulatory sequence using transcription factor binding site prediction software resulted in a total of 41 predicted binding sites from a variety of factors . The matrices used in the identification included those for known insect transcription factors and eukaryotic basal promoter elements . To reduce the number of candidate factors , we narrowed our analysis to the 124 bp region between lines , mgp1-egfp-236 and mgp1-egfp-112 , which is required for sex and tissue specific function . This analysis reduced the number of predictions to 11 candidate binding sites . The sites predicted include generic Drosophila homeodomain binding sites , including ( DHOM: 2 sites ) , OVO transcription factor ( DOVO: 1 site ) , Iroquois factor group ( IRXF: 1 site ) , Tailless ( DTLL: 1 site ) , Abdominal B ( ABDB: 1 site ) , Paired homeodomain factors ( PRDH: 2 sites ) , Dead Ringer ( DRIF: 1 site ) , Giant ( DGTF: 1 site ) and Heat shock factors ( DHSF: 1 site ) . Previous characterizations , annotation of the Glossina genome [16] and RNA-Seq based analysis of lactating and non-lactating flies has identified 12 milk protein genes that function in the lactation process in tsetse , including mgp1 , mgp2-10 , trf and asmase1 [12] . Comparative analysis of the 124 bp mgp1 region critical for transgene expression in Drosophila with the 500 bp sequence upstream of the transcription start site of these genes predicts that only homeodomain protein binding sites ( DHOM ) are common between all 12 sequences ( p-value: 0 . 0241 ) ( Figure 3 ) . The only other site in common between these promoters is that of the TATA binding protein ( TATA box ) . To identify homeodomain type regulatory factors that may recognize the conserved DHOM sites , we annotated and collated all sequences in Glossina bearing homology to the 106 annotated Drosophila homeodomain proteins . This was accomplished by tBLASTn search of the Glossina genome assembly [16] and a de novo assembly of RNA-seq libraries with Drosophila homeodomain protein sequences . All significant hits were submitted to a BLASTx comparison against the NCBI protein database . This analysis predicted a total of 96 putative tsetse homeodomain protein orthologs within the current genome assembly ( see Table S36 in the Glossina genome paper ) . Functional analysis of all candidate homeodomain factors was prohibitive . To reduce the number of targets , the expression profile of the 96 homeodomain genes were screened by RNA-Seq analysis using the available Illumina sequencing data from lactating and non-lactating flies [12] . To identify differential transcript abundance between the two physiological states , we selected genes showing a relative transcript difference of greater than 2 fold higher or lower between samples , and sequence representation by at least 500 reads between libraries . Based upon these criteria , our analysis identified 5 homeodomain genes; 3 with high transcript abundance nubbin ( nub – VectorBase accession: GMOY012175 ) , teashirt ( tsh – VectorBase accession: GMOY011052 ) and vismay ( vis – VectorBase accession: GMOY000857 ) and 2 with lower transcript abundance ladybird late ( lbl – VectorBase accession: GMOY004068 ) , and pox meso ( poxm – VectorBase accession: GMOY002525 ) ( Table 1 ) . Given the tissue specific nature of expression for mgp1 ( and the other milk proteins ) , homeodomain factors specific to that tissue were of primary interest . We performed a qPCR based expression analysis of the five candidate factors using RNA from different tissue samples obtained from lactating female flies . The expression patterns were unique for each gene . Of the genes analyzed , only the Ladybird Late gene ( lbl ) displayed a milk gland/fat body specific expression pattern . The fat body and milk gland are grouped together due to their interconnected nature which makes separating them by dissection next to impossible . Transcripts for lbl were ∼30 fold higher in the milk gland/fat body tissue than in any other tissue ( Figure 4A ) . The other four factors were found in multiple tissues at different levels of transcript abundance ( Figure S2 ) . To determine lbl involvement in milk protein gene regulation , siRNA was utilized to perform gene knockdown analyses . Flies were treated with siRNAs against lbl , or gfp ( control ) . The siRNA injections resulted in significant reductions in target gene transcript abundance . The levels of lbl were reduced to ∼30% of controls ( Figure 4B ) . Following siRNA treatment , transcript abundance of the mgp1 , mgp2 and asmase1 genes was measured by qPCR . The knockdown of lbl , resulted in a significant decrease of mgp1 , mgp2 and asmase transcript levels relative to the GFP controls ( Figure 4C ) . These data indicate that the lbl homeodomain protein functions as a positive regulator of the mgp1 gene either directly or indirectly . We monitored the larviposition of siLbL treated mated females over the first gonotrophic cycle . The lbl knockdown flies showed a significant reduction in the average rate of larviposition per fly per day relative to controls ( Figure 5A ) . Cumulative larval deposition rates between the control and lbl knockdown group revealed that the lbl group birthed about half as many larvae as the controls ( Figure 5B ) . The reduction in fecundity in these flies is likely due to the reduced level of milk protein gene expression resulting from the knockdown of the lbl . This results in nutritional deprivation of developing larvae , larval death and abortion . This knockdown does not result in a complete disruption of tsetse fecundity in terms of the number of larvae developed per female; however we believe that this is due to incomplete penetrance of our injectable siRNA system in Glossina .
The lactation process and the milk proteins are indispensable for intrauterine progeny development in tsetse . Recent transcriptomic/proteomic/genomic analyses in tsetse have provided a comprehensive understanding of the protein constituents of tsetse milk [12] , [16] . These proteins act as complete amino acid sources , lipid emulsification and transport agents , providers of enzymatic function aiding larval digestion , transporters of micronutrients/small molecules and regulators of immune function [6] , [11]–[15] , [31]–[33] . The MGP1 protein is one of the most abundant constituents of tsetse's milk and critical to larval health [6] , [11] . Transcript abundance of the mgp1 gene is tightly associated with the pregnancy status of the female and is restricted to the secretory cells of the milk gland . This pattern of regulation optimizes the utilization of resources for milk protein production by coordinating gene expression with pregnancy status and nutritional demand by intrauterine offspring . The other milk protein genes display almost identical expression profiles suggesting that these proteins are regulated by the same underlying system that controls mgp1 . Our studies here indicate that Lbl is responsible for driving the tissue and stage specific expression of the mgp1 gene and most likely the other milk proteins in tsetse . Furthermore our results show that this tissue specific regulatory mechanism is conserved between Glossina and Drosophila and that Lbl likely regulates expression of the accessory gland products in the adult female Drosophila . The mechanism by which this system monitors pregnancy and larval nutritional demand of tsetse remains unknown and is a target of ongoing study . The powerful genetic capabilities of the Drosophila transgenic system will promote these studies and furthermore open up investigations into the little-known biology of insect reproductive physiology . In tsetse , the milk gland has been a recent focus of study; however less is known regarding the function ( s ) of female accessory glands in Drosophila . Tsetse's physiological adaptations to accommodate viviparous reproduction are unique; but the tissues such as the milk gland and uterus in tsetse are derived from common elements of the female insect reproductive tract . Female accessory gland tissues perform essential functions in insect reproduction . Accessory gland secretions are associated with fertilization , antimicrobial activity , lubrication , embryo adhesion , uterine muscle contraction , defensive secretions and physical protection of embryos . Cockroaches and Mantids ( both members of the Dictyoptera clade ) have modified glands which produce 2 substances ( one from the left and one from the right gland ) which combine in the oviduct to generate an ootheca that encases their eggs and hardens into a protective coating [34] , [35] . The kissing bug ( Rhodnius prolixus ) uses these glands to produce an adhesive secretion to glue eggs to substrate [36] . The accessory glands of many social non-reproductive castes of female hymenopterans have been adapted from reproductive organs to form defensive organs which secrete venom . The conservation of the female accessory regulatory system may extend beyond the higher Diptera , and could be conserved throughout the arthropod lineage as a regulator of glandular gene expression in response to reproductive events . This finding would have implications for other disease vectors such as mosquitoes where little information about accessory gland function is available . The recent release of the Glossina genome sequence [16] and the availability of high throughput sequencing analysis were essential for the analysis of the milk protein promoters , and the identification and screening of the transcription factors we describe here . These in silico analyses led us to identify the homeodomain family of transcription factors and allowed us to narrow down our search from a field of 96 potential factors to a handful of 5 factors . Homeodomain proteins are a conserved family of transcription factors characterized by the presence of a helix-turn-helix DNA binding domain called the homeodomain [37] , [38] . These factors are associated with coordination of body plan organization during development in metazoan organisms [39] . While the function of some of these proteins is well understood within the context of embryonic development , little is known of their function on transcriptional regulation within mature organisms . In addition , a number of homeobox genes have been identified based upon DNA binding domain conservation , yet little is known as to their functions and if the functions are orthologous between organisms [40] . Here , tissue specific expression profiling and RNAi analysis led us to the Ladybird Late homeodomain factor ( Lbl ) . This factor appears critical to the expression of multiple milk protein genes in tsetse based upon our knockdown experiments . The 236 bp tsetse promoter region was sufficient to drive the synthesis of a reporter gene in an accessory gland specific manner in female Drosophila . The conservation of regulatory sequence function in tsetse and Drosophila suggests that LbL performs orthologous reproductive functions in both species . The lbl gene was first discovered with its paralog ladybird early ( lbe ) during a search for novel homeodomain proteins [41] . This factor and most of the characterized homeodomain proteins are primarily associated with developmental functions in immature organisms . Research on the ladybird genes in Drosophila have linked these genes with embryonic heart cell precursor diversification [42] , embryonic muscle precursor cell diversification ( specifically myogenesis in appendages ) [43] , [44] and embryonic neuroblast diversification [45] . Analysis of ladybird gene regulation suggests that the two genes are regulated by the wingless ( wg ) signaling pathway and may in turn regulate wingless expression in a feedback loop [46] . This is the first association of Lbl function with adult regulatory processes . The data presented here indicate that Lbl is part of a system that either directly or indirectly regulates gene expression in female accessory tissues . The details of the other components of this system and the reproductive cues it responds to are unknown . The association of lbl with wingless signaling during development hints at the possibility of the wingless pathway as a regulator of reproduction associated gene regulation in adults [46] . The similarity in function of the mgp1 regulatory sequence between tsetse and Drosophila coupled with the ease of transgenesis in the fruit fly system allows us to use Drosophila as a surrogate in which to study female accessory gland gene expression . This promoter in combination with GAL4 mediated systems can enable in vivo tissue and stage specific knockdowns and/or ectopic expression experiments in Drosophila in order to identify components of the signaling system regulating Lbl activity . Flies carrying mgp1-GAL4/mgp1-EGFP fusions can be crossed with the available UAS-RNAi lines [47] of genes of interest ( such as components of the wg pathway ) to perform knockdowns quickly and efficiently . The presence of the mgp1-EGFP reporter will allow for rapid visual assessment of knockdown phenotypes . The findings from Drosophila can then be translated back into tsetse by RNAi experiments . These findings are an important step in identifying the pathways , factors and signals regulating milk gland/accessory gland function in insect reproduction . Milk production in Glossina is essential for reproductive success and provides an important molecular target for the development of an insect specific reproductive inhibitor . Further analysis of mechanisms governing this system will provide important data towards controlling sleeping sickness as well as broadening the understanding of insect reproductive physiology . The knowledge gained from this work can be put towards the development of an insect specific inhibitor which disrupts milk protein production in tsetse . A substance such as this could be utilized on targets , traps and livestock as a non-toxic alternative to pesticides to reduce tsetse populations and decrease the trypanosome transmission threat .
|
Female tsetse flies ( Diptera: Glossina ) harbor and give birth to live young . To do this , they nourish their intrauterine larvae with milk secretions . This work focuses upon understanding the regulation of tsetse milk proteins , which are essential for fecundity and are expressed in a temporally and spatially specific manner by pregnant females . We identified the minimal upstream regulatory DNA sequence of the major milk protein gene mgp1 , which confers tissue specific expression in the female accessory glands of reproductively active flies . This regulatory sequence functions similarly in transgenic fruit flies ( Drosophila melanogaster ) and drives expression of reporter gene products in the adult female accessory gland . Comparison of this regulatory sequence with sequences from other characterized milk proteins indicates that conserved homeodomain transcription factors may be responsible for regulating these genes . Analysis of Glossina homeodomain proteins identified an accessory gland/fat body specific factor , Ladybird late ( lbl ) , which appears to regulate the expression of multiple milk proteins . Reduction of lbl levels interferes with milk protein gene expression , which in turn reduces Glossina fecundity . These results suggest that milk proteins in Glossina are regulated by a conserved regulatory system mediated in part by the homeodomain transcription factor lbl . Components of this system could provide a target for development of a tsetse reproductive inhibitor .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"signal",
"transduction",
"developmental",
"biology",
"dna",
"transcription",
"rna",
"interference",
"cell",
"biology",
"molecular",
"development",
"gene",
"expression",
"genetics",
"gene",
"regulation",
"biology",
"and",
"life",
"sciences",
"epigenetics",
"molecular",
"genetics",
"molecular",
"cell",
"biology",
"cell",
"signaling",
"evolutionary",
"developmental",
"biology",
"gene",
"function"
] |
2014
|
The Homeodomain Protein Ladybird Late Regulates Synthesis of Milk Proteins during Pregnancy in the Tsetse Fly (Glossina morsitans)
|
Despite the major public health importance of visceral leishmaniasis ( VL ) in Latin America , well-designed studies to inform diagnosis , treatment and control interventions are scarce . Few observational studies address prognostic assessment in patients with VL . This study aimed to identify risk factors for death in children aged less than 15 years admitted for VL treatment in a referral center in northeast Brazil . In a retrospective cohort , we reviewed 546 records of patients younger than 15 years admitted with the diagnosis of VL at the Instituto de Medicina Integral Professor Fernando Figueira between May 1996 and June 2006 . Age ranged from 4 months to 13 . 7 years , and 275 ( 50% ) were male . There were 57 deaths , with a case-fatality rate of 10% . In multivariate logistic regression , the independent predictors of risk of dying from VL were ( adjusted OR , 95% CI ) : mucosal bleeding ( 4 . 1 , 1 . 3–13 . 4 ) , jaundice ( 4 . 4 , 1 . 7–11 . 2 ) , dyspnea ( 2 . 8 , 1 . 2–6 . 1 ) , suspected or confirmed bacterial infections ( 2 . 7 , 1 . 2–6 . 1 ) , neutrophil count <500/mm3 ( 3 . 1 , 1 . 4–6 . 9 ) and platelet count <50 , 000/mm3 ( 11 . 7 , 5 . 4–25 . 1 ) . A prognostic score was proposed and had satisfactory sensitivity ( 88 . 7% ) and specificity ( 78 . 5% ) . Prognostic and severity markers can be useful to inform clinical decisions such as whether a child with VL can be safely treated in the local healthcare facility or would potentially benefit from transfer to referral centers where advanced life support facilities are available . High risk patients may benefit from interventions such as early use of extended-spectrum antibiotics or transfusion of blood products . These baseline risk-based supportive interventions should be assessed in clinical trials .
In Latin America , visceral leishmaniasis ( VL ) cases occur from Mexico to Argentina , but around 90% of reported cases come from Brazil [1] . Despite substantial underreporting in several different parts of the world [1] , Brazil's national surveillance system has informed estimates of disease burden in this country , where VL cases are found in the Distrito Federal and in 22 of 26 states [2] . Between 1990 and 2008 nearly 58 , 000 cases were reported in Brazil , but annual numbers have increased from 1 , 944 cases in 1990 to 3 , 990 in 2008 [3] . While the average VL incidence has been around 2 cases per 100 , 000 inhabitants [2] , these figures do not take into account the focal nature of disease , which selectively pose a much higher burden on neglected populations from rural Northeast or from the slums around large cities , where major epidemics have recently occurred [4] . Although many infected individuals present no signs of disease , symptomatic VL can be very severe and its mortality remains substantial . Studies held in different parts of Brazil have reported case-fatality rates ranging from 4 . 4% to 10 . 2% in treated patients [5]–[7] . Despite the major public health importance of VL in Latin America , a recently published systematic review has shown that well-designed studies to inform diagnosis , treatment and control interventions in the region are scarce [8] . Unfortunately , national guidelines and treatment recommendations are often based on expert opinion instead of hard data from controlled trials [9] . Moreover , few observational studies address risk assessment in patients with the disease . For instance , as far as prognostic indicators are concerned , published evidence tend to come from elsewhere other than Latin America [10]–[13] , from where we found only one report [5] . This report from Teresina ( Brazil ) identifies young age as a risk factor for poor prognosis , but it included patients from all ages in the analysis . In pediatric practice , however , it would be useful to identify children under higher risk of dying so that the best supportive and therapeutic options could be offered . The aim of the present study is to identify risk factors for death in children under the age of 15 years admitted for VL treatment in a referral center in northeast Brazil .
The protocol was approved by the Research Ethics Committee of the Instituto de Medicina Integral Prof . Fernando Figueira ( IMIP ) . Because data were retrieved from hospital records dating up to more than ten years and of patients whose families live away from Recife , the Ethics Committee agreed it would not be feasible to obtain individual signed consent forms . Data were however analyzed anonymously . Socio-demographic , clinical and laboratory variables were assessed for association with death ( primary outcome ) in children and adolescents admitted for VL treatment . In addition , the study aimed to describe the case-fatality rate and the main causes of death in this group . A retrospective cohort of children and adolescents with up to 14 years of age ( inclusive ) admitted for VL treatment was carried out at IMIP , a tertiary non-for-profit teaching hospital located in Recife ( population 1 . 5 million ) , in the northeast of Brazil . IMIP , a referral center for the treatment of VL , belongs to the national public health system and provides free medical care for people from Pernambuco and neighbor states . Patients notified to the hospital epidemiology department as having a diagnosis of VL were considered eligible to participate in the study and their records were reviewed , regardless of the outcome , from admission to discharge or death . Clinical , socio-demographic and laboratory variables were recorded onto a standardized form following a pilot study . Variables with more than 80% missing information were not included . Records were assessed by one of three medically-trained investigators and forms were reviewed for missing data and consistency . Data were entered twice into a computer database and EpiInfo validate module was used to identify inconsistencies . VL diagnosis was established either by confirmatory tests or by clinical and epidemiological criteria . The laboratory tests could be any one of the following: a positive Giemsa-stained bone marrow smear , a titer ≥1∶1 , 600 in direct agglutination test ( DAT ) or a titer ≥1∶40 in indirect immunofluorescence test ( IFI ) . Diagnosis based on clinical and epidemiological criteria consisted of: a ) clinical features of VL ( fever and hepatosplenomegaly ) and b ) pancytopenia; and c ) a positive epidemiological history and d ) clinical response following a course of antimonials . The weight/age Z scores ( WAZ ) were calculated using Nutrition Program , EpiInfo ( Centers for Disease Control and Prevention/World Health Organization , 1978 reference ) . Children with WAZ <−3 were considered severely malnourished . Hemorrhagic manifestations were divided into 2 groups: cutaneous ( bruising and petechiae ) and mucosal ( epistaxis and gengivorrhagia ) . Suspected bacterial co-infection was considered when the attending physician prescribed antibiotics and raised one of the following diagnoses: pneumonia , gastroenteritis , sepsis , otitis media and skin abscess , according to locally published guidelines [14] . Laboratory variables were categorized according to the following cutoff points: a ) severe leucopenia ( ≤2 , 500/mm3 ) ; severe neutropenia ( neutrophil count ≤500/mm3 ) ; b ) severe anemia ( hemoglobin <5 g/dL ) ; and c ) severe thrombocytopenia ( platelet count <50 , 000/mm3 ) . A sample size calculation was performed in EpiInfo software . Assumptions were inferred from preliminary analysis of the first 150 patients and included an unexposed : exposed ratio of 2∶1 and a death rate of 5% in non-exposed . A total of 429 children would be required to detect a 2 . 5 increase in risk , with significance level of 5% and power of 80% . Statistical analysis was performed using Epi-Info and STATA software packages . First , Chi-square tests were used to test the association between death and clinical , socio-demographic and laboratory variables in order to select those significant at a p level of <0 . 2 for inclusion in the logistic regression model in a stepwise backwards procedure . Variables showing an independent association with the risk of dying at the p<0 . 05 level remained in the final model . The factors above listed as independent predictors of death from VL were used to build a prognostic score . All regression coefficients were divided by the lowest one ( suspected bacterial co-infection ) and rounded to the next integer without decimal points in order to facilitate clinical use . Subsequently , we validated the new scoring system with all the patients included in this study: the real outcome of each participant was compared to his/her prediction based on the new scoring system . Sensitivity , specificity , positive and negative predictive values and the area below the receiver operating characteristic curve ( ROC ) were calculated and used to evaluate the predictive performance of the prognostic score [15] . ROC reflects the ability of a scoring system to differentiate positive events ( in this case , death in VL patients ) and negative events ( no death in VL patients ) . A higher ROC represents a better scoring system .
Between May 1996 and June 2006 a total of 557 patients were notified to the hospital epidemiology department as having a diagnosis of VL . Four records were not found and other 7 were excluded because , despite being initially notified as cases of VL , later had an alternative diagnosis ( such as leukemia ) . Therefore , 546 patients are reported here . Out of these , 385 had diagnosis confirmed by a positive bone marrow smear , 14 by a positive DAT test , four by a positive IFI test and in 143 cases , diagnosis relied on clinical and epidemiological criteria . Out of 546 patients , 275 ( 50% ) patients were male . The median age was 3 . 2 years and the youngest child was four months-old and the eldest 13 . 7 years-old on admission . The median duration of symptoms was 30 days ( interquartile range , 15–60 days ) . There were 57 deaths , with a case-fatality rate of 10% . The main immediate causes of death were associated bacterial infections in 21 ( 37% ) cases , mucosal bleeding in 17 ( 29% ) , both infection and bleeding in 10 ( 18% ) and infection or bleeding associated with liver failure in 8 ( 13% ) and other causes in 2 ( 3% ) . On univariate analysis ( table 1 ) , male gender , age less than five years-old , severe malnutrition , presence of diarrhea , edema , bleeding ( cutaneous and mucosal ) , jaundice , dyspnea , suspected bacterial co-infection , hemoglobin <5 g/dL , leukocyte count <2 , 500/mm3 , neutrophil count <500/mm3 and platelet count <50 , 000/mm3 were associated with risk of death with a p value <0 . 2 and were selected for inclusion in the multivariate logistic regression analysis . Results of multivariate logistic regression analysis are shown on table 1 . The presence of mucosal bleeding , jaundice , dyspnea , suspected bacterial co-infection , severe neutropenia ( neutrophil count <500/mm3 ) and severe thrombocytopenia ( platelet count <50 , 000/mm3 ) were identified as independent predictors of death from VL . Severe thrombocytopenia showed the strongest association with death ( adjusted OR 11 . 7 ) . The interactions between “mucosal bleeding” and “thrombocytopenia” as well as “neutropenia” and “suspected bacterial co-infection” were tested , but no statistically significant interactions were found ( p = 0 . 21 and p = 0 . 11 respectively ) . The generated scoring system ( table 2 ) consisted in allocating the following points for each variable when present: one for dyspnea , suspected bacterial co-infection and severe neutropenia; two for mucosal bleeding and jaundice; three for severe thrombocytopenia . Any patient could have a score ranging from 0 to 9 . A score ≥3 was selected as the best predictor of death because it was able to gather the most adequate combination of sensitivity ( 88 . 7% ) , specificity ( 78 . 5% ) , positive predictive value ( 32 . 0% ) , negative predictive value ( 78 . 5% ) and area under ROC curve ( 89 . 5% ) .
The present study describes four clinical ( mucosal bleeding , jaundice , dyspnea , suspected bacterial co-infection ) and two laboratory variables ( severe neutropenia and severe thrombocytopenia ) as independent predictors of the risk of death in children and adolescents younger than 15 years of age admitted for VL treatment in a tertiary hospital in northeast Brazil . Factors related with death or severity of VL were previously assessed in four studies from Africa [10]–[13] and one from Latin America [5] . Only one of them , from Tunisia , exclusively analyzed the pediatric age [10] . A total of 232 children were retrospectively evaluated and seven prognostic factors at hospital admission were identified: visit delayed more than 56 days , fever lasting more than 21 days , normal or low temperature ( < 37 , 5°C according to the authors ) , hemorrhagic syndrome , hemoglobin rate <5 . 5 g/dL , sedimentation rate <25 mm and hypoalbuminemia <30 g/L [10] . We found three large studies from the Sudan and Uganda [11]–[13] . In each of them more than 1 , 000 patients from all age groups were included , but only one described risk factors according to age group [11] . In the subset of Sudanese patients younger than 16 years the following risk factors for death were identified: age <2 years , malnutrition ( weight for height <60% ) , anemia ( hemoglobin level <6 g/dL ) and splenomegaly ( Hackett grade 3–5 ) [11] . In a study from the Northeast of Brazil ( Teresina , Piauí ) both children and adults were included in a case-control study . Diarrhea , jaundice , fever for more than 60 days and hematocrit ≤20% were found to be significantly associated with death from VL in a multiple logistic regression model [5] . Out of the six factors identified in the present study , only jaundice and hemorrhagic syndrome were previously reported by other groups [5] , [10] . These heterogeneous findings could be explained by the diversity of participants included in the different studies regarding age , access to treatment , setting and Leishmania species . An interesting observation is that a higher risk of death was associated with upper gastrointestinal ( UGI ) bleeding ( OR = 38 . 3 , 95% CI not calculable ) on bivariate analysis , but this did not remain significant on multivariate analysis , probably due to the small ( 5 ) number of children with UGI bleeding . Thrombocytopenia , but not decreased hemoglobin level was associated with higher risk of death . Although UGI bleeding can be associated with low platelet count or decreased hemoglobin level , respectively as a cause and consequence of bleeding , we were not able to analyze serial blood samples for each patient and only admission samples were included in the model . The newly generated prognostic scoring system is based on four clinical variables and two laboratorial tests that are usually available in poor resource settings . In addition , it demonstrated satisfactory sensitivity and specificity similarly to those reported in the study held in Teresina , Brazil ( sensitivity 85 . 7% and specificity 92 . 5% ) which also created a prognostic score for children and adults with VL [5] . Our study had limitations that deserve to be discussed . Inherent to retrospective studies , clinical and laboratory data recording might be less accurate , even though our study was carried out in a teaching hospital with regular monitoring of medical recording quality . We have decided to include cases with clinical-epidemiological diagnoses , as well as those with confirmatory tests . While restricting analysis to confirmed cases would be a methodologically interesting option , in real life many cases do not have parasitological confirmation . On the other hand , the case fatality rate ( 10% ) observed in our study was somewhat higher than the national average of 7% [16] and this might be due to the characteristics of this referral hospital that tends to receive cases from the most severe spectrum of disease . The main immediate causes of death were infections and bleeding which are the classical complications of the disease and also compatible with results obtained from other groups in different populations [5] , [10]–[12] . Despite these limitations , no previous reports have assessed children-specific independent predictors for death by VL in Latin America . The prognostic score reported in the present study can be promptly assessed in any setting and require only trained health care professionals , therefore are feasible in developing countries where VL is endemic . Furthermore , the laboratory analyses are simple and relatively cheap and can be performed in most health facilities . Isolated risk factors or a combination of them may lead to important clinical decisions such as: platelet or plasma transfusion , use of antibiotics or use of vitamin K . In addition , early identification of unfavorable prognostic factors can facilitate rational allocation of VL cases to be treated as outpatients , admitted to regional hospitals or referred to larger centers with intensive care facilities . Prognostic and severity markers can also be useful for the selection of candidates for clinical trials . Visceral leishmaniasis is a model for the study of host-pathogen interaction . The biological rationale to explain why some infected individuals will develop any disease instead of asymptomatic seroconversion remains unclear . A similar uncertainty involves understanding the reasons behind a milder or more severe clinical presentation . The predictors of death identified in the present study ( jaundice , dyspnea , suspected bacterial co-infection , mucosal bleeding , severe neutropenia and severe thrombocytopenia ) can be associated with an aggressive bone marrow involvement , but also seem to be proxy of a systemic , severe , imbalanced response to the pathogen . Although predictors can help identifying higher risk patients , thus informing practice , studies addressing earlier risk markers would be welcomed . The role of genetic susceptibility and epigenetic interactions modulating host inflammatory response needs further understanding .
|
Visceral leishmaniasis ( VL ) is a deadly disease caused by a protozoan called Leishmania . It is transmitted to humans from infected animals by a sandfly bite . Most people actually manage to control the infection and do not get sick , while others develop a range of symptoms . VL impairs the production of blood components and causes the immune system to malfunction , thus anemia , bleeding , and bacterial infections often complicate the disease and can lead to death . To identify risk factors for death from VL , the authors studied 546 children in a referral center in Recife , Brazil . They looked at clinical history , physical examination and full blood counts on the assumption these could be easily assessed in peripheral health facilities . They found that the presence of fast breathing , jaundice , mucosal ( e . g . gum ) bleeding and bacterial infections would each increase the risk of death in three to four-fold . The presence of very low counts of neutrophils and platelets would increase the risk of death in three and 12-fold respectively . This knowledge can help clinicians to anticipate the use of antibiotics or transfusion of blood products in high risk patients , who would potentially benefit from transfer to centers with advanced life support facilities .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases/protozoal",
"infections",
"infectious",
"diseases/neglected",
"tropical",
"diseases",
"public",
"health",
"and",
"epidemiology/infectious",
"diseases",
"pediatrics",
"and",
"child",
"health"
] |
2010
|
Risk Factors for Death in Children with Visceral Leishmaniasis
|
Unlike in most pathogens , multiple-strain ( polygenomic ) infections of P . falciparum are frequently composed of genetic siblings . These genetic siblings are the result of sexual reproduction and can coinfect the same host when cotransmitted by the same mosquito . The degree with which coinfecting strains are related varies among infections and populations . Because sexual recombination occurs within the mosquito , the relatedness of cotransmitted strains could depend on transmission dynamics , but little is actually known of the factors that influence the relatedness of cotransmitted strains . Part of the uncertainty stems from an incomplete understanding of how within-host and within-vector dynamics affect cotransmission . Cotransmission is difficult to examine experimentally but can be explored using a computational model . We developed a malaria transmission model that simulates sexual reproduction in order to understand what determines the relatedness of cotransmitted strains . This study highlights how the relatedness of cotransmitted strains depends on both within-host and within-vector dynamics including the complexity of infection . We also used our transmission model to analyze the genetic relatedness of polygenomic infections following a series of multiple transmission events and examined the effects of superinfection . Understanding the factors that influence the relatedness of cotransmitted strains could lead to a better understanding of the population-genetic correlates of transmission and therefore be important for public health .
Unlike most bacterial and viral pathogens , the malaria parasite P . falciparum , while predominantly haploid , must sexually reproduce in a mosquito vector before infecting a new human host . Sexual recombination has a significant impact on the population genomics of the parasite , and its effects depend on epidemiological conditions such as transmission intensity [1–3] . One outcome of sexual recombination is that parasites transmitted by a mosquito vector can be genetically related , which can be measured as the proportion of the genome that is identical-by-descent ( IBD ) . IBD segments are region of the genome that originate from a recent common parental strain . A number of studies have used IBD to study transmission [4–8] , survey antimalarial resistance [9] , and detect signals of selection [10] . The effects of sexual recombination are also apparent in polygenomic ( multi-strain ) infections . Polygenomic infections can be formed through a series of infectious mosquito bites ( superinfection ) or through the transmission of multiple strains from a single mosquito bite ( cotransmission ) [5 , 7 , 11] . Coinfecting strains resulting from superinfection are assumed to be unrelated while those resulting from cotransmission are assumed to be genetically related [5–7] . While superinfection is believed to be common in high transmission settings , owing to high entomological inoculation rates and complexity of infections ( COI , the number of strains per infection ) [12 , 13] , the frequency with which cotransmission occurs is less clear . Studies of genetic relatedness in symptomatic polygenomic infections reporting to clinics in mid-to-low transmission settings show that cotransmission is prevalent in these regions [5–8] , but little is known of the frequencies of cotransmission and superinfection across transmission settings . Genetic relatedness studies reveal a large amount of variation in the relatedness of polygenomic infections . The fact that sexual recombination occurs within the mosquito suggests that the relatedness in these polygenomic infections is associated with transmission . High relatedness in polygenomic infections could be indicative of serial cotransmission chains [14] , but it is unclear what other factors may influence the relatedness of polygenomic infections . Part of the uncertainty stems from an incomplete understanding of the cotransmission process . When a female Anopheline mosquito bites an individual infected with malaria , she ingests male and female gametocytes . The ingestion of these gametocytes activates them to form gametes that fuse to create a diploid zygote . Gametes can fuse with other gametes of the same genotype , resulting in self-fertilization ( selfing ) , or can fuse with gametes from other genotypes resulting in outcrossing . The zygote undergoes meiosis and develops into a motile ookinete that traverses the midgut epithelial layer and forms an oocyst . Within the oocyst , the parasite undergoes many rounds of mitosis to create thousands of haploid sporozoites . These sporozoites travel to the mosquito salivary glands and are stored until deposited by the mosquito into the human host during a blood meal . Only those sporozoites that invade the liver will survive to continue the malaria life cycle . How then could variation in within-host and within-vector transmission dynamics , such as the number of oocysts formed and the number of sporozoites infecting the liver , affect the relatedness of cotransmitted strains , and how could these variables in turn affect the relatedness of polygenomic infections in natural populations ? To address the complexity of this transmission cascade and better understand the process of cotransmission , we devised a classification framework based on parasite pedigrees and kinships to develop an understanding of how the various sampling and mating events within the mosquito vector affects the relatedness of transmitted sporozoites . We then created a transmission model to quantify the relatedness of cotransmitted strains under a variety of within-host and within-vector dynamics and used this model to examine the relatedness of polygenomic infections in transmission chains . Our study reveals new insights into the cotransmission process , which we believe will be useful for the interpretation of population genomic signals obtained from more complicated population-level models or from natural populations .
To simulate sexual recombination , we developed a P . falciparum-specific meiosis model based on the whole genome sequences of 69 genetically distinct progeny derived from 3 previously generated P . falciparum crosses involving different laboratory-adapted strains ( 3D7 , HB3 , Dd2 , 7G8 , and GB4 ) [15–19] . The whole genome sequences generated from these crosses are one of best sources of data for designing a P . falciparum-specific meiosis model because the genotypes of the parental strains are known . Furthermore , we can be confident of the number of sexual reproduction cycles separating progeny and parental strains . While previous IBD analyses of parasites from natural parasite populations have identified putative F1 progeny [5 , 7 , 20 , 21] , having complete knowledge of parental ancestry simplifies the identification of IBD segments and allows us to better identify recombination events throughout the genome . We calculated the number of crossover events and inter-crossover distances ( S1 Fig & S1 Table ) using a hidden Markov model ( HMM ) [4 , 22] to identify IBD segments shared between progeny and parental strains ( Methods ) . We then used this data to test the fit of two different meiosis models , one with and one without obligate chiasma formation . Both were based off the gamma model of crossover formation , which has been used to characterize recombination events in a wide variety of taxa , including H . sapiens , D . melanogaster , and S . cerevisiae [23–26] . The gamma model is an improvement over simpler Poisson-based crossover models because it allows us to explore a wide range of crossover interferences . Regardless of whether obligate chiasma formation was modeled , the number of crossover events and intercrossover distances in our simulated meiotic events resembled those of the laboratory-crossed progeny ( Fig 1A and 1B ) . However , both meiosis models underestimated the frequency of short intercrossover distances ( < 50 cM ) ( Fig 1A ) , which we suspect is because our HMM overestimated the frequency of short intercrossover distances in the laboratory-cross data ( S2 Fig ) . We found that the obligate chiasma model generated crossover events that were more consistent with that of the laboratory-crossed progeny , but overestimated the number of chromosomes with two crossover events . Using a pseudo-likelihood function ( Methods ) , we determined that an obligate chiasma model fit the data better than a non-obligate chiasma model ( Fig 1C ) . However , we could not estimate the level of crossover interference . Because crossover interference is observed in a wide-variety of organisms spanning multiple taxa [23] , we chose to use an obligate chiasma meiosis model with a weak level of interference ( gamma distribution with shape = 2 , scale = 0 . 38 ) for all of our transmission simulations . We then designed a transmission model that partitions transmission into three steps: 1 ) The host-vector sampling of gametocytes from an initial host infection 2 ) the sequence of events starting from gamete fusion and meiosis to the development of the oocyst within the mosquito vector , and 3 ) the vector-host injection of sporozoites and subsequent invasion of the liver to determine the genetic composition of the next human host ( Fig 3 ) . We initiate our model by simulating a mosquito blood-feeding event on a polygenomic infection comprised of unrelated strains and parameterized by 1 ) COI , 2 ) oocyst count , and 3 ) the infected hepatocyte count . The number of unique strains present in the initial infection is determined by COI . In our model , we consider oocyst formation as the final outcome of gamete fusion and subsequent meiosis . Based on the oocyst count , our model samples gamete pairs , which fuse and undergo meiosis to create an oocyst consisting of four unique meiotic products . Competition within the oocyst is not modeled and we assume that each meiotic product is present at equal proportion in the oocyst . After all oocysts are created , the model samples sporozoites according to the infected hepatocyte count to determine the genetic composition of the subsequent host infection . If the resulting infection harbors multiple strains , we calculated the relatedness of cotransmitted strains as the average pairwise relatedness between each of the unique genotypes present in the final host infection . The values for the infected hepatocyte count are pre-specified and drawn from the set {1 , 2 , 3 , 4 , 5 , 10 , 20} . Simulations with COI = 1 were excluded because they always result in selfing and the transmission of genetic clones . Simulations with an infected hepatocyte count = 1 were also excluded , as they cannot result in cotransmission . Small values are overrepresented to reflect the right-skewed distributions of oocyst counts observed in mosquito feeding assays and infected hepatocyte counts estimated from a malaria-challenge study [27–29] . These values also include the COI observed in naturally occurring polygenomic infections from mid-to-low endemic settings ( COI ranging from 2–6 in polygenomic infections ) . From our pedigree/kinship framework , we knew that sporozoites sampled from a single oocyst would be either genetic clones or meiotic siblings . Our transmission simulation confirmed this prediction and found that the expected relatedness of cotransmitted strains in single-oocyst transmission simulations was always 0 . 33 ( Fig 4 ) , which is the expected relatedness of genetically distinct meiotic siblings . In single oocyst transmission simulations , cotransmission can only be achieved by the transmission of two or more genetically distinct meiotic siblings . The distinction between genetically distinct and genetically identical meiotic siblings is relevant in the context of cotransmission , as the transmission of clonal meiotic siblings cannot result in cotransmission . Changes to the infected hepatocyte count do not affect the expected relatedness values , but higher infected hepatocyte counts caused the distribution to be more concentrated around the mean . In multiple oocyst transmission simulations , the relatedness of cotransmitted strains is not as easy to predict , since multiple kinships can be transmitted . Based on our pedigree/kinship framework , we hypothesized that COI modulates the expected relatedness of cotransmitted strains by limiting the transmission of half-siblings and unrelated strains; the transmission of half-siblings and unrelated strains described by pedigrees 6 are only possible when COI ≥ 3 . The transmission of unrelated strains described by pedigree 9 only applies when COI ≥ 4 . Our transmission simulations confirmed these predictions and revealed a simple relationship between COI , oocyst count , and the relatedness of cotransmitted strains ( Figs 5 and 6 ) : the relatedness of cotransmitted strains declines with increasing COI . All COI = 2 simulations have an expected relatedness > 0 . 33 , with a larger increase in high oocyst count simulations . The increase in relatedness is a reflection of the increased transmission of full-siblings and parent-offspring strains . When COI = 3 , increasing oocyst counts no longer increased the expected relatedness of cotransmitted strains due to the additional transmission of half-siblings . Once COI > 4 , increasing oocyst counts decreased the expected relatedness of cotransmitted strains . This was due to the increased transmission of unrelated strains , particularly those described by pedigree 9 ( outcrossed oocysts that do not share any parental strains ) ( Fig 6C and 6D ) . When COI = 20 , the majority of transmitted parasites are either meiotic siblings or unrelated strains described by pedigree 9 . We found that different infected hepatocyte counts altered the distribution of relatedness ( S4 Fig & S5 Fig ) but had no effect on the trends established by either COI or oocyst count . Again , simulations with a COI = 2 consistently had the highest expected relatedness values while simulations with higher COIs had lower expected relatedness values , regardless of the infected hepatocyte count . Thus far , our simulations have assumed that the strains making up polygenomic infection are present and sampled in equal proportions . However , strain proportions in natural polygenomic infections can be highly skewed . Furthermore , different strains can have different transmissibility relating to factors such as gametocyte production . To investigate how skewed gametocyte sampling probabilities could affect the relatedness of cotransmitted strains , we devised a weighted sampling scheme defined by the ratio of the most frequent to the least frequent strain in the infection ( Methods ) . Predictably , skewing the gametocyte strain ratios increased the rate of selfing and the transmission of genetic clones ( S6 Fig ) . Skewed ratios of up to 10:1 increased relatedness of cotransmitted strains by a small amount . Ratios ranging from 1:1 to 10:1 increased the expected relatedness of cotransmitted strains by 0 . 01–0 . 10 . This increase depended on both COI and the magnitude by which strains proportions differed . The relatedness of cotransmitted strains from high COI infections was more robust to differences in strain proportions; a 10:1 ratio in a COI = 20 infection increased relatedness by only 0 . 02 while a 10:1 ratio in a COI = 3 infection increased relatedness by 0 . 03–0 . 06 . The genetic composition of natural polygenomic infections can result from multiple transmission events and influenced by population-level transmission dynamics . However , developing a model that take into account all possible population-level transmission dynamics is beyond the scope of this paper . Instead , we used our model to quantify the relatedness of polygenomic infections in three different multiple transmission simulations , which we refer to as transmission lineages . Each transmission lineage is designed to resemble transmission chains that occur in natural populations and initiated by simulating a mosquito blood-feeding event on a polygenomic infection comprised of unrelated strains . The first transmission lineage does not allow superinfection; all subsequent transmission events in the chain must infect uninfected hosts . The second and third transmission lineages allow superinfection and are differentiated by the nature of the resident strain in the soon-to-be superinfected host . For the second transmission lineage , the resident strain is identical to one of the parental strains in the initial polygenomic infection ( resembling natural backcrossing events ) . For the third transmission lineage , the resident strain is not related to any of the parental strains in the initial polygenomic infection but is the same in all transmission events . In the last transmission lineage , the resident strain is not related to any of the parental strains in the initial polygenomic infection and is different in all transmission events . For our transmission lineage simulations , we modified our cotransmission model so that oocyst and infected hepatocyte counts are determined by randomly sampling from distributions reflecting those of found in previous studies [29 , 30] . Subsequent transmission events sample parasites from the infection generated by the previous transmission event . Allowing oocyst and infected hepatocyte counts to be chosen from these distributions did not affect the previously observed relationship between COI and the relatedness of cotransmitted strains ( S7 Fig ) . The relatedness of cotransmitted strains following single cotransmission events from infections COI = 2 had an expected relatedness greater than 0 . 33 while those with a COI > 3 had an expected relatedness less than 0 . 33 . As expected of serial cotransmission chains , we found that the relatedness of polygenomic infections increases with each transmission event ( Fig 7 ) . Transmission lineages with superinfection had lower relatedness values and smaller proportions of serial transmission simulations that converged to the transmission of single strains . The reduction in relatedness was greatest in those where the resident strain was unrelated to the parental strains of the original infection ( Fig 7 , purple ) . Changing the resident strain after each transmission event prevented the relatedness of polygenomic relatedness from increasing beyond 0 . 10 even after five transmission events . We also saw that the COI of the initial infection could have a lasting effect on the relatedness of polygenomic infections . Transmission lineages initiated with low COI polygenomic infections had higher relatedness values than those initiated with high COI polygenomic infections . This effect was weaker in superinfection lineages with unrelated resident strains . While skewed gametocyte-sampling ratios had a modest effect on the relatedness of polygenomic infection , it drastically increased the rate with which transmission lineages converged to the transmission of single strains for all transmission lineages except the one where unrelated resident strains were changed after each transmission event ( Fig 7B and 7D ) .
Parasite strains in polygenomic infections are often genetically related , but it is unclear why there is so much variation between infections or whether the relatedness of polygenomic infections can be used to understand parasite transmission . In order to help bridge the gaps in our understanding , we developed a pedigree/kinship framework for understanding how COI and oocyst counts affect the relatedness of cotransmitted strains . We then tested the predictions of this framework using a parasite transmission model to quantify changes in the relatedness of cotransmitted strains . We demonstrated that multiple oocyst simulations in low COI conditions favor the transmission of full-siblings / parent-offspring strains and limit the transmission of half-siblings and unrelated strains , causing an increase in the expected relatedness of cotransmitted strains . Multiple oocyst simulations in high COI conditions decrease the relatedness of cotransmitted strains by favoring the transmission of half-siblings and unrelated strains . Alterations to the number of sporozoites that invade the liver have little effect on relatedness , conditioned on the fact that multiple sporozoites invade . We also examined how non-uniform gametocyte-sampling probabilities could affect the relatedness of cotransmitted strains . Previous studies have established that intra-host parasite dynamics depend on patient age [31 , 32] disease severity ( reviewed in [33] ) , and eco-epidemiological factors such as seasonal transmission [34 , 35] . These dynamics are strongly influenced by host immunity [36] and can fluctuate over the course of a single infection [32 , 37–40] . Furthermore , gametocyte sampling is not completely random [41] and not reliant on peripheral blood gametocyte densities at low parasitemias [34 , 42] . Our results show that the relatedness of cotransmitted strains is robust to variations in intra-host strain proportions and gametocyte-sampling probabilities . Even infections where the ratio of the most frequent to least frequent strain is 10:1 do not result in drastic changes to that observed from infections with even strain proportions . This suggests that the relatedness of cotransmitted strains is consistent across differences in patient-age , disease severity , and host immunity . Our results are in agreement with the frequent assumption that cotransmission events are comprised of genetically related parasite strains [5–8] . A large fraction of simulated cotransmission events result in the transmission of genetically distinct meiotic siblings , as evidenced by the peaks at 0 . 33 for all simulations where oocyst counts and hepatocyte counts were randomly sampled . However , we also found that the transmission of unrelated strains is a major aspect of cotransmission . The cotransmission of unrelated strains was present in all multiple oocyst simulations and increased in frequency with COI . Polygenomic infections comprised of unrelated strains are typically assumed to be the result of superinfection , but these findings suggest that some are the result of cotransmission . Current estimates of the prevalence of cotransmission are underestimates , since they rely on the subset of cotransmission events resulting in polygenomic infections comprised of genetically related strains [7] . Our results reveal an inverse relationship between the relatedness of cotransmitted strains and COI . COI is correlated with high entomological inoculation rates [43 , 44] and a known genetic correlate of transmission intensity [43 , 44] . COI is higher in high transmission areas than in low transmission areas due to increased superinfection rates . The association between the relatedness of cotransmitted strains and COI suggests that polygenomic infections in low transmission areas are comprised of more related strains than those in high transmission areas . We previously found that the average relatedness of 32 symptomatic polygenomic patients collected from a clinic in a low transmission region of Senegal ( mean COI of two ) was 0 . 38 [7] . This value exceeds the expected relatedness of meiotic siblings and may reflect an increase in the transmission of full-siblings / parent-offspring parasites but could also result from factors such as population structure . Previous studies of genetic relatedness have focused on areas of mid-to-low transmission setting [5–8] and a comparison of genetic relatedness of polygenomic infections across transmission settings have yet to be performed . High relatedness from low COI infections could have implications for the spread of drug resistance traits in low transmission settings , as the increased relatedness could increase the chance that multi-locus drug resistant genes are passed on together to the next generation . It remains to be seen whether the relationship between relatedness and COI can be reflected in polygenomic infections collected from natural parasite populations . If the inverse relationship between COI and relatedness holds , then the relatedness of coinfecting strains could be a potential population genetic correlate of transmission intensity . Population genetic correlates of transmission are valuable in the context of malaria control and can be used to supplement or supplant traditional epidemiological measures , which can be difficult to collect in low transmission areas [44 , 45] . With regards to polygenomic infections , only the frequency and COI of polygenomic infections are known to correlate with transmission intensity [4 , 44 , 46] . Other population genetic metrics , such as parasite clonality [44] , currently rely on data obtained from monogenomic infections , which are limited in high transmission areas where polygenomic infections are frequent . By providing an additional source of information , genetic relatedness could increase the granularity by which we use genetic signals to monitor changes in transmission . However , spatial-temporal transmission , such as the seasonality or the existence of transmission hotspots , and host immunity can influence population genetic structure [36] . Neither of these are taken into consideration in this study , and it is unclear how these might affect polygenomic relatedness . Population-level models and epidemiological sampling will be needed to understand the effects of cotransmission and establish whether the relatedness of polygenomic infections correlates with transmission intensity . An alternative method of dissecting population-level dynamics is to focus on the characterization of transmission lineage . Transmission lineages consist of chained transmission events and are a simplification of the transmission processes within populations . Our transmission lineages were designed to examine the effect of multiple transmission events and to examine how the co-occurrence of superinfection affects the relatedness of polygenomic infections . They show that superinfection depresses the relatedness of polygenomic infections , but also show how sensitive these lineages are to the conditions of the host infection . Strikingly , they show that cotransmission fails to increase the relatedness of polygenomic infections if each host in the transmission chain harbors a different , genetically unrelated parasite strain . They also reveal the fragility of serial cotransmission chains . In the absence of superinfection , serial cotransmission chains quickly converge to the transmission of single strains . High COI in the initial infection delays this process but a large fraction of serial cotransmission chains still converge within five transmission events . Because these transmission lineages are analogous to the introduction of a polygenomic infection to a new population , polygenomic relatedness could be useful for studying transmission in import scenarios . In conclusion , our study uses a model of parasite transmission to provide mechanistic insight into the process of cotransmission to help understand the factors that influence the relatedness of cotransmitted strains . Understanding the effects of sexual recombination and transmission on malaria population genomics is of key public health interest in an era where parasite populations are experiencing rapid declines in transmission intensity . We believe mechanistic models such as the one used in this study reveal new insights that can be applied to the results obtained from more complicated conditions . Our model highlights the importance of COI in influencing the relatedness of cotransmitted strains , but future models and epidemiology studies are needed uncover how transmission intensity and cotransmission affects the genetic composition of strains in polygenomic infections in natural populations . These models should incorporate background parasite population structure and genetic diversity to understand the effects of cotransmission and establish whether the relatedness of polygenomic infections correlates with transmission intensity . Such models will rely on genetic data collected from well-characterized epidemiological settings to determine whether the relatedness of polygenomic infections is a potential population genetic correlate of transmission .
We simulated meiosis under two different frameworks: one with and one without obligate chiasma formation . Both frameworks sample from a constrained gamma distribution where the average distance between randomly sampled distances is 50 centimorgans to determine the location of chiasma along a bivalent [47 , 24] . For each placed chiasma , our meiosis model chose one sister chromatid from each homolog to undergo recombination . Sister chromatids were independently chosen for each recombination event . Once all recombination events were complete , the model independently segregated and randomly combined sister chromatids from other bivalents to create haploid parasite genomes . For the non-obligate chiasma framework , our meiosis model placed the first chiasma 105 base pairs before the beginning of each chromosome . It then drew a distance , d , from a gamma distribution with shape = v and scale = 1/ ( 2v ) [47] to determine the location of the next chiasma . New chiasma were placed d units after the previous chiasma and a new distance was drawn for each chiasma . Chiasma locations were filtered to include only those that fell within the boundaries of the chromosome under consideration . For the obligate chiasma framework , the position of the first chiasma was determined by drawing from a uniform distribution that spans the length of each chromosome . Subsequent chiasma were placed by drawing distances from a constrained gamma distribution ( described in the next paragraph ) and placing the next chiasma d units before it . This was repeated until the start of the chromosome was reached . Afterwards , the process was repeated in the other direction until the end of the chromosome was reached . Due to the forced placement of chiasma , we could not use the formulas used in the non-obligate chiasma framework to generate appropriately constrained gamma distributions . We used an approximate Bayesian computation ( ABC ) Markov chain Monte Carlo ( MCMC ) to solve the appropriate scale parameter and shape parameters . Shape parameters varied from 1–9 and scale parameters were sampled from a uniform distribution with a range of 0–5 . For each set of scale and shape parameters , we counted the number of chiasma on a bivalant 100 centiMorgans ( cM ) in length and repeated this process 1000 times to estimate the average and standard deviation . We evaluated the fit of each proposed parameter using the following distance metric: D′= ( 2−u ) 20 . 052+δ2 where u and δ are the simulated mean and standard deviations of the number of chiasma , 2 represents the desired number of chiasma per 100 centiMorgans , and 0 . 05 represents a small error term . We then constructed an estimate of the pseudo-likelihood as: L=1eD′ The proposed scale parameter was accepted if the proposed pseudo-likelihood was greater than the pseudo-likelihood of the previously proposed parameter . If the new pseudo-likelihood was smaller , then the probability of rejection was decided by the ratio of the current pseudo-likelihood over the previous pseudo-likelihood . This process was repeated 2 , 500 times to form a MCMC chain . After our MCMC chain was completed , we calculated the mean of the accepted scale parameters from the last 1500 steps to serve as our estimate of the scale for each shape parameter . We calculated the average number of crossover events and intercrossover distances for each chromosome in the genome using SNP data from 69 genetically distinct progeny generated from 3 different laboratory crosses [15 , 17–19] . These data were previously generated by the Pf3k project ( https://www . malariagen . net/projects/pf3k ) [15–19] . VCF files were downloaded and filtered based on the available INFO strings . We removed non-Mendelian sites , sites that did not pass the quality filters used , and sites that were invariant between the parental strains used in the cross . Samples from each laboratory cross were represented by an average of 1028 SNPs . From this filtered dataset , we performed pairwise calculations of percent similarity to identify and remove duplicate strains . Duplicate strains were defined as those having greater than 90% SNP similarity . For each chromosome , we used a modified version of an IBD Hidden Markov Model ( HMM ) [4 , 22] to quantify the average number of crossover events and the average intercrossover distance for each chromosome . Our previously published HMM relied on population SNP frequencies to infer IBD , which is problematic when using cultured strains with vague demographic histories . For each laboratory cross , we used SNP data to infer IBD between progeny and parental strains using the following emission probabilities: P ( Concordance|IBD ) = ( 1−ε ) 2+ ( ε ) 2 P ( Concordance|non−IBD ) =2ε ( 1−ε ) P ( Discordance|IBD ) =2ε ( 1−ε ) P ( Discordance|non−IBD ) =1−2ε ( 1−ε ) where ε refers to the rate of sequencing error , concordance refers to having the same SNP identity , discordance refers to having different SNP identities , and IBD refers to identical-by-descent . The resulting IBD maps closely mirror the parental inheritance boundaries specified in [16] , but sometimes identifies very short IBD fragments that are unlikely to be real ( S5 Fig ) . Crossover events were identified as the points in the chromosome where the IBD map switches from IBD to non-IBD and intercrossover distance was calculated as the distance ( in cM ) between each of the identified crossover points . Intercrossover distances were converted to centiMorgans using the estimates reported in [15 , 16] ( 15 kb/cM ) . If no crossovers were observed , then the intercrossover distance was defined as the length of the entire chromosome . We then used the average number of crossovers and intercrossover distances to determine whether a non-obligate or obligate chiasma model of meiosis would fit the data better . Each simulation was run 20 times to get an average and standard deviation of the number of crossover events and crossover distances per chromosome . We then devised a distance metric defined as: Dj=∑i14 ( ui , sim−ui , observed ) 2δ2i , sim+δ2i , observed where u is the mean , δ is the standard deviation , j is the feature ( number of crossover events or intercrossover distance ) , i is the chromosome number , sim indicates the simulation result , and observed indicates the value observed in the 69 progeny strains . We defined a pseudo-likelihood as L=∏j21eDj and used it to determine the model that fit the data better . To quantify the average relatedness of cotransmitted strains , we developed an agent-based mosquito transmission model that simulates the sampling processes that occur as parasites enter and exit the mosquito vector and parameterized by COI , oocyst count , and infected hepatocyte count . The values for oocyst count and infected hepatocyte count were drawn from the set {1 , 2 , 3 , 4 , 5 , 10 , 20} while the values for COI were drawn from the set {2 , 3 , 4 , 5 , 10 , 20} . Each set of parameters was run 2000 times . Each simulation was initiated by creating an initial infection comprised of unrelated parasite strains; the number of strains within the initial infection was determined by COI . To model differences in intra-host strain proportions and differences in sampling probabilities , we assumed that strain proportions followed an exponential equation of the form: f ( x ) =AeBx where x is a discrete variable representing each strain in the infection . We used an exponential equation to magnify the difference in frequency between the most frequent strain and the other strains present in the infection . For an infection with COI = n , x ranges from 0 to n -1 . We fit this equation to two points , ( 0 , f ( 0 ) ) and ( n—1 , f ( n—1 ) ) , based on the ratio of the most frequent to the least frequent strain in the infection . These ratios ranged from 1:1 to 10:1 , reflecting the observed strain proportions in a set of polygenomic infections collected from Thiès , Senegal ( S8 Fig ) . f ( 0 ) is the ratio of the most frequent to the least frequent strain . f ( n—1 ) is the ratio of the final strain to the least frequent strain and always equal to one . The ratios of all other strains present in the infection was determined by f ( 1 ) , f ( 2 ) , …f ( n-1 ) . We then drew from a Dirichlet distribution with a concentration parameter = {f ( 0 ) , f ( 1 ) , f ( 2 ) , … f ( n—1 ) } 1000 times to calculate the expected frequency of each strain in the infection . Based on the specified oocyst count , our model sampled gametocyte pairs by their intra-host strain proportions to create oocysts , allowing for multiple samplings of the same strain . Each sample pair underwent meiosis to create four meiotic products . The progeny from all the meiotic events were combined without the removal of repeat strains to represent the sporozoites within the mosquito vector . Our model assumed mating success and oocyst formation could be simulated as the random sampling of gametocytes from the human host . It is unclear whether the parasite has a preference for self-fertilization or outcrossing . Evidence for non-random mating is based on the observation of highly inbred oocysts within the mosquito midgut [48] , but it is unknown to what extent self-fertilization occurs more frequently than expected by chance . We then sampled sporozoites to represent the strains in the infected hepatocytes . Multiply-infected hepatocytes were not allowed . At this point , our model performed pairwise comparisons between all the parasites in the infected hepatocytes , regardless of whether or not the pair consisted of genetically distinct parasites , to determine the frequency of the different pedigrees specified in Fig 2 . The expected relatedness of cotransmitted strains was calculated as the average pairwise relatedness between genetically distinct strains . This average is not weighted by the frequency of strains within the infected hepatocytes . Because cotransmission must result in the creation of polygenomic infections , we excluded infections where the infected hepatocytes consisted of a single strain . When an infected hepatocytes consisted of two or more genetically distinct strains , the relatedness of cotransmitted strains was calculated as the relatedness between the two strains; when an infection was comprised of 20 genetically distinct strains , the relatedness of cotransmitted strains is calculated as the average pairwise relatedness from all 20-choose-2 comparisons . Source code is available on GitHub , under the project name Cotransmission ( https://github . com/weswong/Cotransmission ) . The code is written using Python 2 . 7 . 0 and is platform independent . We defined relatedness as the proportion of the genome that is identical-by-descent ( IBD ) owing to inheritance from the same common ancestor . Because the genetic ancestry of all input strains was known and assumed to be genetically unrelated , IBD segments were identified as segments of the genome that originated from the same parental input strain . To calculate the expected relatedness of parasites described by our 9 pedigrees , we generated simulations with the appropriate number of oocysts ( 1 or 2 ) , the appropriate pedigreess for each oocyst , and the appropriate method of sampling parasite pairs ( within or between oocysts ) for each pedigree and quantified the relatedness of a single randomly drawn parasite pair . This process was repeated 800 times to generate distributions of relatedness and to get an estimate of the mean .
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Genomic studies of P . falciparum reveal that multi-strain infections can include genetically related strains . P . falciparum must reproduce sexually in the mosquito vector . One consequence of sexual reproduction is that parasites cotransmitted by the same mosquito are related to one another . The degree of genetic relatedness of these parasites can be as great as that of full-siblings . However , our understanding of the cotransmission process is incomplete , and little is known of the role of cotransmission in influencing population genomic processes . To help bridge this gap , we developed a simulation model to determine which of the steps involved in transmission have the greatest impact on the relatedness of parasites cotransmitted by a mosquito vector . The primary goal of this study is to characterize the outcomes of cotransmission following single or multiple transmission events . Our model yields new insights into the cotransmission process , which we believe will be useful for understanding the results from more complicated population models and epidemiological conditions . Such an understanding is important for the use of population genomics to inform public health decisions as well as for understanding of parasite evolution .
|
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2018
|
Modeling the genetic relatedness of Plasmodium falciparum parasites following meiotic recombination and cotransmission
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Many bacteria exhibit multicellular behaviour , with individuals within a colony coordinating their actions for communal benefit . One example of complex multicellular phenotypes is myxobacterial fruiting body formation , where thousands of cells aggregate into large three-dimensional structures , within which sporulation occurs . Here we describe a novel theoretical model , which uses Monte Carlo dynamics to simulate and explain multicellular development . The model captures multiple behaviours observed during fruiting , including the spontaneous formation of aggregation centres and the formation and dissolution of fruiting bodies . We show that a small number of physical properties in the model is sufficient to explain the most frequently documented population-level behaviours observed during development in Myxococcus xanthus .
Bacteria are able to sense their surroundings in order to adapt to environmental change . Most bacteria live in dense populations , therefore other cells constitute a major part of their physical and chemical environment allowing regulatory interactions between cells to be established . The benefits of coordinated behaviour include: more efficient proliferation resulting from a cellular division of labour , access to resources that cannot be utilised by isolated cells , defence against antagonists and population survival by differentiation into distinct cell types [1] . Myxobacteria are Gram-negative , ubiquitous , soil dwelling bacteria that are semi-flexible , and rod-shaped . Cells glide across a surface using the adventurous ( A ) and the social ( S ) motility systems [2] . S-motility is coordinated at the leading pole; cells extend type IV pili which can adhere to the surface of other bacteria or polysaccharides , and upon retraction the cell is pulled forward . A-motility is coordinated at the lagging pole; cells are thought to extrude a slime which expands and generates a propulsive force to push cells forward [3] , [4] . Myxobacteria display distinct social phenotypes and multicellular behaviours . Myxococcus xanthus is the most commonly studied species of myxobacteria . In response to starvation , cells undergo multiple phases of behaviour culminating in the formation of fruiting bodies and myxospores . The developmental process involves a series of macroscopic changes in colony morphology . A key regulator of development is C-signal ling which occurs when C-signal , a cell surface-associated signal encoded by csgA , is exchanged between cells in close contact with one another . C-signal stimulates the expression of csgA leading to a rise in C-signal ling throughout development from positive feedback . Different colony morphologies are a consequence of different C-signal ling levels [5] . C-signal ling is thought to affect the reversal frequency of individual cells in a contact-dependent fashion allowing the synchronisation of behaviour [5]–[7] . During vegetative growth cells move in the direction of their long axis , reversing typically once every ten minutes [8] . Under starvation conditions , C-signal accumulates within a cell [5] reducing its reversal frequency [9] . The reduction in the reversal frequency and the effects of A and S motility causes cells to form streams and increases the likelihood of aggregation; cells which cannot reverse tend to remain stuck in one location since their ability to move around obstacles is limited by only being able to move forward [10] . M . xanthus cells begin to form fruiting bodies after a prolonged starvation period of approximately 24 h . Starved cells form into large , intricate multicellular aggregates containing between 50 , 000 and 100 , 000 cells [11] . The fruiting body is the precursor to sporulation where cells undergo morphogenesis and physically change shape from rods to nearly spherical cells [12] . Inside the nascent fruiting body , a percentage of the cells differentiate into dormant myxospores . This process requires both temporal and spatial coordination in three dimensions , making it one of the most complex and least understood phases of the life-cycle . Relatively little is known about the spatial dynamics of fruiting body construction with research primarily devoted to understanding the signalling mechanisms required to coordinate development rather than the actual physics [13] . There is some disagreement over how fruiting actually begins . O'Connor and Zusman [11] , [14] observed that cells appear to orbit around a largely stationary aggregation centre . This led to the traffic jam model , which proposes that streams of cells collide together causing the formation of a kernel of stationary cells . Cells move around and over the static centre leading to a mound formation [15] . Work on Stigmatella fruiting body formation showed that cells form circular orbits around a base and then move upwards in a spiral fashion around the base , building the stalk on top of it [14] , [16] . It was presumed other myxobacteria , including M . xanthus , form fruits in a similar way; however , Kuner and Kaiser [17] did not observe the spiralling patterns suggesting that this behaviour is possibly non-essential and may not be intrinsically important to fruiting development . Recent work by Curtis et al . [13] suggests that fruiting bodies are formed using a stepped layer building approach; large streams of cells forming sheets collide causing a rapid build up in density at the meeting point . Cells in one of the opposing streams are forced upwards and over the other , similar to tectonic plate movements . The displaced cells are supported on top of the dense layer of cells and extra-cellular polysaccharide ( EPS ) underneath and begin to spread out forming a new layer . As the new layer becomes more dense itself , cells at the centre start to get pushed upwards to form a new layer and the process repeats causing the formation of an expanding mound of cells . Previous computational models of fruiting body development [18]–[20] are based on the orbiting traffic jam model and rely upon the artificial induction of an aggregation centre to start fruiting body development , typically by making a subset of cells stationary . In this paper , we take a different approach and use an off-lattice Monte Carlo simulation to show how cells can spontaneously aggregate to form layers and fruiting bodies based on the observations of Curtis et al . [13] . The motivation of this work is to gain an increased understanding of fruiting , by examining the physical properties driving cells to engage in fruiting , using mathematical and computational modelling .
Periodic boundary conditions were disabled in the -plane since it does not make sense for cells to be able to push through the floor nor move through the ceiling for which there is no physical interpretation . Boundaries are maintained with a boundary energy term which severely penalises a cell for attempting to cross a particular domain boundary . The energy penalty is several orders of magnitude larger than the value any of the other energy terms might produce so it is nearly impossible for a configuration with these domain crossings to be favourable . Fruiting body formation requires a highly dense region of cells to seed aggregation . To achieve such a density at the start of simulation would require cells to be placed so that they fill all available space on the floor of the simulation volume . Even under these conditions , the cell density is usually not sufficient to seed fruiting , and the lack of space for movement would inhibit cell motility . Biologically , fruiting bodies are thought to form from the confluence of streams of cells resulting in the cell density increasing over time [4] , [22] . To capture this behaviour in the simulations , entry zones were placed around the edges of the simulation volume ( see Figure S3 ) . Entry zones allow new cells to be introduced into the simulation over time to model cell influx . A maximum influx rate ( ) can be specified to govern how quickly new cells enter the simulation volume . The actual influx rate is stochastic and less than the maximum influx rate , , and is determined by the amount of free space within the entry zones where new cells can be placed . New cells are placed at random locations by periodically sampling the entry zones to see if there is free space to place a cell and then placing a cell if the maximum influx rate ( ) has not been exceeded . Fruiting requires a high cell density and simulating a finite number of cells makes it problematic to assemble enough cells in an area to form a fruit; the cell density is never high enough . A finite number of cells may clump and partially aggregate but they are unlikely to form a fruiting body . With the entry zone model , a constant cell density can be maintained to sustain fruiting body growth . Cell reversals are thought be controlled by C-signal stimulating the complex Frz pathway , however the exact function of each component has yet to be determined [23] . We therefore model the macroscopic behaviour of the pathway , where an internal phase switch is used as an abstract representation of C-signal . The switch increments until it reaches at which point it resets and the cell reverses . The switch can be perturbed by signalling between neighbouring cells to make reversals happen more quickly , by a factor proportional to the number of collisions a cell experiences with its neighbouring cells . The function is therefore: ( 1 ) ( 2 ) where is the new cumulative value , is the current value , is a basal increase factor , is the signal strength , and is the level of C-signal ling a cell experiences at time , defined by the collisions a cell experiences with each of its neighbours and a collision factor . In this work we keep the model of C-signal ling quite simple , as our goal is to explore other factors which can facilitate the formation of aggregates and fruiting bodies . Experiments indicate that even 15 hours into starvation , levels of C-signalling are sufficient to reduce the rate of reversals to once every 22 minutes [24] . Moreover , these experiments show that the slowdown in reversal induces a 15-fold increase in travel distance , in what could be considered a ‘unidirectional behaviour’ . We approximate this low frequency of reversals by considering cells which have come near to an aggregation as non-reversing , reflecting the approach taken in other simulations [18] , [19] . Nevertheless , in simulations of fruiting C-signal ling levels and collisions are monitored , enabling the imposition of a threshold C-signal ling level governing the induction of sporulation . Figure 1 describes the program used for simulation . The Metropolis algorithm [25] is used to determine the acceptance probability of making any particular change . Simulations were carried out using a volume equivalent to m . The model captures the physical dynamics of the cells using the method proposed by Glazier and Graner [26] . A Cellular Potts Model is a probabilistic Cellular Automata with Monte-Carlo updating , where the next state of the lattice is chosen by evaluating a Hamiltonian equation used to calculate the probability of accepting lattice updates . The original Potts model [27] was developed to capture behaviour at the level of statistical mechanics but has been successfully generalized for a variety of domains . The tuning of a Cellular Potts Model is based on finding an appropriate Hamiltonian function and appropriate parameters for this function . The heart of our model is the development of a set of terms that correctly describes important physical characteristics of the M . xanthus cell ( see Figure 2 ) . The level of detail used needs to be balanced with the computational cost of these calculations . The following Hamiltonian function , inspired by the approach of Izaguirre et al . [28] , describes the energy components of M . xanthus we use: ( 3 ) A separate collision resolution algorithm such as used by Wu et al . [29] was not required since collision avoidance is a feature of the Hamiltonian . In the following presentation of each of the components of the Hamiltonian , we use boldface fonts to indicate vectors , and the cap operation to denote an average or mean vector . M . xanthus cells are modelled as having a finite volume and stable shape; cells can be squashed to an extent but they maintain a rod shaped structure except during sporulation . Cell length governs a cell's length and is analogous to the spring constant in Hooke's Law . ( 4 ) where is a dimensionless stretching coefficient , is the number of segments in cell , is the optimal distance between segments , is the vector position of segment in cell and a dimensionless stretching coefficient . Stretching energy is defined as a squared sum which compares the distance between the centres of neighbouring segments and to and penalises a cell for allowing segments to get either too close or too far apart . In close proximity , cells tend to align with each other reflecting the effect of the S-motility engine . Cells extend Type IV pili from their leading pole which grab onto neighbouring cells . Upon retraction this pulls a cell closer to the neighbour it latched onto [4] . The natural consequence of this movement is the alignment of cells [30] . ( 5 ) ( 6 ) ( 7 ) ( 8 ) ( 9 ) where is a dimensionless alignment coefficient , is the normalised average direction of the cell , is the average direction of all the cells in a local neighbourhood surrounding cell . reflects that cells tend to turn through the acute angle to align with other cells in either direction . Each cell in the model has a semi-flexible body which must maintain a certain stiffness , otherwise the cell would fold up upon itself . Incorporating bending energy in the Hamiltonian ensures that the radius of curvature of a cell does not exceed a threshold causing the cell to flail uncontrollably and unnaturally . ( 10 ) ( 11 ) ( 12 ) ( 13 ) ( 14 ) ( 15 ) ( 16 ) where is a dimensionless bending coefficient , returns the angle between and , is the average direction of segment of cell and is the vector between the segment ahead of ( ) and the segment behind ( ) . The A-motility system provides myxobacteria cells with propulsion . Cells extrude a polysaccharide slime from nozzles at their lagging pole , which is thought to expand when hydrolysed and push a cell forward [4] . This effect is modelled using a propulsion term which causes cells to move preferentially in the average direction of the cell simulating the slime pushing a cell along . ( 17 ) ( 18 ) ( 19 ) where is a dimensionless propulsion coefficient , is the normalised average direction of the cell and is the update direction of segment of cell . Each segment moves towards where its head segment was previously , unless this causes segments to become unaligned . As well as extruding slime to move , cells can also detect slime trails left by other cells and preferentially follow them . This allows cells to follow other adventurous cells and leads to the formation of streams that can break away from the main colony . Slime following is complementary to A-motility . As each cell moves , it deposits a slime trail . Early evidence of the presence and effect of slime trail following is provided by the videos created by Reichenbach [31] . This effect of slime following is represented in our model as a set of normalised vectors representing the average direction of a cell . The slime ages over time and is eventually removed . Cells can sense slime trails within a limited neighbourhood around them . Using a weighted sum of the all slime trail directions based upon their age , the average slime direction is calculated and cells preferentially follow that . We use a weighted sum to account for the fact that a cell is more likely to follow a large slime trail than a small one . ( 20 ) ( 21 ) ( 22 ) ( 23 ) ( 24 ) where is a dimensionless slime following coefficient , is average direction of the slime trails in a neighbourhood and is the normalised direction vector of the slime trail at location . Curtis et al . [13] observe that cells appear to move in sheets towards each other and , upon impact during a collision , cells from one sheet can move up and on top of the other . This is consistent with O'Connor and Zusman [11] who suggest that cells appear to behave as independent sheets . This effect has been modelled so that it is somewhat analogous to a snow plow , which is pushed forwards into the snow pushing the snow up and away . In a similar way it is proposed that the oncoming force of a sheet of cells is sufficient to push oncoming cells up and direct them over and on top . Each cell monitors the number of head-on collisions it has , and the more the collisions the greater the chance of it being pushed up . Cells are not forced to always be pushed upwards , as this would be imposing an artificial constraint on the system , instead cells prefer regions of lower cell density where they are freer to move . Some cells will be pushed outwards away from the stream , but the majority will be pushed upwards since this is the only region of free space available . Curtis et al . [13] propose that when two sheets of oncoming cells encounter each other , individual cells have a proclivity to move out of the potential “traffic jam” that can ensue and typically this is upwards so one sheet of cells effectively moves over the other . A simulation of climbing cells which form layers can be seen in Figure 3 . The energy term we use , described below , encourages cells to move upwards , proportionally to the number of oncoming cells they interact with . ( 25 ) ( 26 ) ( 27 ) ( 28 ) ( 29 ) where is a dimensionless climbing coefficient , determines the number of oncoming cells , determines if two cells are moving in opposing directions by examining the dot-product between the normalised average direction ( ) of each pair of interacting cells , and compares the direction cell tries to move in ( ) with a normal vector ( typically a normal to the -plane ) . In a three-dimensional model , cell movement in the -axis needs to be controlled so that cells do not randomly climb into empty space and defy gravity . The other energy terms do not prevent cells from climbing so gravity is therefore introduced as an energy penalty for trying to climb; the steeper the climb the greater the penalty . An object acting under gravity requires the greatest amount of energy to directly oppose the force and move in the opposite direction ( upwards ) . It should be noted that the use of the dot-product means there is no net effect of this term for a cell moving horizontally in the -plane; since gravity is a constant , there is no change in energy from moving between two positions with a direction vector perpendicular to the direction of the gravitational force . ( 30 ) ( 31 ) where is a sensitivity parameter , is the normalised update direction of the head segment , a normalised direction vector pointing towards the ground , is a location below the centre of segment of cell and is a local neighbourhood surrounding . In order to capture natural elasticity and bending , each cell is modelled as a number of segments each with a finite volume . Segments must exert a repulsive force between themselves to prevent cells colliding . This force contributes to the Hamiltonian as follows . ( 32 ) ( 33 ) where is the position of segment of cell and is the minimum distance allowed between segments of difference cells . The collision energy compares the distance between a segment and the neighbouring segments around it and severely penalises a cell for getting too close to another . Although the centres of segments cannot occupy the same space , a small overlap is allowed to model deformation effects of cells in close proximity . This is required because of the rigid segment shape which would otherwise not allow for this type of effect . Extracellular polysaccharide ( EPS ) secreted by the cells during aggregation formation appears to play an important role in the formation of the physical structure of the fruiting body [11] , [17] . The exact role of the slime has yet to be elucidated due to the methods used to collect data and the very high cell densities within the fruit , making it difficult to resolve individual cells . Electron microscopy can resolve cells at higher resolutions [11] , [16] but this can only take a snapshot of a dynamic process and is unsuitable for tracking cells over a relatively long time period . In a dense region , cells generate a lot of EPS with a fruit being a large amalgamation of cells within an EPS matrix . The EPS is likely to exert a surface tension effect causing cells to stick together rather than drifting apart . This is separate from the slime trail following effect as it is non-directional , acting over the whole cell area . If two cells are close to each other and encased in slime , breaking them apart requires extra energy to counter the adhesive effects of the slime . In contrast to the climbing effect , here cells experience an energy penalty for breaking apart . It is a form of non-specific attraction and operates over short ranges since two cells several cell lengths apart will not affect each other; only cells in close proximity experience adhesion . The high density of cells in a swarm and fruiting body means there is a large amount of polysaccharide slime produced which encases all of the cells in a slime matrix [11] , [16] , [32] . The slime casing prevents cells coming apart , for example even with a rotary shaker . This matrix effects an adhesive force on the cells making it harder for cells to move apart from each other . Cells typically aggregate at a colony edge due to surface tension effects making it difficult to escape the colony [20] . This effect is different from the effects of A-motility and is a global property of a large mass of cells . ( 34 ) where is a dimensionless adhesion coefficient , determines the number of oncoming cells , determines if two cells are moving in opposing directions by examining the dot product between the normalised average direction ( ) of each pair of interacting cells , and compares the direction cell wants to move in ( ) with a normal vector ( typically a normal to the -plane ) . As a fruiting body matures , 65–90% of cells lyse , with the remaining cells going on to form myxospores [33] , [34] . Spores appear to migrate to the centre of the fruiting body with motile cells remaining on the outside and the periphery [11] . The fruiting model was extended to incorporate sporulation and its effects on fruit formation . Each cell is given a type: motile or spore . Motile cells accumulate C-signal from collisions with other motile cells . Once C-signal exceeds a threshold ( ) , cells convert to non-motile spores . Spores can be moved by motile cells pushing them . Each cell type has its own Hamiltonian governing its behaviour . Normal cells continue to use the Hamiltonian defined in Equation 3: ( 35 ) However spores are non-motile cells with a fixed size and shape , and the Hamiltonian controlling them loses terms associated with autonomous cell motion and is therefore simpler: ( 36 ) Although spore cells are immobile , other motile cells can move them during collisions when they collide and through adhesive effects between cells . ( 37 ) ( 38 ) ( 39 ) where is a dimensionless coefficient , is the normalised average direction of the cell , is the average direction of all the cells in a local neighbourhood surrounding cell . The term reflects the tendency of cells to turn through an acute angle to align with other cells in either direction . The parameters used in the simulation are listed in Table 1; the same parameters were used in our previous model of rippling behaviour [35] . Some of these parameters reflect the level of abstraction that approximate the level of behaviour observed through video microscopy . The 7∶1 length to width ratio reflects evidence from [36] . The volume of each cell was set so that thousands could be fit into a volume large enough to hold a fruiting body without requiring unreasonable amounts of computational memory . Representing cells via eight segments seemed to provide a reasonable approximation of the degree of flexibility observed in various phases of the lifecycle . Motility parameters were based initially on experimental evidence [37] , [38] to get an idea of the speed of cells , and then tuned so that cells moved at the correct speed given their size and volume in the simulation environment . Likewise , parameters governing the flexibility of cells were based initially on [39]; other parameters were tuned relative to these to emulate the cell motion patterns observed in nature .
Simulations were carried out in a three-dimensional environment using a model based on our previous stochastic model of myxobacteria motility [35] . M . xanthus is approximately 5–7 long and 0 . 5 in diameter so the model cells were given a length to width ratio of 7∶1 . Each cell was composed of eight three-dimensional segments ( see Figure 2 ) with each segment being composed of 27 segment nodes arranged in a cube formation . Segments were allowed to overlap so that cells maintained a continuous volume and the correct aspect ratio despite being made of multiple separate segments ( see Figure 2 b ) . The physical behaviour of cells was described using a Hamiltonian function whilst the internal state was described using ordinary differential equations ( ODEs ) . The EPS surrounding cells is rarely considered in models; however , in our model we found that slime can have an essential role in fruit formation . The Hamiltonian includes an adhesion term , which generates energy proportional to the inverse square of the distance between any two cells in a neighbourhood . It is more energetically favourable for cells to remain close to other cells otherwise there is a severe penalty for moving apart that increases exponentially with distance . An inverse relationship was chosen so that long range interactions are weak; cells towards the perimeter of the local neighbourhood should not exert the same influence as cells in close proximity . Adhesion acts to control the viscosity of the slime determining how easy it is for cells to move through it . The amount of slime and thickness varies depending on the stage of fruiting and the cell density [16] . We note that Curtis et al . reported that a pilA mutant produces less EPS and this inhibits fruiting body formation [13] . It is not known biologically whether the effect of the pilA mutation is a consequence of reduced EPS production , or due to altered motility properties . Therefore , a direct comparison with the pilA mutant described by Curtis et al . is not possible . Figure S1 shows the effect of varying the strength of adhesion on a stream of cells . When there is no adhesion , cells at the front of the stream are able to move adventurously , causing the stream to break down into a number of smaller streams which diverge . As the adhesion strength ( ) is increased , cells remain much closer . When cells tend to stay as one or possibly two large coherent streams . When , the slime is so viscous that cells are no longer able to move . Fruiting begins with streaming and the confluence of streams to form aggregation centres . The fruiting model presented here allows cells to spontaneously form streams and aggregation centres ( see Figure 4 ) . A simulation consisting of 300 cells was run three times to determine the efficacy of streaming and aggregation ( model parameters are given in Table 1 ) . Cells were initially randomly distributed . After approximately 100 min of simulated real time , cells formed into streams regardless of their initial configuration . Cells aligned and formed small streams which joined other streams when they came into contact . After 300 min cells typically formed an aggregate , which expanded as the the majority of cells joined it . The effects of motility along with cell adhesion causes model cells to form streams . As the streams approach an aggregation centre , cells will attempt to avoid collision and alter course . They begin to move around the aggregate causing the stream to change direction and form the characteristic spiral patterns observed by O'Connor and Zusman [11] . In a model with a finite number of cells , it is difficult to achieve a high enough cell density to maintain aggregates . There is an upper bound on the density of cells in a mono-layer above which cells will not have enough space to move and be able to engage in activity . With a high cell density which still allows cells to move , it is possible to get aggregation , but once the fruit starts to form , the number of cells moving into the aggregate will not be sustainable and the fruit will simply dissociate . This type of model is also unrealistic because in reality , an aggregate would be surrounded by other cells and not sit in isolation as more cells join it . Although it would be ideal to model a vast mono-layer of cells to ensure there were a sufficient number of cells to a form a fruit , computational limitations ( typically memory ) restrict the size of a simulation . Figure S4 shows the output of a fruiting body simulation using a finite number of cells . 1600 cells were arranged into two opposing sets of streams with one set perpendicular to the other . The streams move into each other and collide . In the aggregation centre , some cells push upwards and move over others forming new layers and the base of a stalk . The effect of using a finite number of cells becomes apparent after 300 time steps when the stalk begins to disassociate . The cells organise themselves into a stack four layers thick , but since there are no more cells to expand the base layer , the upper cells begin to climb down and move away from the fruit . Once a few cells move away , a mass exodus is triggered causing all of the cells to move away . The formation and subsequent rapid dispersion of fruits will occur at any point where an aggregate forms . This effect will be more apparent on subsequent aggregation formations since the number of cells within the mass is unlikely to be as high as in the initial formation so the deterioration will be more pronounced . Curtis et al . [13] observed that during the initial stages of fruiting , small fruiting bodies would sometimes repeatedly start to form and then dissipate before a stable fruiting body finally formed ( see Figure 1 in [13] ) . The formation of transitory aggregates can be explained by adjusting the cell influx rate . The simulations maintained the same initial conditions as the previous fruiting simulation , except the rate of influx was altered . Figure S2 shows a snapshot of a simulation where the influx rate was reduced by 90% . Although a fruiting body begins to form it rapidly dissociates over time . Cells accumulate and the stack expands outwards from the centre for approximately 200 min after which the fruit collapses and the cells begin to disperse . The cell density remains too low for cells to attempt a new fruit formation suggesting that influx could be a primary driving factor behind development . The base influx rate was selected to ensure a constantly high cell density to enable fruit formation . Lower influx rates promote transitory fruiting body formation and dissociation . Figure 5 shows three-dimensional snapshots of fruiting development when the influx rate was reduced to 25% of the base value . After 500 min , three small mounds have formed; however , they dissociate and new mounds form . This agrees with experimental evidence showing transitory aggregates [13] . If the simulation volume were enlarged by several orders of magnitude ( which has not been computationally feasible ) , we predict that as fruiting bodies disperse , a cohesive layer of cells would form and drift off . This would meet other disparate layers from other dispersed fruits and further fruiting development would be initiated where they collide . The process would repeat leading to multiple transitory fruit formations [13] . The prerequisite for this to occur is a sufficiently high cell influx that allows a fruit to form but at a sub-optimal rate such that development cannot be sustained . The fruiting body must be sufficiently large so that , when cells leave it , they form a layer of equal density to the initial layers so that fruiting can occur spontaneously at other locations . The influx rate appears to be the rate limiting step in controlling fruiting growth; there is a point where the number of cells forming new layers will begin to exceed the number of cells flowing into the system so the development of new layers is arrested . The influx rate we use here refers specifically to the addition of new cells into the simulation volume . The effect of EPS adhesion also affects influx rate into a local area , since it governs cell alignment and density . The pilA mutant described by Curtis et al [13] can be approximated through the EPS model described here . Figure 6 shows how mound formation in the fruiting simulation varies over time . The motile cell count rises sharply during the first 16 h of simulated real time as cells accumulate within the fruit and surrounding area . After this time point , a combination of the cell density and the aggregate size makes it more difficult for new cells to enter the aggregate . The high cell density ensures constant C-signal ling triggering a constant rise in myxospores , which occupy an increasing fraction of the aggregate . Fruiting development begins after 10 h with a small mound formation . This rapidly expands and develops into a fruiting body after 24 h around which motile cells orbit in stream formations . Towards the periphery of the fruit , the cell density rapidly decreases leaving only a thin layer of cells ( less than three cells deep ) in the regions not occupied by the fruit . C-signal is not evenly distributed throughout the colony . Cells within the fruit collide much more frequently with other cells so they accumulate C-signal faster ( see Figure 7 a ) and sporulate faster ( see Figure 7 b ) . The majority of C-signal ling occurs within the fruit hence spores are formed within or close to the fruit centre and are pushed into the centre by the motile cells . The large hemispherical aggregate agrees with the formations observed by Kuner and Kaiser [17] . The aggregate is stable due to the spores that occupy the centre of the mound ( see Figure 7 a ) . The spores limit the movement of the motile cells causing them to stall more frequently in the aggregate and expanding the size of the traffic jams . The motile cells push the non-motile cells towards the centre of the aggregate in agreement with existing data [11] . Cells are highly crowded and aligned , forming streams and sheets . This is in agreement with the observations of O'Connor and Zusman ( see Fig . 6A in [11] ) . Video S1 is a video of simulation output showing the formation of the fruiting body .
A precise understanding of how and why myxobacteria cells form fruiting bodies remains elusive; however , we can start to address the issue of fruiting body development from a theoretical perspective and provide a possible explanation of how they form . The models presented here indicate that fruit formation can be simply a natural consequence of cell behaviour without any form of centralised coordination . The lack of reversals makes cells prone to collisions [10] . Aggregation centres tend to form quickly as small streams of cells frequently collide and block each other's path . Without the ability to reverse , cells are forced to remain stuck in their current position . Any cells that join the tail of the trail become stuck as well leading to a traffic jam and the formation of an aggregate . It should be noted that these small streams are typically too small to trigger cells to instigate climbing since although the cells are blocked , the density of neighbouring cells is not generally sufficient to support a new layer of cells on top of it . A primary goal of the modelling work presented here is to show that a physical and biochemical model can explain multiple phases of behaviour . While it is beyond the scope of this work to fully address all factors controlling the myxobacteria life-cycle , we previously showed that a similar Monte Carlo model of cell physics can explain rippling and the behaviour of cells in the early stages of starvation [35] . We also modelled a C-signal ling mutant by switching off the C-signal ling component in the Hamiltonian and observed vegetative behaviour in agreement with Curtis et al . [13] . By adjusting the C-signal ling levels of the model presented in this article , cells can be shown to revert to responding to C-signal with a higher incidence of reversal . Cell climbing was an important factor in governing how well aggregates formed in our model . Cells were allowed to climb at any angle , but it was more favourable for them to climb at steeper inclines . Climbing simulates the effect of oncoming cells pushing up and under cells causing them to rise upwards to a new layer . It was found that cells need to climb at a fairly steep angle ( though only for relatively short distances ) with a typical incline angle being rad since a cell is being pushed upwards by another cell so the height it rises must be sufficient to allow the opposing cell to move into its space . Sozinova et al . [18] used a three-dimensional lattice gas cellular automata model to study rippling formation . Cells were oriented in one of six directions on a hexagonal grid , which introduces spatial inaccuracy . This limits the direction cells can move in and any orbiting patterns of cells may be an artefact of this; any alteration in direction is a turn of rad so cells can move through tight arcs . The rigid cell body also means that the cell must alter its course dramatically . In reality , the partial flexibility of the cell means it does not have to completely alter its course to avoid an obstacle; it can bend slightly to align itself alongside the object and move around it . O'Connor and Zusman [11] showed that cells cluster in small aligned patches within a fruit and move together . A hexagonal lattice model does not allow for this; cells maintain alignment only if they never change direction , otherwise they alter course by rad and spatial alignment is lost almost immediately . The models presented here show that it is possible for fruiting bodies to develop without artificial induction . Cell density and an upward pushing force seem to be sufficient to instigate formation . Importantly , the EPS surrounding cells must exert an adhesive force , binding cells together . Without this force , cells are too unconstrained and move away from the aggregate . Each layer acts almost independently . Cells from one layer have a much reduced effect on cells in another layer than cells in the same layer . Experiments where all terms in the Hamiltonian were dependent on a local three-dimensional neighbourhood showed that cells cannot move freely due to feeling the effects of cells moving in all directions around them . The fruiting models offer an explanation of the initial formation of fruiting bodies as a consequence of cell physics and a low reversal frequency , suggesting that fruiting bodies form can form spontaneously without the need for an artificial aggregation centre to start the process . The fruiting model has also shown that observed transitory fruiting body developments before a stable fruiting body forms can be explained as a consequence of net cell influx . Sporulation appears to be important for stable fruiting body formation . A large aggregate of non-motile cells provides a base around which the motile cells can move to form the fruiting body . Motile cells are still driven upwards at the base of the aggregate where streams collide and they force the spores to move upwards as well . This offers a potential mechanism for allowing myxobacterial cells to form sporangiole on stalks without extensive behaviour modifications . Although the model incorporates sporulation , it is still not clear how cells choose to sporulate since only a percentage of the fruiting body do so . The fruiting model approximates this behaviour by only allowing a percentage of the cells to sporulate . Future experimental work will hopefully provide more information on the sporulating process which can be incorporated into the models . The models presented here offer potential mechanisms M . xanthus could use to organise streaming , aggregation and fruiting body formation . Importantly , by deriving the fruiting models from an existing model of rippling , we have shown that a single model based on the observable , physical characteristics of myxobacteria can explain multiple spatial phenotypes and may shed more light on how myxobacteria is able to exhibit multiple different behaviours during its life-cycle .
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Understanding how relatively simple , single cell bacteria can communicate and coordinate their actions is important for explaining how complex multicellular behaviour can emerge without a central controller . Myxobacteria are particularly interesting in this respect because cells undergo multiple phases of coordinated behaviour during their life-cycle . One of the most fascinating and complex phases is the formation of fruiting bodies—large multicellular aggregates of cells formed in response to starvation . In this article we use evidence from the latest experimental data to construct a computational model explaining how cells can form fruiting bodies . Both in our model and in nature , cells move together in dense swarms , which collide to form aggregation centres . In particular , we show that it is possible for aggregates to form spontaneously where previous models require artificially induced aggregates to start the fruiting process .
|
[
"Abstract",
"Introduction",
"Model",
"Results",
"Discussion"
] |
[
"computational",
"biology/synthetic",
"biology",
"computer",
"science/applications",
"cell",
"biology/cell",
"signaling",
"biophysics/theory",
"and",
"simulation",
"cell",
"biology/microbial",
"growth",
"and",
"development",
"microbiology/microbial",
"growth",
"and",
"development",
"computational",
"biology/systems",
"biology"
] |
2010
|
Spatial Simulations of Myxobacterial Development
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Patients affected by chronic inflammatory disorders display high amounts of soluble CD95L . This homotrimeric ligand arises from the cleavage by metalloproteases of its membrane-bound counterpart , a strong apoptotic inducer . In contrast , the naturally processed CD95L is viewed as an apoptotic antagonist competing with its membrane counterpart for binding to CD95 . Recent reports pinpointed that activation of CD95 may attract myeloid and tumoral cells , which display resistance to the CD95-mediated apoptotic signal . However , all these studies were performed using chimeric CD95Ls ( oligomerized forms ) , which behave as the membrane-bound ligand and not as the naturally processed CD95L . Herein , we examine the biological effects of the metalloprotease-cleaved CD95L on CD95-sensitive activated T-lymphocytes . We demonstrate that cleaved CD95L ( cl-CD95L ) , found increased in sera of systemic lupus erythematosus ( SLE ) patients as compared to that of healthy individuals , promotes the formation of migrating pseudopods at the leading edge of which the death receptor CD95 is capped ( confocal microscopy ) . Using different migration assays ( wound healing/Boyden Chamber/endothelial transmigration ) , we uncover that cl-CD95L promotes cell migration through a c-yes/Ca2+/PI3K-driven signaling pathway , which relies on the formation of a CD95-containing complex designated the MISC for Motility-Inducing Signaling Complex . These findings revisit the role of the metalloprotease-cleaved CD95L and emphasize that the increase in cl-CD95L observed in patients affected by chronic inflammatory disorders may fuel the local or systemic tissue damage by promoting tissue-filtration of immune cells .
Screening for death inducers led to the discovery of the receptor CD95 , which was initially cloned and described as a death receptor belonging to the TNF-R family [1] , [2] . Since CD95 is devoid of enzymatic activity , both aggregation [3] and conformational modification [4] of the receptor are crucial to recruit the adaptor protein FADD , which in turn aggregates initiator caspases-8 and -10 . On binding of CD95L to CD95 , the death receptor clusterized and polarized in a structure designated the CD95-CAP [5] , [6] . The local concentration of caspases is followed by their cleavage and the release in the cytoplasm of active caspases culminating in the induction of an apoptotic signal [7] . We and others showed that depending on the extent of CD95 mobilization , it is possible to convert CD95 into a non-apoptotic signaling receptor [8]–[10] . Expression of the CD95 ligand , CD95L , is tightly controlled and is found at the plasma membrane of activated T-lymphocytes and NK cells where it participates in immune surveillance and in peripheral tolerance [11] . As a consequence , failure of the CD95/CD95L signalling leads to auto-immunity [12] and increased risk of cancer [13] , [14] . Recent evidence pinpointed that CD95L behaves as a chemoattractant molecule for neutrophils and macrophages [15]–[17] and for malignant cells in which the CD95-mediated apoptotic signal is non-productive [18] , [19] . Nonetheless , the biological role of CD95L has to be clarified due to the fact that physiologically , the ligand is present under two main forms with different stoichiometries . Whereas the membrane-bound CD95L induces a potent apoptotic signal , the physiological and pathological role of the metalloprotease-cleaved CD95L remains poorly defined . Indeed , most studies on CD95L are focused on membrane-bound or engineered CD95L , which multimerizes and thereby efficiently triggers cell death . Initial studies focusing on the apoptotic signal induced by CD95 showed that the homotrimeric CD95L behaves as a competitive inhibitor of its membrane-bound counterpart [20] , [21] . More recently , the antagonist role of cleaved CD95L ( cl-CD95L ) has been challenged by Strasser's group [22] . Indeed , they found that ectopic expression of the soluble CD95L in a CD95L-deficient mouse strain dramatically aggravated the lupus-like disease and the occurrence of histiocytic sarcoma compared to micegld/gld expressing a mutated CD95L incapable of CD95 binding . As a consequence , we may envision that soluble CD95L promotes autoimmunity and tumorigenesis through unknown non-apoptotic activities [22] . Cell migration is a multifactorial and multi-step process involving the formation of lamellipodia/pseudopodia , cell body contraction , and tail retraction [23] . For instance , production of phosphatidylinositol- ( 3 , 4 , 5 ) -trisphosphate ( PIP3 ) , formation of actin mesh , and localized Ca2+ rise are common features of migrating cells [23] , [24] . Class I PI3K phosphorylates plasma membrane phospholipids and generates the second messenger PIP3 , which serves as docking sites for various signaling factors . The cytosolic serine-threonine kinase Akt binds PIP3 via its pleckstrin homology domain ( PH domain ) and thus redistributes to the plasma membrane . Once at the membrane , Akt is activated through phosphorylation on two sites by the PI3K-dependent kinase-1 ( PDK1 ) on Thr308 and by mTOR ( mammalian target of rapamycin ) complex-2 on Ser473 . Chronic inflammatory disorders such as systemic lupus erythematosus ( SLE ) achieve massive infiltration of organs with cells from innate ( neutrophils/macrophages ) and adaptive ( activated T-lymphocytes ) immune systems [25] . The initial steps involved in the attraction of these cells remains to be deciphered . Herein , we ascertain that the naturally processed CD95L is increased in SLE patients and this cytokine does not induce the “orthodox” apoptotic signal but rather ignites a c-yes/Ca2+/PI3K signaling pathway , which promotes cell migration .
Physiologically , the plasma membrane homotrimeric ligand is released after cleavage of its ectodomain by metalloproteases between either serine 126 and leucine 127 [20] or lysine 129 and glutamine 130 [26] . Since a recent study described that the soluble form may contribute to the aggravation of a lupus-like disease in mice [22] , we explored the effect of cl-CD95L on activated lymphocytes . To produce cleaved CD95L ( cl-CD95L ) , we transfected the epithelial kidney cells 293 with wild type CD95L-encoding cDNA . These cells secrete exosomes , which contain full-length CD95L , and thus contaminate supernatants . Therefore , to eliminate this potential contaminant , supernatants from wild type CD95L-transfected 293 cells underwent an ultracentrifugation step to pellet the secreted vesicles [20] . As expected , CD95L-transfected human epithelial kidney 293 cell line produced full-length plasma membrane-bound CD95L ( ≈40 kDa ) ( Figure S1A ) and pelleted exosomes also contained full-length CD95L ( Figure S1A ) . Cleaved-CD95L was found in exosome-free supernatant at a lower molecular weight ( ≈30 kDa ) than the full-length ligand ( Figure S1A ) . In contrast , supernatants harvested from 293 cells expressing a CD95L in which serine 126 and leucine 127 were replaced by glutamic acid ( CD95LS126E/L127E ) [20] , the putative cleavage site of metalloprotease , exhibited a dramatic decrease in their amounts of soluble CD95L , confirming that CD95L was cleaved between these amino acids ( Figure S1A ) . The faint amount of soluble CD95L found in CD95LS126E/L127E-transfected 293 cells may be due to the presence of another cleavage site ( i . e . , Lys129/Gln130 ) since a shift of this cleavage site has also been described [26] . Next , we confirmed the homotrimeric stoichiometry of the cleaved CD95L using gel filtration ( Figure S1B ) . In the purpose of generating a multi-aggregated and highly cytotoxic ligand , several CD95L-containing constructs have been engineered . As already reported , we have generated a dodecameric CD95L ( gel filtration analysis , unpublished data ) through the fusion of the extracellular region ( a . a . 105 to 281 ) of CD95L with a dimerization domain ( Ig ) derived from the LIF receptor gp190 [27] . Although the Ig-CD95L triggered induction of the initiator caspase-8 and cell death ( Figure S1C and S1D ) , cl-CD95L failed to initiate either cleavage of caspase-8 ( Figure S1C ) or apoptosis ( Figure S1D ) . Overall , these findings support the conclusion that in contrast to the multi-aggregated forms of CD95L , which mimic the membrane-bound ligand , homotrimeric cl-CD95L does not achieve induction of the “orthodox” CD95-mediated apoptotic signal . It has been described that soluble CD95L acts as an inert ligand , which once bound to CD95 competes with the membrane-bound CD95L and prevents the transmission of the apoptotic signal [20] , [21] . Consistent with this statement , cells incubated with cl-CD95L did not undergo caspase-8 activation or cell death ( Figure S1C and S1D ) . Nevertheless , cl-CD95L was able to induce proximal events of the CD95 signal up to the formation of CD95-CAP ( Figure 1A ) . Strikingly , once incubated with 100 ng/ml ( 3 . 3 nM ) of cl-CD95L , classical round-shaped activated T-lymphocytes underwent a dramatic alteration of their morphology and emitted pseudopods ( red arrows , Figure 1A ) . Formation of CD95-CAP and pseudopods in the presence of cl-CD95L was also observed with the leukemic T-cell lines H9 and Jurkat ( Figure S2A–C ) . In contrast , incubation of activated lymphocytes with the dodecameric Ig-CD95L triggered cell blebbing and shrinkage , hallmarks of cells dying through an apoptotic process ( Figure 1B ) . Alteration of cell morphology and formation of CD95-CAP was not restricted to hematological cells since the fibroblastic PS120CD95 cell line , derived from PS120 , a CD95-deficient cell that has been reconstituted with wild type human CD95 ( Figure S3A ) , also underwent alterations of its morphology in the presence of cl-CD95L ( Figure 1C ) . Indeed flat , cubical epithelial shape was converted to bipolar , well-oriented , slanted , fibroblastic appearance following incubation with 100 ng/ml of cl-CD95L ( Figure 1C ) . Similarly to lymphocytes , clustering of CD95 was observed at the extremity of the emitted pseudopods in the fibroblastic cell line ( Figure 1C ) . The initial response of a cell to a migration-promoting agent is to emit protrusions in the direction of migration . These protrusions can be large , broad lamellipodia or more slender and filiform structures such as pseudopods and are usually driven by actin polymerization [23] . Several studies established that an intracellular calcium rise and an activation of PI3K play a crucial role to achieve cell motility [28]–[31] . These signals contribute to the remodeling of actin cytoskeleton , which in association with myosin motors elicits the mechanical process of cell movement . We then investigated whether although unable to trigger caspase activation , cl-CD95L induced remodeling of actin , Ca2+ response , and activation of the PI3K signaling pathway . Using the non-competing molecule Lifeact , a small peptide , which has recently been described to bind polymerized actin and to not interfere with actin dynamics [32] , we clearly observed that the pseudopods emitted by leukemic T-cells concentrated a dense meshwork of polymerized actin ( Figure 2A ) . The serine-threonine kinase Akt is a downstream effector of the PI3K signal and its phosphorylation on its serine473 is a key feature of its activation . As shown in Figure 2B , T-cell lines incubated with cl-CD95L underwent a potent Akt phosphorylation . To confirm that cl-CD95L behaved as an inducer of the PI3K/Akt signal , we analyzed the impact of the naturally processed ligand on the production of the plasma membrane PIP3 . For this purpose , we expressed in T-cells a chimeric protein encompassing GFP fused to the pleckstrin homology domain of Akt ( PHAkt-GFP ) , which possesses a strong affinity to the negatively charged lipid . Addition of cl-CD95L on H9 T-cells promoted production of PIP3 since the cytoplasmic localization of PHAkt-GFP in resting cells changed for a plasma membrane reorganization ( Figure 2C ) . Overall these findings highlighted that the naturally processed CD95L activates the PI3K/Akt signaling pathway . On the grounds that spatiotemporal distribution of “calcium microdomains” orchestrates directional movement during cell migration [33] , we further investigated the effects of cl-CD95L on the intracellular distribution of Ca2+ using video-microscopy and we established that T-lymphocytes incubated with cl-CD95L revealed significantly higher Ca2+ concentration at the leading edge of the pseudopod ( Figure S4 ) , beneath the CD95-CAP ( Figure 2D ) . Activation of a wide variety of plasma membrane receptors elicits a rapid biphasic Ca2+ response through the induction of phospholipase C ( PLC ) that generates inositol 1 , 4 , 5-trisphosphate ( IP3 ) , which in turn activates the IP3-receptors present in the membrane of the endoplasmic reticulum ( ER ) . IP3-R activation drives the release of the ER-stored Ca2+ [34] . The second phase of the Ca2+ signal is mainly achieved by activation of the store-operated Ca2+ ( SOC ) channels in the plasma membrane that induce a Ca2+ influx [35] . In order to address the cl-CD95L-mediated molecular mechanism evoking the Ca2+ response , we first investigated whether PLCγ1 participated in this signal . Phosphorylation of PLCγ1 on its tyrosine 783 indicated that the lipase was activated in the presence of cl-CD95L ( Figure S5A ) . To prove that PLCγ1 participated in the cl-CD95L-mediated Ca2+ rise , we analyzed the cl-CD95L-mediated Ca2+ signal in PLCγ1-deficient T-cells ( Figure S5B ) . The Ca2+ signal was abrogated in the PLCγ1-deficient T-cells , while it was restored in the PLCγ1-reconstituted counterpart ( Figure S5B ) . Second , T-cells pre-incubated with the IP3-R inhibitors , 2-APB ( Figure S5C and S5D ) and xestospongin C ( unpublished data ) , failed to mobilize Ca2+ in the presence of cl-CD95L , indicating that the homotrimeric ligand elicited the Ca2+ signal through activation of PLCγ1 and the subsequent induction of the IP3-receptors . In nonexcitable cells , SOC influx is the major Ca2+ entry mechanism [35] , [36] . Recently it has been ascertained that two genes , STIM1 ( stromal interaction molecule 1 ) and Orai1 , participate in the SOC entry [37]–[41] . While STIM1 behaves as an ER-localized Ca2+ sensor , Orai1 is a pore-forming component of the SOC channel [42] , [43] . To finely identify the molecular mechanism that drives the Ca2+ rise observed in the presence of cl-CD95L , we first explored whether the extracellular Ca2+ contributed to the cl-CD95L-mediated Ca2+ signal . In T-cell lines bathed in a Ca2+-free medium , the cl-CD95L-mediated Ca2+ signal was significantly reduced as compared to regular medium ( Figure S6A and S6B ) . These findings suggested that Ca2+ influx participated in the cl-CD95L-induced Ca2+ response . To investigate the role played by the SOC channels on the cl-CD95L-mediated Ca2+ response , we analyzed the effect of the pharmacological inhibitor of SOC channels termed BTP2 [44] on this Ca2+ signal . Pre-incubation of peripheral blood lymphocytes ( PBLs ) with BTP2 reduced the magnitude of the Ca2+ peak and abrogated the Ca2+ plateau observed in the presence of the metalloprotease-processed cl-CD95L ( Figure S6C ) , suggesting that the SOC channels contributed to Ca2+ entry upon the addition of cl-CD95L . Next , the role of Orai1 in the cl-CD95L-driven Ca2+ entry was studied . Confocal microscopy revealed that addition of cl-CD95L led to the compartmentalization of Orai1 into the CD95-CAP ( Figure 2E ) . Substitution of the conserved glutamate in position 106 to an alanine ( Orai1E106A ) results in a non-conducting Orai1 channel acting as a dominant negative construct that abrogates the native SOC current [40] , [42] , [43] . Consequently , we generated leukemic T-cell clones stably expressing the Orai1 mutant or over-expressing the wild type Orai1 and we assessed the Ca2+ response induced by cl-CD95L . Compared to GFP-expressing control cells , the ectopic expression of Orai1E106A dramatically reduced the amplitude and the duration of the Ca2+ response ( Figure 2F ) . On the other hand , over-expression of Orai1 resulted in a significant increase in the intensity and the duration of the CD95-mediated Ca2+ response ( Figure 2F ) . Overall , these findings established that in the presence of cl-CD95L , T-cells elicit a Ca2+ response through a PLCγ1/IP3R/Orai1 signaling pathway at the leading edge of the emitted pseudopod in a structure that may contribute to cell migration . Next , we addressed the impact of the metalloprotease-processed CD95L on cell migration by using Boyden chamber and wound healing assays . For this purpose , we reconstituted the CD95-deficient PS120 cell line with either wild type CD95 ( PS120CD95 ) or death-domain truncated counterpart ( PS120CD95 ( Δ1-210 ) ) ( Figure S3A ) . In contrast to the parental PS120 or PS120CD95 ( Δ1-210 ) , PS120CD95 exhibited a sensitivity towards the multimeric Ig-CD95L ( Figure S3B ) , which indicated that the intracellular CD95 machinery remained functional in these cells . As expected , cl-CD95L did not trigger cell death in the different PS120 cell lines ( Figure S3B ) . Strikingly , we found that in contrast to PS120 and PS120CD95 ( Δ1-210 ) cells , migration of PS120CD95 cells was stimulated in the presence of cl-CD95L ( Figure 3A and 3B ) , indicating that a functional death domain was required to mediate the cl-CD95L effects on motility . To confirm the role of cl-CD95L on cell motility , wound-healing assays were carried out . Fibroblasts were grown to confluency , and a “wound” was realized in the cell monolayer . While in the presence of cl-CD95L cells devoid of CD95 or expressing a DD-deficient CD95 failed to fill the gap after 24 h , the CD95-reconstituted PS120 completely healed the “wound” ( Figure 3C ) . In addition , incubation with cl-CD95L did not modify the cell proliferation rate ( unpublished data ) . Overall , these findings led to the conclusion that cl-CD95L induces a DD-dependent signal , which promotes migration of the CD95-expressing cells . As the death domain of CD95 was crucial to mediate the motile signal , we next analyzed whether this intracellular region was also mandatory to induce Akt phosphorylation upon addition of cl-CD95L . We did not observe any Akt phosphorylation in PS120control and PS120CD95 ( Δ1-210 ) cells ( Figure 4A ) . Conversely , expression of wild type CD95 restored the transmission of the PI3K/Akt signal upon cl-CD95L addition ( Figure 4A ) . Since interconnections have been reported between Ca2+ and PI3K/Akt signaling pathways , we next investigated whether the cl-CD95L-mediated Ca2+ response contributed to the magnitude of the Akt activation level . To prevent the cl-CD95L-mediated calcium response , cells were pre-treated with the calcium chelator BAPTA-AM and 2-APB , an inhibitor of inositol 1 , 4 , 5-trisphosphate receptor ( IP3R ) and store-operated calcium ( SOC ) channels . Inhibition of the cl-CD95L-induced Ca2+ response using BAPTA-AM or 2-APB decreased by 60% and 70% the amount of Akt phosphorylation ( Figure 4B ) , respectively , indicating that the Ca2+ rise observed upon addition of cl-CD95L enhanced the activation level of Akt . Next , the role played by Ca2+/PI3K signals in the CD95L-mediated cell motility was investigated . Non-cytotoxic and non-cytostatic amounts of the PI3K inhibitors LY294002 , a quercetin analogue [45] , or Wortmannin , a fungal metabolite , dramatically reduced the cl-CD95-mediated cell motility ( Figure 4C and 4D ) . Likewise , BAPTA-AM and 2-APB , which both prevented the cl-CD95L-mediated Ca2+ response ( Figure S5C and S5D , S7A and S7B ) , significantly impeded cell migration ( Figure 4C and 4D ) . Indeed , we found that inhibition of PI3K activity by LY294002 and Wortmannin reduced cell motility by 75% and 83% , respectively . On the other hand , down-modulation of the CD95-mediated Ca2+ response by either BAPTA-AM or 2-APB achieved 47% and 71% of cell migration blockade , respectively . We next examined the effects of small-molecule inhibitors of class I PI3Ks to explore the contribution of the different isoforms in the cl-CD95L-mediated cell migration . While the catalytic subunits p110α and p110β are ubiquitously expressed , p110δ and p110γ are found predominately in hematological cells [46] . By screening for p110 isoform-selective PI3K inhibitors ( Figure S8A ) that could prevent the cl-CD95L-mediated cell migration , we observed that whereas p110-γ was instrumental in both the Akt activation and the transmigration of H9 T-cells across a barrier of endothelial cells ( Figure S8B and S8D ) , the α and β isoforms orchestrated the motility process in the non-hematological cell line PS120CD95 ( Figure S8C and S8E ) . These findings definitively proved that the cl-CD95L-mediated motility signal occurred through the class I PI3K activation and that the implicated isoform varied between hematological and non-hematological cells . Finally , we wondered whether Orai1 contributed to the cl-CD95L-mediated cell migration . To address this question , HEK cells were transiently transfected with either scrambled or Orai1-targeting shRNA ( Figure 4E ) . Orai1 knock-down in HEK cells reduced the SOC current by 85% [47] . Strikingly , the silencing of Orai1 abrogated the cl-CD95L-mediated cell migration ( Figure 4F ) , strongly supporting the conclusion that the Orai1-driven Ca2+ entry contributed to the motility signal induced in the presence of cl-CD95L . Altogether , these findings underlined that the metalloprotease-cleaved CD95L does not behave as an inert ligand but rather stimulates cell migration through an Orai1/Ca2+/PI3K-dependent mechanism . Induction of the CD95-mediated PI3K/Akt response remains poorly defined . However , it is established that activation of PI3K can be reached through activation of src kinases [48] , which are found enriched into sub-domains of the plasma membrane designated lipid rafts [49] . In this regard , src kinases are anchored to lipid rafts through the double acylation ( i . e . , palmitoylation and myristoylation ) of their amino-terminal sequence [50] . Lipid rafts were tagged using the amino-terminal domain of the src kinase Lck fused to the fluorescent protein GFP [51] , and we examined whether cl-CD95L enabled the compartmentalization of CD95 into lipid rafts , meaning in close vicinity of the src kinases . While T-cells displayed a homogeneous distribution of the Lck-GFP probe at the plasma membrane , addition of cl-CD95L partitioned CD95 into lipid rafts at the leading edge of the pseudopod ( Figure 5A ) . To follow lipid rafts in activated PBLs , we incubated cells with a fluorescent-conjugated cholera toxin B sub-unit that exhibits strong affinity for the monosialoganglioside GM1 , a lipid enriched into lipid rafts [52] . We confirmed that cl-CD95L achieved the partition of CD95 into lipid rafts at the leading edge of the emitted pseudopod in activated PBLs ( Figure 5A ) . In addition , inhibition of Src kinase activity using the pharmacologic inhibitor PP2 impeded the phosphorylation of Akt , supporting the notion that src kinases were involved in the transduction of the CD95-mediated PI3K/Akt signal ( Figure 5B ) . C-yes belongs to the src family and its activity promotes activation of PI3K in glioblastoma cells [19] . Strikingly , in activated PBLs and in the T-cell line H9 , we observed that in contrast to the “canonical DISC” formed in the presence of the agonistic antibody APO1-3 , addition of cl-CD95L failed to induce the binding of FADD , caspases-8 ( Figure 5C ) , and -10 ( unpublished data ) to CD95 but promoted the recruitment of the tyrosine kinase c-yes ( Figure 5C ) . In contrast , another src kinase termed syk , which is involved in the BCR ( B-cell receptor ) -mediated PI3K activation [48] , was not detected in the CD95-containing complex ( unpublished data ) . These findings indicated that binding of cl-CD95L to CD95 reached the formation of a molecular complex devoid of the initiator caspases-8 and -10 . In agreement with these latter observations , inhibition of the caspase activity using the broad-spectrum caspase inhibitor zVAD-fmk did not alter Akt phosphorylation ( Figure 5B ) and cell motility ( Figure S9A and S9B ) . Next , the role of src kinases in the cl-CD95L-mediated cell motility was explored . Using Boyden chamber and wound healing assays , we demonstrated that the src kinase inhibitor PP2 completely abrogated the cl-CD95L-mediated cell motility ( Figure 5D and S10A ) . Likewise , activated PBLs underwent a significant increase in their migration across a vascular endothelial monolayer in the presence of cl-CD95L as compared with control medium and both PP2 and LY294002 hindered the transmigration process ( Figure 5E ) . To ascertain that c-yes participated in igniting the CD95-mediated motility signal , cells were transduced with c-yes shRNA-encoding lentiviral vectors . ( Figure 5F ) . C-yes silencing prevented the cl-CD95L-mediated Akt activation ( Figure 5G ) and thus abrogated both the cell migration of the non-hematological cell line PS120CD95 ( Figures 5H and S9B ) and the transmigration of T-cells across endothelial cells ( Figure 5I ) . Overall , these findings provide unique evidence that the naturally processed CD95L elicits a non-apoptotic signal through the formation of a “non-canonical” DISC devoid of FADD and caspases-8/-10 but encompassing the tyrosine kinase c-yes . This newly disclosed molecular complex was designated MISC for motility-inducing signaling complex . Since in murine model of systemic lupus erythematosus ( SLE ) ectopic expression of CD95L leads to a dramatic exacerbation of the disease index , we wondered whether cleaved CD95L was found increased in SLE-affected patients as compared to healthy individuals . We observed that the CD95L level was significantly higher in sera from SLE patients ( 431 . 28±301 . 8 pg/ml ) than in healthy donors ( 217 . 12±125 . 8 pg/ml ) ( p = 0 . 008 ) ( Figure 6A ) . Furthermore , we showed that the level of soluble CD95L was inversely correlated with two markers of the disease activity , complement C3 ( R2 = 0 . 51 , p = 0 . 0026 ) and C4 ( R2 = 0 . 51 , p = 0 . 0007 ) ( Figure 6B ) . In this regard , we propose that serum CD95L may serve as a solid surrogate marker for the inflammatory progression of the disease in SLE patients . Next , to address whether CD95L found in sera of SLE patients behaves similarly to the one produced in vitro , we incubated activated PBLs with control or SLE sera and analyzed cell morphology by contrast phase and plasma membrane distribution of CD95 using confocal microscopy . Sera from healthy donors induced neither clustering of CD95 at the plasma membrane nor alteration of the cell morphology , whereas sera from lupus patients dramatically aggregated CD95 at the plasma membrane of activated lymphocytes . Similar to observations obtained with cl-CD95L produced in 293 cells , CD95-CAP was found distributed at the extremity of the emitted pseudopod ( see red arrows in Figure 6C ) . In addition , both modification of cell morphology and formation of CD95-CAP were abrogated by pre-incubating cells with anti-CD95 blocking mAb ( clone ZB4 , unpublished data ) . To decipher whether the increased amount of CD95L found in SLE patients compared to healthy individuals was able to trigger cell motility , titration of CD95L was applied on PS120 and PS120CD95 and motility was assessed using wound healing assay . Doses of cleaved CD95L as low as 500 pg/ml efficiently promoted cell migration of PS120CD95 cells , while 250 pg/ml of CD95L was totally inefficient to enhance the basal motile pattern of these cells ( Figure 6D ) . As previously mentioned , cells underwent migration through a CD95-driven mechanism , since CD95-deficient PS120 did not occur in cell migration upon addition of cleaved-CD95L ( Figure 6D ) . Interestingly , 500 pg/ml of soluble CD95L was around the median concentration measured in SLE patients ( 431 pg/ml ) , while 250 pg/ml corresponded to the median concentration found in healthy donors ( 253 pg/ml ) ( Figure 6A ) . Finally , to establish that cleaved-CD95L promotes extravasation of activated T-lymphocytes , a process that is instrumental in accumulating T-cells in inflamed tissues and thus in fuelling the inflammatory process , activated T-lymphocytes were incubated either with SLE or healthy sera and both adhesion on and transmigration across endothelial cells were quantified . In the presence of SLE sera , activated T-lymphocytes underwent a significant increase in their adherence ( 4±1 . 21 versus 2±0 . 4×104 cell equivalent , for SLE versus control sera , p<0 . 05 ) ( Figure 6E ) and migration across a vascular endothelial monolayer ( Figure 6E ) as compared with T-lymphocytes incubated in sera from healthy donors . In addition , blocking the CD95/CD95L interaction with an anti-CD95 neutralizing mAbs ( clone ZB4 ) impeded both adhesion and transmigration of the activated T-cells . In summary , these findings indicated that the high amounts of cleaved-CD95L found in lupus patients promote T-lymphocyte extravasation and accumulation of the immune cells in inflamed tissues .
Most of the experiments aiming to understand the biological functions of CD95L have focused on its transmembrane moiety or engineered soluble CD95L mimicking the multimerized membrane-bound ligand . However , using the physiological soluble CD95L , which is the result of its membrane shedding , we demonstrated for the first time , to our knowledge , that this cytokine released in the circulation behaves as a potent inducer of non-canonical ( caspase-independent ) and non-apoptotic signals that causes endothelial transmigration of activated T-lymphocytes . Cleaved CD95L induces the rapid emission of pseudopods in both activated lymphocytes and fibroblastic cells and subsequently promotes cell motility through a c-yes/Orai1/Ca2+/PI3K signal . Recent studies have revealed that a localized Ca2+ rise occurs beneath the immune synapse upon the TCR engagement through the activation of the Ca2+ release-activated Ca2+ ( CRAC ) channel consisting of STIM1 and Orai1 [53] . STIM-1 is an ER-store Ca2+ sensing molecule [38] , [39] , [41] that links store depletion to Orai1 aggregation , which in turn leads to the selective Ca2+ entry . In this study , we ascertain that the CRAC channel consisting of the pore-forming subunit Orai1 plays a crucial role in the Ca2+ entry observed in the presence of cl-CD95L . In addition , this Ca2+ response that promotes the CD95-mediated cell motility does not display a homogeneous cytosolic distribution but instead constitutes a Ca2+ microdomain localized at the leading edge of the cell protrusion emitted by the migrating cells . This heterogeneous Ca2+ distribution has recently been described as essential in steering cell migration [33] and we speculate that the Ca2+ microdomain observed contiguous to the CD95-CAP may play a similar function . The spatial and temporal co-distribution of the PI3K activity ( production of PIP3 ) , actin polarization , and Ca2+ increase at the leading edge of the CD95-driven pseudopods suggests interconnections between these different processes . This assumption is supported by the fact that failure to induce a calcium response hinders Akt activation and , as a result , decreases cell migration . Few publications have reported molecular targets linking cytosolic Ca2+ increase to Akt activity; for instance , a calmodulin-dependent mechanism in glioblastoma [54] and a PKCα-dependent process in HUVEC [55] enhance Akt phosphorylation and thus activation . Nevertheless , the molecular mechanism underlying the modulation of the Akt activity following the formation of polarized Ca2+ microdomains remains totally unknown and would require further investigation . Upon CD95 engagement , the proximal molecular events leading to activation of the non-canonical PI3K signal remains poorly defined . Here , we underscore that despite the fact that the CD95-mediated PI3K activation occurs through a death domain ( DD ) -dependent mechanism , FADD and the initiator caspases are not detected in the newly identified CD95-containing complex , suggesting either that another adaptor protein may participate in this CD95-mediated “non-orthodox” signal or that undetectable amounts of FADD remain sufficient to promote both the partition of CD95 into aggregated lipid rafts and the subsequent activation of src kinase yes through a yet unknown signaling pathway . In agreement with this latter notion , it has previously been reported that 2% of the caspase-8 activity is sufficient to redistribute CD95 into lipid raft platform through a ceramide-driven process and thus to evoke the apoptotic signal [56] . However , this assumption seems unlikely since inhibition of the caspase activity does not affect either PI3K/Akt activation or cell motility . The newly disclosed CD95-containing complex , which gathers the src kinase c-yes , has been designated MISC for Mobility-inducing signaling complex in comparison with the Death-inducing signaling complex ( DISC ) [57] . Recent evidence emphasizes that the tyrosine kinase c-src abrogates the caspase-8 activity through its phosphorylation on tyrosine 380 , which serves as a docking site for the recruitment and the activation of p85 , the regulatory sub-unit of the class IA PI3K [58] . Nevertheless , the implication of caspase-8 in the recruitment and activation of the PI3K signal remains doubtful in the context of the MISC since no caspase-8 or -10 were detected in this CD95-containing complex . In conclusion , even if the CD95-mediated c-yes activation is instrumental in eliciting PI3K/Akt signal and cell migration , the molecular ordering connecting the src kinase to the activation of PI3K/Akt remains to be clarified . Surprisingly , redistribution of CD95 into lipid rafts has initially been described as a crucial step in the induction of the apoptotic signal [59]–[64] . It is tempting to postulate that as we previously reported [65] , at least two different types of lipid rafts can be gathered around CD95 , and thus , according to the composition of the recruited lipid platform , an opposite signal may be transduced in the presence of cleaved and membrane-bound CD95L . This hypothesis has still to be confirmed , and furthermore , our observations raise the question of how ligands that only diverge by their stoichiometry may account for the partition of CD95 into different types of lipid rafts . This new observation not only is important to better appreciate the function of seric CD95L in cell biology but also offers the opportunity to gain insight into mechanisms underlying autoimmune disorders . Soluble CD95L was significantly increased in SLE patients as compared to healthy individuals , and furthermore , the concentration of CD95L was correlated with the activity of the autoimmune disease . We were concerned that the mix of cytokines present in the serum of SLE patients may affect the effect of cl-CD95L . However , we showed that in contrast to sera from healthy donors , soluble CD95L present in SLE patients efficiently achieved clustering of CD95 at the leading edge of the emitted pseudopods , which promoted both adhesion and transmigration across endothelial cells of the activated T-lymphocytes . Overall , the naturally processed CD95L cytokine evokes lymphocyte motility , which may account for the accumulation of cytotoxic T-cells in inflamed areas , causing tissue damages associated with chronic inflammatory disorders . Indeed , cell migration contributes to leukocyte extravasation and metastasis transition , among others , and these cellular mechanisms participate in the chronicity of inflammatory disorders and subsequent malignancy occurrence . Identification of cleaved-CD95L as a cytokine underlying these cellular processes may hold promises of new therapeutical approaches to prevent both tissue infiltration and damages . As a consequence and counter-intuitively , these findings point out that soluble CD95L may accelerate tumorigenesis through the activation of pro-survival , pro-proliferative signals and besides by promoting cell migration . In agreement with two recent publications [66] , [67] , the concept of CD95-mediated apoptosis contributing to elimination of unwanted and damaged cells can be revisited . Even if the so-called “death receptor” CD95 is not only dedicated to induce cell death , the molecular mechanisms finely tuning the switch from apoptotic to non-apoptosis signals or vice-versa remain unknown . Herein , we show that a crucial factor monitoring the CD95 signaling pathway is its ligand itself . Indeed , the post-translational modification consisting in the cleavage by metalloprotease of the membrane-bound CD95L creates a new ligand displaying totally different functions . Whereas the membrane-bound CD95L helps to contract the immune response and maintains peripheral tolerance , its metalloprotease-cleaved counterparts released in blood circulation induce non-apoptotic signals promoting cell migration , which plays a pivotal function in inflammation and tumorigenesis .
All clinical investigations have been conducted according to the principles expressed in the Declaration of Helsinki . Blood was sampled from patients diagnosed with SLE after written consent was obtained from each individual . This study was approved by the Institutional Review Board at the Centre Hospitalier Universitaire de Bordeaux . All SLE patients fulfilled four or more of the 1982 revised ACR criteria for the disease . C3 and C4 complement components were measured by nephelometry using commercially available kits ( Siemens Healthcare , Saint Denis , France ) . BTP2 , PP2 , LY294002 , Wortmannin , BAPTA-AM ( [1 , 2-bis- ( o-Aminophenoxy ) ethane-N , N , N′ , N′-tetraacetic Acid Tetra- ( acetoxymethyl ) Ester] ) , and 2-APB ( 2-Aminoethoxydiphenyl borate ) were purchased from Calbiochem ( Merck Chemicals Ltd . , Nottingham , UK ) . PHA , DAPI , Fura-2AM , FITC-conjugated cholera toxin B subunit , and DiOC6 were purchased from Sigma-Aldrich ( L'Isle-d'Abeau-Chesnes , France ) . Anti-Akt and anti-phospho-Akt antisera were from Cell Signaling Technology , Inc . ( Boston , MA , USA ) . The homemade soluble CD95L ( gp190-CD95L ) was generated in the laboratory [27] . Anti-caspase-8 ( C15 ) was purchased from Axxora ( Coger S . A . , Paris , France ) . Anti-human CD95 mAb ( DX2 ) was from BD Biosciences ( Le Pont de Claix , France ) . Anti-c-Yes , Anti-Akt , anti-Akt-phosphoS473- , anti-PLCγ1 , and anti-phospho-PLCγ1 antibodies were from Cell Signaling Technology ( Boston , MA , USA ) . The anti-human Orai1 was from Abcam ( Paris , France ) . Plasmid encoding the EGFP-Lifeact was a kind gift from Dr . R . Wedlich-Söldner ( Max Planck Institute of Biochemistry , Martinsried , Germany ) [32] . Lck-GFP- and PHAkt-GFP-containing vectors were provided by Dr . Rodgers ( Oklahoma Medical Research Foundation , Oklahoma City , USA ) [51] and Dr . T . Balla ( National Institutes of Health , Bethesda , USA ) [68] , respectively . The pCR3-FasL-S126E/L127E came from Dr . P . Schneider ( University of Lausanne , Epalinges , Switzerland ) [20] . The pEGFP-C2 plasmids encoding GFP-Orai1 and GFP-Orai1E106A were kindly provided by Dr . M . Cahalan ( University of California , Irvine , CA , USA ) . The human leukemic T-cell line Jurkat and the lymphoma T-cell line H9 were maintained in RPMI supplemented with 8% v/v heat-inactivated FCS and 2 mM L-glutamine at 37°C in a 5% CO2 incubator . 293 cells and the hamster fibroblastic cell line PS120 were cultured in DMEM supplemented with 8% v/v heat-inactivated FCS and 2 mM L-glutamine at 37°C in a 5% CO2 incubator . PBMCs ( peripheral blood mononuclear cells ) from healthy donors were isolated by Ficoll centrifugation and washed twice in PBS . Monocytes were removed by a 2 h adherence step and the naive PBLs ( peripheral blood lymphocytes ) were incubated overnight in RPMI supplemented with 1 µg/ml of PHA . Cells were washed extensively and incubated in the culture medium supplemented with 100 units/ml of recombinant IL-2 ( PeproTech Inc . , Rocky Hill , NJ , USA ) for 6 d . Human umbilical vein endothelial cells ( HUVEC ) [69] were grown in human endothelial serum-free medium ( Invitrogen , Cergy Pontoise , France ) supplemented with 20% FCS , 20 ng/ml basics FGF , 10 ng/ml EGF ( Invitrogen ) , and 1 µg/ml heparin ( Sigma-Aldrich ) . In order to select stable clones , Jurkat cells were transfected as previously mentioned and then placed in a medium supplemented with 1 . 8 mg/ml of neomycin . GFP- , GFP-Orai1WT , and GFP-Orai1E106A-expressing cells were cloned by limiting dilutions and next selected based on their expression of GFP using flow cytometry . Silencing experiments were performed by lentiviral transduction of H9 T-cells or PS120CD95 using validated shRNAmir-pGIPZ vectors for c-yes ( RHS4430-98843955 , -99161516 , -98843955 ) , Orai1 ( RHS4430-98715881 , -101067842 ) , or a nontargeting shRNAmir-pGIPZ vector as a negative control ( Open Biosystems , USA ) . To improve the percentage of transduced T-cells , living cells were harvested 72 h after transduction and green cells ( pGIPZ encodes GFP ) were sorted by flow cytometry using FACSAria ( BD Bioscience ) . 293 cells maintained in a 1% FCS-containing medium were transfected using Calcium/Phosphate precipitation method with 3 µg of empty plasmid or wild type CD95L-containing vector . Media containing cleaved CD95L and exosome-bound full-length CD95L were harvested 5 d after transfection . Dead cells and debris were eliminated through two steps of centrifugation ( 4 , 500 rpm/15 min ) , and then exosomes were pelleted via an ultracentrifugation step ( 100 , 000 g/2 h ) . Finally , debris- and exosome-free supernatants were concentrated ( 10 kDa cut-off centricon ) and dialyzed against PBS . Anti-CD95L ELISA ( Diaclone , Besançon , France ) was performed to accurately quantify the cleaved-CD95L present in sera following the manufacturer's recommendations . Cell viability was assessed using MTT assay , exactly as previously described [63] . In brief , 4 . 104 cells were cultured for 24 h in flat-bottom , 96-well plates with the indicated concentrations of the apoptosis inducer in a final volume of 100 µl . 15 µl of MTT ( 5 mg/ml in PBS ) solution were added , and after 4 h of incubation at 37°C , the absorbance was measured at 570 nm wavelength using the Titertek Labsystems Multiskan reader ( Turku , Finland ) . All steps were performed at 4°C . Cells were washed in PBS/1% ( w/v ) BSA , washed with PBS , and then stained with anti-CD95 mAb ( clone DX2 ) for 30 min at 4°C . Cells were incubated for 30 min with a FITC-conjugated seconfodary antibody and immediately analyzed using FACScalibur ( BD Bioscience ) . Cells were lyzed for 30 min at 4°C in lysis buffer ( 25 mM HEPES pH 7 . 4 , 1% v/v Triton X-100 , 150 mM NaCl , 2 mM EGTA supplemented with a mix of protease inhibitors; Sigma-Aldrich ) . Protein concentration was determined by the bicinchoninic acid method ( PIERCE , Rockford , IL , USA ) according to the manufacturer's protocol . Proteins were separated on a 12% SDS-PAGE and transferred to a nitrocellulose membrane ( GE Healthcare , Buckinghamshire , UK ) . The membrane was blocked 15 min with TBST ( 50 mM Tris , 160 mM NaCl , 0 . 05% v/v Tween 20 , pH 7 . 8 ) containing 5% w/v dried skimmed milk ( TBSTM ) . Primary antibody was incubated overnight at 4°C in TBSTM . The membrane was intensively washed ( TBST ) , and then the peroxydase-labeled anti-rabbit or anti-mouse ( SouthernBiotech , Birmingham , Alabama , USA ) was added for 45 min . The proteins were visualized with the enhanced chemiluminescence substrate kit ( ECL , GE Healthcare ) . The T-cell line H9 and activated PBLs ( 20 . 106 cells per condition ) were incubated with 1 µg/ml of APO1-3 or 100 ng/ml cl-CD95L for 15 min at 4°C ( 0 min ) or at 37°C ( 15 min ) . Cells were then lysed and CD95 was immunoprecipitated using protein A-sepharose beads ( Sigma-Aldrich ) . To immunoprecipitate the cl-CD95L-induced CD95-containing complex , 1 µg of Apo1-3 was added in the cell lysate and CD95 was immunoprecipitated as previously mentioned . After extensive washing , the immune complex was resolved using a 12% SDS-PAGE . Cells were left to adhere 5 min at room temperature to poly-L-Lysine-coated slides and treated with Ig-CD95L or cl-CD95L for indicated times at 37°C . After extensive washing , cells were fixed in PBS containing 4% w/v paraformaldehyde for 15 min . The aldehyde groups were quenched for 10 min using a solution of PBS supplemented with 5% FCS . Cells were incubated with 1 µg/ml of the anti-CD95 mAb ( APO1-3 ) for 30 min at 4°C . Finally , CD95 was revealed using either the Alexa488-conjugated ( green ) or the Alexa555-conjugated ( red ) goat anti-mouse antibody ( Molecular Probes , Cergy Pontoise , France ) in PBS/1% w/v BSA for 30 min at 4°C . Lipid rafts were tagged by FITC-labeled cholera toxin B ( CTB ) . For Orai1 staining , cells were incubated with APO1-3 and 1 µg/ml of the anti-Orai1 mAb in PBS/1% w/v BSA for 60 min at room temperature . CD95 was revealed using secondary Alexa594-coupled goat anti-mouse mAb and Orai1 was observed using Alexa488-conjugated donkey anti-rabbit mAb ( Invitrogen , Carlsbad , CA , USA ) for 60 min at room temperature . Slides were washed with PBS , dried , and mounted with Fluorescent Mounting Media ( Dako , Carpinteria , CA , USA ) . Images were acquired with a confocal microscope TSC SP5 ( Leica , Wetzlar , Germany ) with a 63× objective . T-cells were loaded with Fura2-AM ( 1 µM ) at resting temperature for 30 min in Hank's Balanced Salt Solution ( HBSS ) . After washing with HBSS , the cells were incubated for 15 min in the absence of Fura2-AM to complete de-esterification of the dye . Cells were placed in a thermostated chamber ( 37°C ) of an inverted epifluorescence microscope ( Olympus IX70 ) equipped with a ×40 , UApo/340–1 . 15 W water-immersion objective ( Olympus ) , and fluorescence micrograph images were captured at 510 nm and at 12-bit resolution by a fast-scan camera ( CoolSNAP fx Monochrome , Photometrics ) . To minimize UV light exposure , 4×4 binning function was used . Fura2-AM was alternately excited at 340 and 380 nm , and ratios of the resulting images ( excitations at 340 and 380 nm and emission filter at 520 nm ) were produced at constant intervals ( 5 s or 10 s according to the stimulus ) . Fura-2 ratio ( Fratio 340/380 ) images were displayed and the Fratio values from the regions of interest ( ROIs ) drawn on individual cells were monitored during the experiments and analyzed later offline with Universal Imaging software , including Metafluor and Metamorph . Each experiment was independently repeated 3 times , and for each experimental condition , we displayed an average of more than 20 single-cell traces . Fluorescent images were pseudocolored using the IMD display mode in MetaFluor and assembled without further manipulation in Photoshop ( Adobe ) . Raw data were acquired with MetaFluor and graphed in Origin ( OriginLab ) . [Ca2+]i was calculated using the following equation: [Ca2+]i = Kd ( R−Rmin ) / ( R−Rmax ) ×Sf2/Sf1 , where Kd is the Fura2-AM dissociation constant at the two excitation wavelengths ( F340/F380 ) ; Rmin is the fluorescence ratio in the presence of minimal calcium , obtained by chelating Ca2+ with 10 mM EGTA; Rmax is the fluorescence ratio in the presence of excess calcium , obtained by treating cells with 1 µM ionomycin; Sf2 is the fluorescence of the Ca2+-free form; and Sf1 is the fluorescence of the Ca2+-bound form of Fura2-AM at excitation wavelengths of 380 and 340 nm , respectively . In some experiments cells were placed in a Ca2+-free medium consisting of the HBSS described above in which CaCl2 was omitted and 100 µM EGTA was added in order to chelate residual Ca2+ ions . This medium was added to the cells just before recording to avoid leak of the intracellular calcium stores . In experiments combining CD95 immuno-staining and Ca2+ imaging , cells were incubated or not with 100 ng/ml of cleaved-CD95L at 4°C and then washed . To visualize the plasma membrane distribution of CD95 , the cells were incubated at 4°C with the non-agonistic anti-CD95 mAb DX2 ( 1 µg/ml ) associated with an Alexa555-coupled goat anti-mouse mAb ( GAM ) . Finally , the CD95-stained cells were mixed with 1 µM fura-2AM and cl-CD95L for 30 min at RT in HBSS solution . CD95 immuno-staining was imaged at the beginning and at the end of the experiment . Each experiment was independently repeated 3 times , and for each experimental condition , we displayed an average of more than 15 single-cell traces .
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The “death receptor” CD95 ( also known as Fas ) plays an essential role in ensuring immune tolerance of self antigens as well as in the elimination of the body's cells that have been infected or transformed . This receptor is engaged by the membrane-bound ligand CD95L , which can be released into blood circulation after cleavage by metalloproteases . Hitherto , most of the studies on the CD95 signal have been performed with chimeric CD95Ls that mimic the membrane-bound ligand and exhibit a level of aggregation beyond that described for the metalloprotease-cleaved ligand . Multi-aggregated CD95L elicits a caspase-driven apoptotic signal . In this study , we observe that levels of soluble and naturally processed CD95L in sera of patients suffering from lupus correlate with disease severity . Strikingly , although this soluble CD95L fails to trigger cell death unlike its chimeric version , it induces a “non-canonical” Ca2+/c-yes/PI3K-dependent signaling pathway that promotes the transmigration of T-lymphocytes across the endothelial barrier . These findings shed light on an entirely new role for the soluble CD95L that may contribute to local or systemic tissue damage by enhancing the infiltration of activated T-lymphocytes . Overall , these findings underline the importance of revisiting the role of this “apoptotic cytokine” in the context of chronic inflammatory disorders .
|
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"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
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"cell",
"death",
"signal",
"transduction",
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2011
|
The Naturally Processed CD95L Elicits a c-Yes/Calcium/PI3K-Driven Cell Migration Pathway
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Cytomegalovirus ( CMV ) infection causes birth defects and life-threatening complications in immunosuppressed patients . Lack of vaccine and need for more effective drugs have driven widespread ongoing therapeutic development efforts against human CMV ( HCMV ) , mostly using murine CMV ( MCMV ) as the model system for preclinical animal tests . The recent publication ( Yu et al . , 2017 , DOI: 10 . 1126/science . aam6892 ) of an atomic model for HCMV capsid with associated tegument protein pp150 has infused impetus for rational design of novel vaccines and drugs , but the absence of high-resolution structural data on MCMV remains a significant knowledge gap in such development efforts . Here , by cryoEM with sub-particle reconstruction method , we have obtained the first atomic structure of MCMV capsid with associated pp150 . Surprisingly , the capsid-binding patterns of pp150 differ between HCMV and MCMV despite their highly similar capsid structures . In MCMV , pp150 is absent on triplex Tc and exists as a “Λ”-shaped dimer on other triplexes , leading to only 260 groups of two pp150 subunits per capsid in contrast to 320 groups of three pp150 subunits each in a “Δ”-shaped fortifying configuration . Many more amino acids contribute to pp150-pp150 interactions in MCMV than in HCMV , making MCMV pp150 dimer inflexible thus incompatible to instigate triplex Tc-binding as observed in HCMV . While pp150 is essential in HCMV , our pp150-deletion mutant of MCMV remained viable though with attenuated infectivity and exhibiting defects in retaining viral genome . These results thus invalidate targeting pp150 , but lend support to targeting capsid proteins , when using MCMV as a model for HCMV pathogenesis and therapeutic studies .
Cytomegalovirus ( CMV ) is a member of the β-herpesvirus subfamily of the herpesvirus family and can establish lifelong subclinical ( latent ) infection among the majority of the human population . Active human CMV ( HCMV ) infection is the leading viral cause of birth defects ( in utero and in neonates ) and often the culprit of life-threatening complications in immunocompromised individuals , such as organ transplant recipients and AIDS patients . Currently , there is no licensed vaccine against HCMV infection and conventional anti-HCMV drugs have well-known adverse side effects and are compromised by resistance [1] . These factors call for novel approaches toward vaccine design and drug development against HCMV infections . Thanks to similarities in pathology and disease manifestation caused by CMV infections of humans and mice [e . g . , 2 , 3–5 , and review 6] , pathogenesis studies and therapeutic developments have often relied on murine CMV ( MCMV ) as a model to evaluate the efficacy of lead compounds [7–9] and vaccine candidates [10 , 11] . Recent high-resolution cryoEM structures of human herpesviruses [12–14] , particularly the demonstration of inhibitors designed based on the structure of small capsid protein ( SCP ) [12 , 13] , have opened the door to structure-guided design of new drugs and vaccines targeting HCMV capsid proteins and the β-herpesvirus-specific tegument protein pUL32 [or phosphoprotein pp150 , see review 15 , 16–18] . Rationales for targeting pUL32 are manifold: First , it is essential to HCMV propagation [19 , 20]; Second , pUL32 is unique to human β-herpesviruses; Third , pp150 has been shown to be the most immunogenic in clinical setting [21]; Fourth , from a general point of view , regulatory functions of protein phosphorylation have been targeted against cancers [22] and viral infections , including HCMV [23] . The cryoEM reconstruction of HCMV at 3 . 9 Å resolution [14] reveals that pUL32 forms a unique capsid-binding tegument layer , likely to secure encapsidation of its dsDNA genome of 235 kbp , which is the largest among all herpesviruses . Particularly , 320 groups of three pUL32nt subunits form a “Δ”-shaped fortifying structure on every triplex . pUL32 is an abundant and immunogenic protein that is essential for HCMV virion egress and maturation [19 , 24] . Yet careful examination of their genomes suggests there might be structural differences between HCMV and MCMV . For example , HCMV pUL32 sequence is about 40% longer than pM32 ( the homolog of pUL32 in MCMV ) [25] , suggesting that an examination of the structure of MCMV in detail may be fruitful in assessing the similarities and differences between HCMV and MCMV . Therefore , the functional and structural significance of pM32 remains to be established , in stark contrast to the large body of MCMV-based cell and animal studies concerning CMV infections . Here , by cryoEM and sub-particle reconstructions , we have obtained structures of the MCMV capsid and its associated pM32 ( pp150 ) at near-atomic resolutions and built their atomic models , the first for any MCMV proteins . Comparison of the virion structures of MCMV and HCMV reveals that the patterns of pp150 binding to capsid differ between HCMV and MCMV , despite highly similar structures of their capsid proteins , including SCP . The atomic details underlying pp150-pp150 and pp150-capsid protein interactions rationalize the different capsid-binding patterns of pp150 in MCMV and HCMV . Our mutagenesis studies further establish pp150’s varying levels of functional significance in MCMV and HCMV infections . These results thus establish the validity of using MCMV as HCMV model for pathogenesis and therapeutic studies when targeting capsid proteins , but raise concerns when targeting pp150 .
A technical challenge of determining the structure of herpesvirus particles is the enormous size of herpesvirus virions exceeding 200 nm in diameter , creating a focus gradient across the large sample thickness needed to fully embed the virion and the breakdown of the Central Projection Theorem due to a curved Ewald-sphere [14 , 26] . Initial cryoEM reconstruction from the 1 , 200 MCMV virion particle images ( e . g . , S1A–S1D Fig ) recorded on a CCD camera was limited to ~12 Å resolution ( S1E Fig ) . To alleviate the Ewald-sphere curvature effect [26 , 27] , we attempted to reduce the sample thickness by partially solubilizing the viral envelope through mild detergent treatment ( S2A Fig ) . From a total of 47 , 982 particles of MCMV virions from 2 , 200 300kV cryoEM micrographs recorded on photographic films ( e . g . , S2A Fig ) , we obtained an icosahedral reconstruction at ~5 Å resolution ( Fig 1A and S1 Movie ) . Local resolution assessment by Resmap ( S2B Fig ) [28] indicates that densities of the capsid shell and especially near the base of the capsomers have the best resolution ( from 4 . 0 Å to 4 . 5 Å ) and that densities at the outmost radii and inside the capsid shell ( i . e . , DNA genome ) have resolutions worse than 4 . 5 Å , likely due to a combination of structural heterogeneity/flexibility ( for DNA-related densities ) and the more severe Ewald-sphere curvature effect for densities at larger radii . Though at different resolutions , both the early reconstruction from CCD images of intact virions and the higher resolution reconstruction from detergent-treated virions show identical structural organizations of tegument and capsid proteins ( S1E Fig and Fig 1A ) . The ~5 Å 3D reconstruction shows a highly conserved T = 16 icosahedral virion capsid revealing the molecular boundaries among 12 pentons , 150 hexons , 320 triangular triplexes and 260 pM32 ( pp150 ) dimers ( Fig 1A and 1B ) , allowing identification of individual molecules ( Fig 1C and 1D ) . Imposing icosahedral symmetry during 3D reconstruction both weakens the density and lowers the resolution of regions with deviation from strict icosahedral symmetry . One of the twelve icosahedral vertices of the herpesvirus capsid does not contain a penton , but a DNA packaging/ejection portal complex [29–33]; thus , tegument densities interacting with pentons are weaker than those interacting only with hexons . The 3-fold symmetrized tegument densities associating with triplex Tf are also weakened and are only visible when displayed at a lower density threshold ( see S3 Fig for a sub-particle reconstruction of this region showing a pp150 dimer attached to triplex Tf ) . The structural components within an asymmetric unit encompass 1/5 of a penton capsomer , 2 . 5 hexon capsomers ( 1 P hexon , 1 C hexon , and 1/2 of an E hexon ) , 5 and 1/3 triplexes/pM32 dimers ( Ta , Tb , Tc , Td , Te , and 1/3 Tf ) ( Fig 1C ) [34] . To overcome the aforementioned problem of weakened densities and limited resolutions , we have used a sub-particle reconstruction strategy to obtain structures of the MCMV capsid and its associated pM32 at near atomic resolutions ( Fig 2B–2E , S3–S8 Figs , S2–S4 Movies ) and also built atomic models for tegument protein pM32 and all capsid proteins ( S9–S12 Figs ) . In particular , our sub-particle reconstruction of the region around the 3-fold axis shows that pM32 on triplex Tf also exists as a dimer ( S3 Fig ) , as with triplexes Ta , Tb , Td , and Te ( Fig 1A–1D ) . Notably , all previous structural studies failed to establish the directionality of Tf . Our result suggests the underlying triplex Tf lacks strict 3-fold symmetry , as the other triplexes , thus resolving a historic mystery about the chemical composition of Tf . The improved resolution of the sub-particle reconstructions allowed us to build de novo atomic models for the N-terminal one-third portion of pM32 ( pM32nt ) and the four capsid proteins [the major capsid protein ( MCP ) , the small capsid protein ( SCP ) , the triplex monomer protein ( Tri1 ) , the triplex dimer protein ( Tri2 ) ] . In total , we have built atomic models of 55 unique conformers , including 16 MCP , 16 SCP , 5 Tri1 , 5 Tri2A , 5 Tri2B and 8 pM32nt ( Fig 2H ) , amounting to over 27 , 000 amino acid residues . As detailed below , the atomic models of pM32 of MCMV and pUL32 of HCMV differ , though those for their capsid proteins are similar , with root-mean-square deviation ( RMSD ) distances between corresponding capsid protein models ranging from 1 . 95 to 3 . 36Å ( 2 . 47 Å for penton MCP , 2 . 09 Å for hexon MCP , 1 . 95 Å for SCP , 3 . 36 Å for Tri1 , 2 . 05 Å for Tri2A , and 2 . 10 Å for Tri2B ) . Like that of HCMV [14] , the structure of the 1 , 353 a . a . long ( 149 kDa ) MCP monomer of MCMV consists of seven domains ( Fig 3A ) : upper ( a . a . 477–1015 ) , channel ( a . a . 398–476 and 1303–1353 ) , buttress ( a . a . 1090–1302 ) , helix-hairpin ( a . a . 190–233 ) , dimerization ( a . a . 291–362 ) , N-lasso ( a . a . 1–59 ) , and a bacteriophage HK97-like ( “Johnson” ) -fold ( a . a . 60–189 , 234–290 , 363–397 , and 1016–1089 ) . These domains are located in the tower ( upper , channel , and buttress domains ) and the floor ( helix-hairpin , dimerization , N-lasso , and Johnson-fold domains ) regions of each capsomer subunit ( Fig 2H ) . Because the Johnson-fold domain of MCMV MCP corresponds to the entire molecule of the HK97 MCP ( gp5 ) [35] , segments corresponding to HK97 gp5’s four domains—axial ( A ) , extended loop ( E loop ) , peripheral ( P ) , and spine helix—are designated as A , E-loop , P , and spine helix sub-domains , respectively ( Fig 3B ) . MCP monomers in hexons and pentons have distinct conformations . For instance , the sequence segment corresponding to the helix-loop-helix motif in the buttress domain of hexon MCP ( Fig 3C ) folds into a single long helix in penton MCP ( Fig 3D ) . As in HCMV [14] , there are three notable types of network interactions in the MCP floor regions ( Fig 3E and 3F ) . Type I interactions are intra-capsomeric β-sheet augmentations which occur between two adjacent MCPs within a capsomer . As exemplified by C4 and C5 MCPs in Fig 3F ( left panel ) , two β-strands from the E-loop of Johnson-fold domain and one β-strand from the dimerization domain of C5 MCP are joined by two β-strands from the N-lasso domain of C4 MCP , resulting in a five-stranded β-sheet ( Fig 3F , left panel ) . Type II interactions are inter-capsomeric interactions that occur between two pairs of α-helices in the dimerization domains of MCPs across local 2-fold axes , as illustrated by E2 and C5 MCPs in Fig 3F ( middle panel ) . Like type II interactions , type III interactions are inter-capsomeric interactions , but are formed by three MCPs featuring the lassoing action of the N-lasso domain ( E1 ) , which extends out and lashes around an E-loop ( C5 ) and a N-lasso neck ( C4 ) of two type I interacting MCPs located across a local 2-fold axis ( Fig 3F , right panel ) . Additionally , a small helix bundle is formed from an α-helix from E1 N-lasso , two α-helices from the helix-hairpin domain of C5 MCP , and an α-helix from the buttress domain of C5 MCP , further securing the E1 N-lasso ( Fig 3F right panel ) . Our model of SCP encompasses residues 36–95 of the 98 a . a . long M48 . 2 gene product and consists of three 3 . 5-turn α-helices and two connecting loops , folded into a triangular spiral with the N-terminal helix ( H1 ) pointing outwards ( Fig 4A ) . An MCP monomer binds an SCP monomer to form a heterodimer ( Fig 4B ) , which constitutes one of the five and six subunits in penton and hexon capsomers , respectively ( Fig 4C ) . The H3 helix of SCP inserts into a deep groove in a region of MCP upper domain rich in α-helices and loops ( Fig 4D ) . Sequence-based surface analysis indicates that predominantly hydrophobic interactions contribute to MCP-SCP binding ( Fig 4E ) . Each triplex is a heterotrimer containing one unique conformer of Tri1 and two conformers of Tri2—Tri2A and Tri2B—that “embrace” each other to form a dimer ( Fig 5A–5D and 5F–5G ) . Tri2 monomer consists of three domains: clamp ( a . a . 1–88 ) , trunk ( a . a . 89–183 and 291–311 ) , and embracing arm ( a . a . 184–290 ) . While the clamp and trunk domains in Tri2A and Tri2B are nearly identical ( RMSD is only 1 . 12 Å ) and are superimposable by a ~120° rotation about the local 3-fold axis ( Fig 5F , right panel ) , their embracing arms differ by a ~45° bend with an RMSD of 3 . 69 Å ( Fig 5H ) . Clung to the side of the two embracing Tri2 subunits is the Tri1 monomer , which consists of three domains: N-anchor ( a . a . 1–45 ) , trunk ( a . a . 46–171 ) , and third-wheel ( a . a . 172–294 ) ( Figs 2H and 5D ) . The helix-loop-helix-loop motif of the N-anchor domain penetrates the capsid floor near its local 3-fold axis such that both its helices fill the valley between the P-subdomain β-sheet and the spine helix of a Johnson-fold domain of an MCP subunit ( Fig 5C and 5E , and S1 Movie ) . Thus , N-anchor anchors Tri1 and the entire triplex from inside the capsid beneath the MCP floor , simultaneously sealing the hole at the local 3-fold axis where three neighboring MCP P-subdomains assemble . Notably , such “internally anchored” interactions could be pressure-fortified [12] , i . e . , when the N-anchors are pressed against the MCP floor region from within the capsid by incoming DNA during genome packaging , the capsid floor would be better sealed and further strengthened , rather than weakened [12] . The reconstruction densities exhibit different patterns of tegument-capsomer association between MCMV and HCMV ( Fig 6A and 6B ) . Our MCMV reconstructions show 260 tegument densities ( Fig 6A ) , as opposed to the 320 tegument densities of HCMV ( Fig 6B ) [14 , 36] . These different patterns and reduced number of the tegument densities in MCMV are not due to detergent treatment as reconstruction of intact MCMV virions at 12Å resolution shows identical structures ( cf . S1F and S1G Fig ) . In addition , their detailed structures differ , existing as a group of two subunits in MCMV ( Fig 6A ) and a group of three in HCMV ( Fig 6B ) , with arrangements resembling the Greek letters “Λ” and “Δ” , respectively . Moreover , as shown in yellow in Fig 6A , pM32 does not bind to triplex Tc in the MCMV capsid . Neither are pM32 density connections observed between edge and facet capsomers . Specifically , pM32 dimers of the edge type join P hexons , E hexons , ( S13 Fig ) and pentons on the 30 edges of the icosahedral capsid to triplexes Ta , Tb , and Td ( Figs 1B and 6A ) , while pM32 dimers of the facet type bind three C hexons together in the center of each icosahedral facet to triplexes Te and Tf ( Figs 1B and 6A , and S13 Fig ) . In contrast , all triplexes of the HCMV nucleocapsid—including triplexes Tc—are occupied by three pUL32 subunits arranged as a dimer and a monomer of pUL32 . Atop each triplex , dimeric pUL32 branches out to interact intimately with their closest respective capsomer subunits , in an analogous fashion to the “Λ”-shaped tegument densities in MCMV ( Fig 6B ) . However , the third ( monomeric ) copy of pUL32 in HCMV bridges the top of the triplex with a third neighboring capsomer ( Fig 6B ) . This monomeric tegument density , in conjunction with the presence of pUL32 subunits above triplex Tc , facilitates connections between the edge and facet capsomers of HCMV ( Fig 6B ) [36] not observed analogously in MCMV . Our atomic models of MCMV pM32 contain approximately 1/3 of the full-length ( 718 a . a . ) protein at its N-terminal region ( pM32nt-a , consisting of a . a . 66–87 , 91–105 , 137–150 , and 173–295; pM32nt-b , consisting of a . a . 66–113 , 128–156 , and 173–295 ) . These regions correspond to all visible densities in the cryoEM maps , suggesting that the rest of the pM32 protein is flexible . Similar to pUL32nt of HCMV , pM32nt of MCMV is dominated by α-helices ( Fig 7A and S14 Fig ) and characterized by upper and lower helix bundles joined by a central long helix ( ~69 Å in length , a . a . 208–253 ) ( Figs 2H and 7A ) . We also identified the conserved region 1 ( CR1 ) and region 2 ( CR2 ) in MCMV pM32nt ( Fig 7A ) . However , only one cysteine , rather than four , was identified in pM32’s equivalent sequence of HCMV pUL32’s cys tetrad ( Fig 7A ) [25] . All bound pM32s are dimerized in MCMV and can be classified into two types , either pM32-a ( cyan ) or pM32-b ( orange-red ) , based on their relative locations on the capsid ( Fig 1C and 1D ) . The subunits of pM32nt dimers cluster on each triplex and lean against two neighboring MCPs ( S15A Fig ) . pM32nt-a and pM32nt-b are similar in structure ( the magnitude of RMSD is 1 . 20 Å ) ( Fig 7B ) and form a “Λ”-shape configuration . As shown in S15B and S15C Fig , the two conformers interact with capsid proteins via hydrophobic and/or hydrophilic interactions , bearing a marked resemblance to those in HCMV ( S15E and S15F Fig ) . Careful comparison of the atomic model of pM32 dimer and the corresponding pUL32 subunits in HCMV ( for example , pM32 and pUL32 dimer from triplex Te regions ) reveals that 17 residues in the pM32-pM32 interface are within 3 Å of each other , as opposed to only 4 residues at the pUL32-pUL32 interface ( Fig 7C ) . The existence of 13 additional residues at the molecular interface of the pM32 dimer indicates a stronger and more rigid pM32-pM32 association than between pUL32-pUL32 , which is congruent with the pairwise presence/absence of pM32 subunits on MCMV capsid . This also supports the observation that pM32 exists only as a dimer in our reconstructions in contrast to the existence of both monomeric and dimeric forms of pUL32 in HCMV [14] . Additionally , rigid-body fitting of the HCMV triplex-pUL32nt atomic model ( for example , Td region ) into MCMV triplex density ( in order to align triplex models from MCMV and HCMV ) reveals further comparative insights into pM32/pUL32 binding in MCMV and HCMV ( Fig 7D ) . First , as mentioned above , only two pM32 subunits bind with MCMV triplex as a dimer instead of three pUL32 subunits for each HCMV triplex . Second , the organization of pM32/pUL32 dimers in MCMV and HCMV with respect to triplex resemble each other , though the specific orientation of dimer subunits possesses some distinctions ( Fig 7D ) . In contrast to the minor rotational displacements exhibited between pM32-a and pUL32-a , pM32-b and pUL32-b show greater translational and rotational displacements ( Fig 7D ) . Third , Tri1 residues of MCMV and HCMV that interact with pM32 and pUL32 , respectively , are conserved ( S16 Fig ) and conceivably play a crucial role in pM32/pUL32 binding . In contrast to the binding of a pM32 dimer to each of triplexes Ta , Tb , Td , Te , and Tf , no pM32 is bound to triplex Tc ( Figs 6A and 8A , and S19 Fig ) . Rigid-body fitting of triplex Td decorated with pM32nt dimer into triplex Tc density ( Fig 8B ) reveals that distances between pM32’s upper domains to their adjacent SCPs are greater at triplex Tc than triplex Td ( 7 Å vs . 4 Å for pM32nt-a; 14 Å vs . 6 Å for pM32nt-b ) ( Fig 8C , left panels ) . The rigidity of pM32 dimer discussed above conceivably prevents pM32 from extending , or “spreading , ” to span such long distances , explaining the absence of pM32 above triplex Tc in MCMV . Next , we show that pM32 is important , though not essential , for MCMV replication in vitro by successfully generating an infectious MCMV mutant with deletion of the coding sequence of M32 , which we term ΔM32 . To generate ΔM32 , we adopted our previously-published protocols for generating gene-deletion mutants of HCMV to mutagenize a BAC clone of the wild-type MCMV ( Smith strain ) genome ( MCMVBAC ) by deleting the M32 open reading frame [37–39] . Furthermore , rescued viral mutant , R-M32 , was generated from ΔM32 , by restoring the M32 sequence , following the procedures as described previously [38 , 40] . Deletion of the M32 gene in ΔM32 and the restoration of the M32 sequence in R-M32 were confirmed by PCR and Southern blot analysis . To determine whether ΔM32 has any growth defects in vitro and whether pM32 is essential for MCMV replication in cultured cells , we measured the growth rates of mutant ΔM32 , rescued mutant R-M32 , and parental MCMVBAC viruses in NIH 3T3 cells . ΔM32 exhibited growth defective phenotype as the titers of ΔM32 were lower than those of MCMVBAC in a 6-day growth study ( Fig 9A ) . At day 4 , the titer of ΔM32 was about 100-fold lower than that of MCMVBAC ( Fig 9A ) . The observations that ΔM32 grew in NIH3T3 cells indicate that M32 is not required for MCMV replication in vitro . Thus , in contrast to its HCMV homolog pUL32 , which is essential for HCMV replication [38 , 40] , M32 is not essential—though important—for MCMV replication in vitro . Though the reduced titer of ΔM32 limited the isolation of large numbers of viral particles , we still managed to purify ΔM32 viral particles . Consistent with the 100-fold reduction in its growth rate ( Fig 9A ) , the concentration of ΔM32 viral particles produced in the cell culture is low compared to that of the wild-type virus based on our EM analysis ( Fig 9B and 9C , and S1A–S1D Fig ) . Specifically , deletion of the M32 gene appeared to reduce the formation of infectious , DNA-containing virions , as it was difficult to find DNA-containing particles . Instead , most observed particles were non-infectious enveloped particles ( NIEPs ) and dense bodies ( Fig 9B and 9C ) , suggesting that DNA-containing particles are less stable in ΔM32 . From 200 fully-enveloped cryoEM particles ( an example of which is denoted by the open black arrow in Fig 9B and 9C ) , we obtained a 3D icosahedral reconstruction of ΔM32 at ~25 Å resolution ( Fig 9D ) . This resolution is sufficient to resolve pentons , hexons , and triplexes on the capsid , and as shown in the enlarged facet of the icosahedral reconstruction ( Fig 9E ) , the capsid of ΔM32 possesses the same molecular architecture as that of other herpesviruses [41 , 42] . In contrast to the ΔM32 sample , wild-type MCMV sample prepared by the same procedure demonstrated a significantly higher viral particle concentration when examined by cryoEM ( S1A Fig ) , and many particles are DNA-containing virions ( solid black arrow in S1A Fig ) . From 1 , 200 wild-type MCMV virion particles ( e . g . , S1B Fig ) , we obtained a ~12 Å resolution reconstruction ( S1E Fig ) . We also obtained a reconstruction from enveloped particles without DNA ( NIEPs , an example is shown in S1C Fig ) , the capsid and tegument densities of which were identical to those of the virion reconstruction . Lastly , structural comparison between wild-type MCMV ( ~12 Å ) and the mutant ΔM32 ( ~25 Å ) confirmed the absence of pM32 atop triplexes ( Fig 9D and 9E ) .
In this study , we present the first cryoEM structures obtained from the MCMV virion and M32-deletion mutant , as well as functional data that together establish important differences concerning the structural and functional roles of pp150 in HCMV and MCMV . The attainment of an atomic model of the MCMV particle—consisting of 55 unique protein conformers of capsid and tegument proteins—is a remarkable endeavor , considering there were no atomic structures ever reported for any MCMV proteins prior to this study . Despite highly similar structures of their capsid proteins , MCMV and HCMV have distinctive capsid-tegument binding patterns: 260 “Λ”-shaped pM32nt dimers on all triplexes but Tc in each MCMV , as opposed to 320 “Δ”-shaped pUL32nt structures ( one dimer + one monomer of pUL32nt ) on all triplexes in each HCMV . Substantially more amino acids are involved in pM32-pM32 interactions in MCMV than in pUL32-pUL32 associations in HCMV , suggesting a more rigid pM32-pM32 dimer structure than pUL32-pUL32 dimer . Finally , M32-deletion mutant can be successfully generated , albeit with a growth rate reduced ~100-fold , whereas UL32 deletion in HCMV is lethal . The different extent of pp150-pp150 interaction and pp150 dimer rigidity in MCMV and HCMV may account for the distinctive capsid-binding patterns of pp150 in the respective viruses . The additional rigidity of pp150 dimer in MCMV may prevent the spreading of the dimer necessary to span the increased distances to adjacent SCPs surrounding the triplex Tc , presumably leading to the lack of pM32-binding in this region . The more extensive inter-pp150 interactions observed for MCMV CATC may also account for the observation that pp150 ( pM32 ) does not exist as a monomer in MCMV , while approximately 1/3 of pp150 ( pUL32 ) exists as a monomer in HCMV . Interestingly , HCMV pp150 is about 40% longer in sequence than its homologs in MCMV ( S1 Table ) , simian CMV , and human herpesviruses 6 and 7 [25] though a correlation has not been established between this length difference and the polymorphic difference in tegument-capsomer association due to the limited resolutions of the existing cryoEM structures of these β-herpesviruses [43 , 44] . On the basis of no viral growth in cultured cells electroporated with a bacterial artificial chromosome containing the UL32-deletion HCMV genome [38] , pUL32 was considered to be an essential tegument protein that can be targeted for therapeutic development against HCMV infection [14] . In contrast , its counterpart in MCMV , pM32 , is nonessential: M32-deletion mutant is viable in vitro , although defective in generating DNA-containing virions and its infectivity attenuated by 100-fold ( Fig 9A ) . Evidence shows that “SCP-deficient” HCMV viral particles have decreased viral yield ( 10 , 000-fold ) compared to that of wild-type virus [45] . As mentioned previously , SCP structures in MCMV and HCMV are highly conserved . Thus , it is worth considering SCP as a drug target while rationally designing novel drugs against MCMV infections . While our results establish the structural basis of using MCMV as a model for HCMV pathogenesis and therapeutic studies when targeting capsid proteins such as SCP , caution is warranted when targeting tegument protein pp150 due to its different structural organization and functional roles in MCMV and HCMV reported in this study .
Mouse NIH3T3 cells ( ATCC® CRL-1658™ ) were cultured in Dulbecco’s Modified Eagle Medium ( DMEM ) plus 10% fetal bovine serum ( FBS ) . Twenty flasks ( 175 cm2 each ) of cells were grown to 90% confluence and then infected with MCMV Smith strain at a multiplicity of infection ( MOI ) of 0 . 1 . At 6 days post infection , when half of the cells were lysed , the media was collected and centrifuged at 10 , 000g for 15 min to remove cell debris . The clarified supernatant was then collected and centrifuged at 80 , 000g for 1 hr to pellet MCMV virions . Pellets were resuspended in a total volume of 2 ml phosphate buffered saline ( PBS , pH 7 . 4 ) and loaded on a 15%-50% ( w/v ) sucrose density gradient and centrifuged at 100 , 000g for 1 hr . We usually observe three light-scattering bands—top , middle , and bottom–containing mainly noninfectious enveloped particles ( NIEPs ) , virions , and dense bodies , respectively . The middle band ( virions ) was collected and diluted in PBS to a total volume of 13 ml . Virion particles were pelleted again at 80 , 000g for 1 hr and resuspended in 30 μl PBS for cryoEM sample preparation . Purified intact MCMV virions were mixed with NP-40 detergent at 1% final concentration to partially solubilize the viral envelope . Immediately after , aliquots of 2 . 5 μl of this treated sample were applied to 200-mesh Quantifoil R2/1 grids , blotted with filter paper , and plunge-frozen in liquid ethane . CryoEM images were collected at liquid nitrogen temperature in an FEI Titan Krios cryo electron microscope operated at 300 kV with parallel illumination . Images were recorded on Kodak SO163 films with a dosage of ~25 e−/Å2 at 47 , 000× nominal magnification . A total of 2 , 200 films were recorded and digitized using Nikon Super CoolScan 9000 ED scanners at 6 . 35 μm per pixel ( corresponding to 1 . 351 Å per pixel at the sample level ) . Defocus values of all micrographs were determined with CTFFIND3 [46] to be in the range of -1 μm to -3 μm . Particles were picked with Ethan [47] and then manually screened with the boxer program in EMAN [48] to keep only well-separated and artifact-free particles . A total of 58 , 254 particle images were boxed out from the micrographs with EMAN . The original particle images were binned 8× , 4× , or 2× stepwise to speed up data processing . Icosahedral refinement and reconstruction were carried out with the common line-based IMIRS package [49 , 50] and GPU-implemented reconstruction program eLite3D [51] , respectively . The final capsid reconstruction was obtained by averaging 47 , 982 particles . Due to the large size of the MCMV capsid ( over 1 , 300 Å in diameter ) , resolution of our initial icosahedral reconstruction was limited to 5 Å , likely due to slight particle deformation and defocus gradient across the depth of the sample [12 , 26] . To obtain higher resolution structures for reliable atomic model building , we applied a localized reconstruction strategy [12 , 52–54] to reconstruct subareas surrounding the 2-fold , 3-fold , and 5-fold axes of the icosahedral MCMV capsid ( the defocus value for each sub-particle was recalculated based on the geometric location of the sub-particle on the capsid ) . The 47 , 982 high-quality particle images selected from IMIRS refinement were binned 4x and reprocessed using Relion [55] with icosahedral symmetry applied . Using the script downloaded from www . opic . ox . ac . uk/localrec [52] , positions of sub-particles in the original particle images ( i . e . , without binning ) were calculated with a radial distance of 576 . 8 Å from the center of the viral particle . A total of 575 , 784; 959 , 640; and 1 , 439 , 460 sub-particles in 400 x 400 pixels were then boxed out for the 5-fold , 3-fold , and 2-fold axis , respectively . Localized reconstruction of these sub-particles were iteratively refined in Relion , reaching a final estimated resolution of 3 . 8 , 3 . 6 , and 3 . 8 Å for the 5-fold axis , 3-fold axis , and 2-fold axis sub-particle maps , respectively , based on the 0 . 143 FSC criterion [56] . Each asymmetric unit of the T = 16 icosahedral reconstruction of the MCMV particle contains 55 unique copies of protein subunits: 16 MCPs , 16 SCPs , 15 triplex subunits ( excluding triplex Tf , the resolution of which at 6 . 8 Å is insufficient for atomic model building ) , and 8 pM32s . We utilized the SWISS-MODEL server [58] to generate homology models of penton MCP , hexon MCP , SCP , Tri1 , Tri2A , and Tri2B with the corresponding subunit conformers in the atomic model of HCMV [14] as templates . These initial models were docked into the sub-particle reconstructions ( sharpened with a B factor of -150 Å2 for the 5-fold axis map , -160 Å2 for the 3-fold axis map , and -160 Å2 for the 2-fold axis map ) and then adjusted manually in Coot [59] . All models were then iteratively improved by Phenix real space refinement [60] and manual readjustment in Coot . Eventually , all the atomic models built based on high-resolution sub-particle reconstructions were assembled together and refined against the icosahedral reconstruction with Phenix to correct for inter-molecular clashes . Unlike for the capsid proteins , the atomic model for pM32nt was built de novo . The resolution of densities corresponding to pM32nt was poorer than that of capsid proteins even in the improved sub-particle reconstructions . Thus , the map obtained by averaging triplexes Tb , Td , and Te regions as previously mentioned was also used to facilitate atomic model building for pM32nt . Secondary structures predicted by Phyre2 server [61] were used to guide backbone tracing using the Baton_build utility in Coot . Observable main chain residue bumps in the density informed Cα placement , and registration was accomplished using distinguishable side chain densities . Iterative model refinement was performed as for the capsid proteins described above . We used a previously reported bacterial artificial chromosome ( BAC ) -based clone of MCMV Smith strain ( MCMVBAC ) , maintained as a BAC-based plasmid in E . coli , to produce infectious progeny in mouse NIH3T3 cells [37–39] . As reported , this progeny retained wild-type growth characteristics in vitro , as previously shown [37–39] . Mutant ΔM32 , which contained a deletion of the entire coding sequence of M32 , was derived from MCMVBAC using a two-step mutagenesis protocol [38 , 40] . In the first step , we inserted at the M32 sequence with a cassette ( tet/str ) containing the tetracycline resistance gene tetA and rpsL gene conferring streptomycin susceptibility , following the mutagenesis procedures as described previously [38 , 40] . The bacteria harboring the mutant BAC constructs were electroporated with the PCR-amplified tet/str cassette . Successful insertion of the tet/str cassette was screened by selecting for bacterial colonies resistant to tetracycline . In the second step , the tet/str cassette was targeted for deletion . The resulting mutant , which only contained the deletion of M32 ORF sequence , was streptomycin-resistant and , therefore , was easily selected in the presence of the antibiotics [38 , 40] . Rescued mutant R-M32 was generated from ΔM32 , following the experimental procedures for construction of rescued viruses as described previously [38 , 40] . The M32 regions in the mutant and the rescued virus were analyzed by restriction digestion profile and sequencing analyses . Virus growth analyses were carried out by infecting NIH 3T3 cells ( n = 1x106 ) with viruses ( MOI = 0 . 5–1 ) [62 , 63] . The cells and medium were harvested at different time points postinfection . Viral stocks were prepared and used to infect NIH3T3 cells , followed by agar overlay . Viral titers were determined by counting the number of plaques 5–7 days after infection [62 , 63] . The values obtained were averages from three independent experiments . For cryoEM , we used a sucrose density gradient to purify viral particles from the supernatant of culture media of wild-type or ΔM32-infected NIH3T3 cells as previously described [36] . The sucrose gradient fraction with the most viral particles from the wild-type MCMV prep and the corresponding fraction from the ΔM32 prep were collected for cryoEM imaging . We suspended a 3 μl aliquot of each of these samples to a holey carbon-coated cryoEM grid , which was blotted and immediately plunge-frozen so that viral particles were suspended within vitreous ice across holes of the holey carbon [64] . Low-dose ( ~20 e−/Å2 ) cryoEM images were recorded on a Gatan 16-megapixel CCD camera in a Titan Krios cryo electron microscope operated at 300 kV at a magnification of 79 , 176× with Leginon [65] . Consistent with the 100-fold reduction in its growth rate , the concentration of ΔM32 viral particles produced in cell culture is low when compared to that of the wild-type virus . Because it was hard to find viral particle-containing sample regions for imaging , many imaging sessions were painstakingly carried out to obtain just 500 micrographs for ΔM32 . ( On average , each micrograph only contained one viral particle . ) We selected 200 fully-enveloped cryoEM particles ( see an example pointed by the open black arrow in Fig 9B and 9C ) to reconstruct a 3D structure of ΔM32 at ~25 Å resolution with the IMIRS package [49 , 50] . In contrast to the ΔM32 sample , wild-type MCMV preparation obtained from the same procedure had a significantly higher viral particle concentration . 3D reconstructions obtained from wild-type virions and NIEPs with IMIRS were thus at higher resolutions than ΔM32 due to the larger number of particles used .
CryoEM maps and atomic models are deposited in the Electron Microscopy Data Bank ( EMDB ) and the RCSB Protein Data Bank ( PDB ) , respectively . They include the cryoEM density maps of the MCMV capsid , sub-particle reconstructions at 2-fold , 3-fold , and 5-fold axes ( accession code EMD-9366 , EMD-9367 , EMD-9368 , and EMD-9369 , respectively ) and a single coordinate file containing 55 atomic models ( PDB accession code 6NHJ ) .
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Cytomegalovirus ( CMV ) infection is a leading viral cause of birth defects and could be deadly to AIDS patients and organ transplant recipients . Absence of effective vaccines and potent drugs against human CMV ( HCMV ) infections has motivated animal-based studies , mostly based on the mouse model with murine CMV ( MCMV ) , both for understanding pathogenesis of CMV infections and for developing therapeutic strategies . Distinct from other medically important herpesviruses ( those responsible for cold sores , genital herpes , shingles and several human cancers ) , CMV contains an abundant phosphoprotein , pp150 , which is a structurally , immunogenically , and regulatorily important tegument protein and a potential drug target . Here , we used cryoEM with localized reconstruction method to obtain the first atomic structure of MCMV . The structure reveals that the organization patterns of the capsid-associated tegument protein pp150 are different in MCMV and HCMV , despite their highly similar capsid structures . We also show that deleting pp150 did not eliminate MCMV infection in contrast to pp150’s essential role in HCMV infections . Our results have significant implication to the current practice of using mouse infected with MCMV for HCMV therapeutic development .
|
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2019
|
Atomic structures and deletion mutant reveal different capsid-binding patterns and functional significance of tegument protein pp150 in murine and human cytomegaloviruses with implications for therapeutic development
|
Cell cycle control is fundamental in eukaryotic development . Several modeling efforts have been used to integrate the complex network of interacting molecular components involved in cell cycle dynamics . In this paper , we aimed at recovering the regulatory logic upstream of previously known components of cell cycle control , with the aim of understanding the mechanisms underlying the emergence of the cyclic behavior of such components . We focus on Arabidopsis thaliana , but given that many components of cell cycle regulation are conserved among eukaryotes , when experimental data for this system was not available , we considered experimental results from yeast and animal systems . We are proposing a Boolean gene regulatory network ( GRN ) that converges into only one robust limit cycle attractor that closely resembles the cyclic behavior of the key cell-cycle molecular components and other regulators considered here . We validate the model by comparing our in silico configurations with data from loss- and gain-of-function mutants , where the endocyclic behavior also was recovered . Additionally , we approximate a continuous model and recovered the temporal periodic expression profiles of the cell-cycle molecular components involved , thus suggesting that the single limit cycle attractor recovered with the Boolean model is not an artifact of its discrete and synchronous nature , but rather an emergent consequence of the inherent characteristics of the regulatory logic proposed here . This dynamical model , hence provides a novel theoretical framework to address cell cycle regulation in plants , and it can also be used to propose novel predictions regarding cell cycle regulation in other eukaryotes .
The eukaryotic cell cycle ( CC ) in multicellular organisms is regulated spatio-temporally to yield normal morphogenetic patterns . In plants , organogenesis occurs over the entire lifespan , thus CC arrest , reactivation , and cell differentiation , as well as endoreduplication should be dynamically controlled at different points in time and space [1] . Endoreduplication is a variation of the CC , in which cells increase their ploidy but do not divide . Normal morphogenesis thus depends on a tight molecular coordination among cell proliferation , cell differentiation , cell death and quiescence . These biological processes share common regulators which are influenced by environmental and developmental stimuli [1–3] . It would not be parsimonious to depend on different regulatory circuits to control such interlinked cellular processes , CC behaviors and responses . Thus we postulate that a common network is deployed in all of them . Such overall conserved CC network may then connect to different regulatory networks underlying cell differentiation in contrasting tissue types or to signal transduction pathways elicited under different conditions , and thus yield the emergence of contrasting cellular behaviors in terms of cycling rate , entrance to endocycle , differentiation , etc . Furthermore , the overall CC behaviors are widely conserved and robust among plants and animals . Hence , we aim at further investigating the collective behavior of the key upstream regulators and studied CC components to understand the mechanisms involved in the robustness of CC regulation under changing developmental stages and environmental conditions faced by plants along their life-cycles . Previous studies , that have shown the oscillatory behavior of several transcription factors , that had not been associated as direct regulators of the CC , support our proposed hypothesis [4] . We thus propose to uncovering the set of necessary and sufficient regulatory interactions underlying the core regulatory network of plant CC , including some key upstream transcriptional regulators . Computational tools are essential to understanding the collective and dynamical behavior of these components within the regulatory networks involved . As a means of uncovering the main topological and architectural traits of such networks , we propose to use Boolean formalisms that are simple and have proven to be useful and powerful to follow changes in the activity of regulators of complex networks in different organisms and biological processes [5 , 6] . Although the key CC components have been described in different organisms , the complexity and dynamic nature of the molecular interactions that are involved in CC regulation and the emergence of the cyclic behavior of the CC molecular components are not well understood yet . The use of systemic , dynamic and mathematical or computational approaches has been useful towards this already . Previous models have focused mainly on yeast and animal systems and have been useful to analyze many traits of CC behavior such as robustness , hysteresis , irreversibility and bistability [7–11] . The latter two properties have been validated with experimental data [12–14] . We herein summarize the main traits and components of the eukaryotic CC . The molecular CC regulators have been described and they are well conserved across distantly related organisms [15 , 16] . CC progression is regulated by Cyclin-Dependent Kinases ( CDKs ) [17] that associate with different cyclins to confer substrate specificity [18] . CDK-cyclin complexes trigger the transition from G1 ( Gap 1 ) to synthesis phase ( S phase ) in where the genome is duplicated , and from G2 ( Gap 2 ) to mitotic phase ( M phase ) for the delivery of the newly duplicated DNA to the two daughter cells [19] ( see for a review [17 , 20] ) . The CDK-cyclin activity also regulates the cell transit between G and S phases during the endoreduplication process [21 , 22] . Two CDKs ( CDKA and CDKB ) are involved in CC regulation . CDKA;1-CYCDs and CDKA;1-CYCA3 complexes regulate G1/S and S phase progression [23–25]; while CDKB-CYCA2 and CDKB-CYCBs regulate G2/M phase and M progression [26–28] . Thus CDK-cyclin activity is finely-tuned by phosphorylation , interactions with CDK inhibitors such as Kip-related proteins ( KRPs ) , and degradation of cyclins and KRPs by Skp1/Cullin/F-box ( SCF ) , as well as by the anaphase-promoting complex/Cyclosome ( APC/C ) [29–31] . Besides these components , plant CC machinery has a greater number of CC regulators than other eukaryotes and some of those components such as the CDKB are plant-specific . Several key transcriptional regulators participate in the G1/S and G2/M transitions [32] . The E2F/RBR pathway regulates G1/S transition by transcriptional modulation of many genes required for CC progression and DNA replication [33 , 34] . While E2Fa and E2Fb with their dimerization partner ( DP ) activate transcription of a subset of S phase genes , E2Fc-DP represses transcription [35] . The function of E2Fa and E2Fb is inhibited by their interaction with RBR [36]; in G1/S transition CDKA;1-CYCD-mediated RBR hyperphosphorylation , releases E2Fa/b-DP heterodimers allowing transcriptional activation of E2Fa and E2Fb targets . Simultaneously the E2Fc-DP transcriptional inhibitor is degraded [37] . Little is known about the regulation of G2/M transition in plants , however a class of conserved transcription factors belonging to the MYB family has been described , that seem to have key roles in CC regulation . MYB transcription factors have a prominent role during G2/M transition , by regulating , for example , CYCB1;1 which is determinant in triggering mitosis [38–43] . For the mitosis exit , APC/C mediates degradation of the mitotic cyclins as CYCB1;1 and CYCA2;3 , inactivating CDK-cyclin complexes . CCS52A2 , an activator subunit of APC/C , is transcriptionally inhibited by E2Fe [44] . Some previous models have recovered the limit cycle attractor as well for CC components [45–48] . A pioneer model of the CC focused on mitotic CDK-cyclin heterodimer and a cyclin protease oscillatory behavior [49] . On the other hand , Novak and Tyson incorporated additional nodes and interactions to model the G1/S and G2/M transitions of the S . pombe CC [50 , 51] . They also analyzed evolutionary roles of CC regulators [52] , mutant phenotypes [53] , stable steady states [7] and the role of cues such as cell size or pheromones in CC progression [54 , 55] . Additionally , comprehensive CC continuous models [45] and generic modules for eukaryotic CC regulation [56 , 57] have been proposed . In addition to continuous formalisms , CC models have used discrete approaches as Boolean models for yeast and mammalian systems [46–48 , 58–61] , and more recently , hybrid models for mammalian cells have been published [62] . Subsequently , time-delayed variables [63] and variables defining CC events [47 , 48] were incorporated . Time robustness was improved with specifications of the temporal order with which each component is activated [60] . Recent published reports on CC dynamics use steady state probability distributions and potential landscapes , and highlight the enormous potential of CC models to characterize normal and altered regulation of mammalian CC [64 , 65] . Yeast CC Boolean models with summatory thresholds [58 , 59] , incorporated self-degradation for proteins , but did not incorporate several negative regulators explicitly . In a later work [61] , nodes were kept active when the summatory effect of their regulators was greater than the activation threshold , which implies self-degradation of the protein , when such summatory is equal to or below the threshold . Fauré and Thieffry have transformed CC Boolean models , that use threshold functions , to models with a combinatorial scheme , and they have also presented a broader discussion about these two approaches to logical frameworks [66] . Two Boolean models of budding yeast CC and another one of mammalian CC recover cyclic attractors [46–48] . The mammalian CC model [46] also recovers a fixed-point attractor corresponding to G0 . In another study , Fauré and collaborators integrated three modules to yield a comprehensive model for the budding yeast CC GRN [47] . The components included variables to represent cellular growth , citokinesis , bud formation , DNA replication and the formation of the spindle . The yeast CC model by Irons also included variables of CC events ( e . g . bud formation or DNA replication ) as well as time delays [48] . In contrast to other eukaryotes , in Arabidopsis thaliana ( A . thaliana herein ) very few attempts have been made to integrate available experimental data on CC regulators using mechanistic models . Only a study that considers the G1/S transition has been proposed and contributed to show some additional conserved features of this CC control point among eukaryotes [67] . We integrated available experimental data on 29 A . thaliana regulatory interactions involved in CC progression into a Boolean discrete model , that recovers key properties of the observed plant CC . The regulatory network , that we put forward , also incorporates three uncovered interactions , based on animal systems ( E2Fb → SCF , CDKB1;1-CYCA2;3 ⊣ E2Fa , APC/C ⊣ SCF ) , as well as 16 interactions based on bioinformatic analyses . Therefore , the latter proposed interactions constitute new predictions that should be tested experimentally . The use of yeast or animal data is supported by the fact that main CC components or regulatory motifs are conserved among eukaryotes [16] . In our model , we include solely molecular components and avoid artificial self-degradation loops , which have been used for recovering the limit cycle attractor . We validated the model simulating loss- and gain-of-function lines , and hence demonstrate that the Boolean network robustly implements true dynamical features of the biological CC regulatory network under wild type and genetic alterations . Possible artifacts due to the discrete dynamical nature of the model used , and of its synchronous updating scheme , were discarded by comparing the Boolean model results to those of a continuous approximation model . The continuous model indeed recovers the robust limit cycle that mimics the dynamical behavior of CC components under a wide range of parameters tested . Finally , we provide novel predictions that can be tested against biological experimental measurements in future studies . The model put forward constitutes a first mechanistic and integrative explanation to A . thaliana CC .
We proposed a Boolean approach to integrate and study the qualitative complex logic of regulation of the molecular components underlying the CC dynamics . We formalized available experimental data on logical functions and tables of truth that rule how the state of a particular component is altered as a function of the states of all the components that regulate it . In a Boolean model each node state can be 0 , when the expression of a gene or other type of molecular component or complex of such components is unexpressed or “OFF” , or 1 when it is expressed , or “ON” . Nodes states are updated according to the function: Xi ( t+1 ) = Fi ( Xi1 ( t ) , Xi2 ( t ) , … , Xik ( t ) ) , where Xi ( t+1 ) is the state of Xi gene at time t+1 and Xi1 ( t ) , Xi2 ( t ) , … , Xik ( t ) is the set of its regulators at time t . The set of logical rules for all the network components defines the dynamics of the system . By applying the logical rules to all nodes for several iterations , the dynamics of the whole network can be followed until it reaches a steady state; a configuration or set of configurations that does not change any more or are visited in a cyclical manner , respectively . Such state is called an “attractor” . Single-point attractors only have one GRN configuration , or cyclic attractors with period n , which have n configurations that are visited indefinitely in the same order . In this paper we propose a GRN model that converges to a single limit cycle attractor that recovers the CC molecular components’ states of presence ( network configuration ) in a cyclic pattern that mimics the pattern observed for the molecular components included in the model along the different CC phase . A . thaliana CC Boolean model has the following assumptions: Nodes represent mRNA , proteins or protein complexes involved in CC phase transitions . Node state “ON” is for the presence of regulator , and “OFF” is for absence; in the latter case , it may also indicate instances in which a component may be present but non-functional due to a post-translational modification . The state of the RBR ( RETINOBLASTOMA-RELATED ) node corresponds to a 1 or “ON” when this protein is in its hypo-phosphorylated form and therefore is ready to inhibit E2F transcription factors . When a particular CDK is not specified , a cyclin can form a complex with CDKA;1 , a kinase that is always present because it is expressed in proliferative tissues [68] during the complete CC . E2Fa , E2Fb and E2Fc need dimerization partner proteins ( DPa or DPb ) for its DNA-binding . Given that DP expression does not change drastically in CC [69] , we assumed that the state of these heterodimers is given only by the presence of E2F factors . The Boolean logical functions integrate and formalize experimental data available mainly for the A . thaliana root apical meristem , however some data from leaves were considered , and we assumed that these are also valid for CC regulation in the root meristem . Also , data from other systems and data obtained by sequence promoter analysis were considered as indicated in each case [27 , 39 , 40 , 67 , 70–85] ( summarized in Table 1 ) . The dynamics of complex formation ( such as CDK-cyclin and KRP1 , or RBR and E2F factors ) are specified directly in the Boolean function of their target genes . For instance , the logic rule for E2Fb is E2Fa & ! RBR , indicating that E2Fb state is “ON” when it is transcriptionally activated by E2Fa free of RBR . All E2Fa targets also included in their logical rules RBR , as is shown in S1 Text . Then , the presence of KRP1 or RBR in a logical rule does not imply that they are regulators acting directly on the corresponding target . The updating scheme for the node states was synchronous . Most regulatory interactions and logical rules were obtained from the A . thaliana data [20 , 21 , 25–27 , 29 , 30 , 35 , 37 , 38 , 40 , 43 , 44 , 78–80 , 85–103] ( detailed in Table 2 ) . A . thaliana CC-dependent expression data for validation was obtained from: [72–74] . The consensus site used for MYB77 was CNGTTR , according to: [75 , 76] , while that for MYB3R4 was AACGG according to: [43] . The motifs were searched in the regulatory sequences of all network nodes using Pathmatch tool ( http://arabidopsis . org/cgi-bin/patmatch/nph-patmatch . pl ) of TAIR . Regulatory sequences in TAIR10 Loci Upstream Sequences-1000bp and TAIR10 5’ UTRs datasets were used . We used BoolNet [104] ( a library of R language [105] ) and Atalia ( Á . Chaos; http://web . ecologia . unam . mx/achaos/Atalia/atalia . htm ) to simulate the CC GRN dynamics and perform robustness , and mutant analyses . Systematic alterations in Boolean functions for robustness analyses were done with Atalia , while stochastic perturbations in random networks to compare attractor’s robustness were done with BoolNet . For random perturbations made in transitions between network configurations or in Boolean functions , the “bitflip” method was applied . To validate the GRN model proposed here , we used BoolNet and simulated loss- and gain-of-function mutations for each node , by skipping the node’s logical rule and setting the respective gene to “0” and “1” , respectively . For the continuous model , we followed [106 , 107] . In the continuous version of the model the rate of change for each xi node is represented by a differential equation that comprises production as well as decay rates: d x i d t = - e 0 . 5 h + e - h * ( ω i ) ( 1 - e 0 . 5 h ) * ( 1 + e - h * ( ω i - 0 . 5 ) ) - γ i x i ( 1 ) The parameter h determines the form of the curve; when h is very close to 0 , the curve becomes a straight line , while with values close to 100 , the curve approximates a step function . The parameter ωi is the continuous form of Fi ( Xi1 ( t ) , Xi2 ( t ) , … , Xik ( t ) ) used in the Boolean model , and γi is its degradation rate . Detailed information about the continuous model can be found in S2 Text .
The CC model proposed here integrates and synthesizes published data for A . thaliana CC components interactions , as well as some molecular data from other organisms ( mammal and yeast ) , that we propose as predictions for A . thaliana CC regulation , and assume to be conserved among all eukaryotes . The whole set of interactions and nodes included in the model and detailed in Tables 1 and 2 are shown in Fig 1 . Four types of molecular interactions can be distinguished: ( i ) transcriptional regulation , ( ii ) ubiquitination , ( iii ) phosphorylation and ( iv ) physical protein-protein interactions . Additionally , an in silico analysis of transcription factors and promoters was carried out , in order to further substantiate 16 predicted interactions in the GRN ( these are: E2Fb → MYB77; MYB77 → E2Fe , MYB3R1/4 , KRP1 , CYCB1;1 , CYCA2;3 , CDKB1;1 and CCS52A2; MYB3R1/4 → SCF , RBR , CDKB1;1 , CYCA2;3 , APC/C , KRP1 , E2Fc and MYB3R1/4 ) . The logical rules are available in S1 Text . Our results show that the nodes and interactions considered are sufficient to recover a single robust cyclic steady state , and thus the cyclic behavior of the components considered . Such behavior closely resembles the periodic patterns observed during actual CC progression , Fig 2 . The first two columns or network configurations match a G1 state , given that during the early G1 phase , the CDKA;1-CYCD3;1 complex is absent or inactive by the presence of KRP1 [92 , 93 , 108] . The CDKA;1-CYCD3;1 state is given only by the presence of CYCD3;1 since CDKA;1 is always expressed in proliferative cells [68] . To facilitate understanding , in Fig 2 the complex CDKA;1-CYCD3;1 is shown instead of only CYCD3;1 . The absence of mitotic cyclins ( CYCA2;3 and CYCB1;1 ) at this stage [28 , 38] , as well as the APC/C presence until the early G1 phase , which is needed for the mitosis exit , also coincides with experimental observations [44 , 109 , 110] . The presence of the RBR protein in G1-phase implies an inactive state of the E2F , as expected [33 , 111 , 112] . Then , the third column resembles G1/S transition , where the presence of CDKA;1-CYCD3;1 complex would be inducing RBR phosphorylation and its inactivation [32] . In the fourth configuration , the S-phase is represented by RBR inactivation and E2Fa/b transcriptional activation [113] . In the fifth and sixth configuration , E2Fc state returns to “ON” but RBR state is kept in “OFF” , which indicates that transcription driven by E2Fa and E2Fb can still happen . Indeed , the E2Fb factor appears from the fifth configuration and it is consistent with their function regulating the expression of genes needed to achieve the G2/M transition . In the sixth configuration , MYB77 is turned on , although in synchronization experiments it has been observed to be on until the beginning of mitosis [73] . During G2-phase the MYB transcription factors and KRP1 are expressed [31 , 73 , 93] , the former would maintain dimers of CDKA;1 and mitotic cyclins inactive; and together , this data is consistent with what is observed in the seventh configuration of the CC attractor . In the eighth column , KRP1 is lost because it was phosphorylated by CDKB1;1-CYCA2;3 , which is active in the G2/M transition and the onset of mitosis [27] . The phosphorylation of KRP1 drives its degradation and posterior activation of mitotic complexes such as CDKA;1-CYCB1;1 to trigger mitosis [21 , 78] ( configuration 9 and 10 in Fig 2 ) . The lack of APC/C at the onset of mitosis is determinant for the accumulation of the mitotic cyclins , but APC/C presence is necessary for the mitosis exit [110] , which occurs in the eleventh configuration of the attractor ( Fig 2 ) . Thus , our CC GRN model recovers a unique attractor of eleven network configurations ( Fig 2 ) , which shows a congruent cyclic behavior of its components with that observed experimentally . This result validates that the proposed set of restrictions converge to a single cyclic behavior , which is independent of the initial conditions . A further validation of the proposed CC model , would imply that the recovered cyclic attractor is robust to permanent alterations , as is the case for real CC behavior that is highly robust to external and internal perturbations [14 , 58 , 114 , 115] . To provide further validation for the proposed CC regulatory network , we performed robustness analyses of the attractor to four types of alterations in the logical functions of the model . First , we altered the output of each logical rule by systematically flipping one by one , each one of their bits . We found that 87 . 47% of the perturbed networks recovered the original attractor , while 1 . 77% of the altered networks maintained the original attractor and produced new ones ( see supplementary material S3 Text for details ) . In contrast , the remaining 10 . 76% of alterations reduced the number of network configurations of the original attractor . In the second robustness analysis , after calculating the transitions between one network configuration to the next one , one bit ( i . e . the state of a node ) of this next configuration is randomly chosen and its value changed . Then , the network is reconstructed and its attractors recovered again . This procedure was repeated 100 times , thus we found that in 88 . 2 ± 3 . 2 out of the 100 perturbations ( mean ± SD ) the original attractor was reached . These results suggest that the proposed GRN for A . thaliana CC is robust to alterations as expected and in coincidence with previous GRN models proposed for other developmental processes [116 , 117] . To confirm that the robustness recovered in these two types of analyses is a specific property of the network under study , we performed robustness analyses of randomly generated networks with similar structures ( same number of input interactors for the logical functions ) to the one proposed here for the A . thaliana CC regulatory network , and compared the above robustness analyses results to those recovered for equivalent analyses for the random networks . We generated 1000 random networks . Then , 100 copies of the random and of our network were done . In each copy we randomly flipped the value of one bit in one logical function ( to confirm the first robustness analysis ) , or in one next configuration ( for the second robustness analysis ) . When perturbations are made in logical functions , the A . thaliana CC GRN recovers its attractor in 68% of perturbations , while the median of percentage of cases in which such attractor was recovered in the random networks was only 18 . 55% ( mean 19 . 12%±13 . 86 SD , Fig 3A ) . The difference between the 68% of this latter analysis and the 87 . 47% of the first robustness analysis could be due to sampling error . If transitions between network configurations are perturbed , the median of original attractors recovered in random networks is 24 . 2% ( mean 24 . 6% ± 18 . 2 SD ) . In contrast , the original attractor of A . thaliana CC GRN was found in 88% of perturbed networks starting with that grounded on experimental data ( Fig 3B ) . These results confirm that the CC GRN proposed here is much more robust than randomly generated networks with similar topologies and suggests that its robustness is not due to overall structural properties of the network . Boolean models can produce cyclic dynamics as an artifact due to their discrete nature and the time delays implied . To address this issue we approximated the Boolean model to a continuous system of differential equations following [106 , 107 , 118 , 119] . To recover steady states of such continuous system , the continuous versions of the GRN were evaluated for 1000 different randomly picked initial conditions ( See S2 Text ) . In all cases and independently of the methodology ( i . e . [106 , 107] or [118 , 119] ) , we recovered the same limit cycle steady state . In the continuous model , key cyclins for the main phase transitions , CYCD3;1 and CYCB1;1 , have an oscillatory behavior that is not attenuated with time ( Fig 4 ) . Importantly , this result is robust to changes in the decay rates or alterations of the h parameter that affects the shape of activation function ( see details in S2 Text ) ; the limit cycle was recovered in 92 . 86% of the cases . The results of the continuous model corroborate that the limit cycle attractor recovered by the Boolean version , is not due to an artifact associated to the discrete and synchronous nature of the Boolean model , but is rather an emergent property of the underlying network architecture and topology . In addition , the recovery of the cyclic behavior of the continuous model constitutes a further robustness test for the Boolean model . Previous studies have also tested asynchronous updating schemes [46] . In this study we have used a continuous form of the model to discard that the recovered cyclic attractor is due to an artifact owing to the discrete and synchronous nature of the model used . Future studies could approach analyses of asynchronous behavior of the model by devising some priority classes distinguishing fast and slow processes , and thus refining the asynchronous attractor , under a plausible updating scheme . On the other hand , biological time delays may be involved in CC progression , but they are not enough for irreversibility . The CC unidirectionality has been proposed to be a consequence of system-level regulation [120] , here we hypothesize that the ordered transitions of A . thaliana CC are an emergent property of network architecture and dynamics . An additional validation analysis for the proposed A . thaliana CC model implies simulating loss- and gain-of-function mutations and comparing the recovered attractors with the expression profiles documented experimentally for each mutant tested . We simulated mutants by fixing the corresponding node to 0 or 1 in loss- and gain-of-functions mutations , respectively . The recovered altered configurations are summarized in S4 Text , and in Table 3 as well as in Table 4 for gain- and loss-of-function mutants , respectively . The simulated mutant attractors are coherent with experimental data in most cases [2 , 21 , 23 , 30 , 35 , 37 , 43 , 44 , 76 , 79 , 80 , 88 , 90–93 , 103 , 108 , 109 , 111 , 113 , 114 , 121–129] . In Fig 5 we show a representative example of attractors recovered by simulations of CDKB1;1 and KRP1 loss-of-function and APC/C and E2Fa gain-of-function mutants . It is noteworthy that several simulated mutants , such as mitotic cyclins or B-type CDK loss-of-function , converge to a cyclic attractor that corresponds to the configuration observed under an endoreduplicative cycle ( e . g . Fig 5A ) . In such attractors , endoreduplication inductors , such as APC/C , KRP1 and E2Fc [37 , 78 , 130] are present , at least in some network configurations ( Fig 5A , 5C and 5D-right ) . Another outstanding feature of these mutant attractors is that , although mitotic CDK-cyclin complex may be present , it is inhibited by KRP1 , therefore there is no CDK-cyclin activity to trigger the onset of mitosis . These data are coincident with the reported regulation during the onset of endoreduplication [21] . In the attractors where E2Fa coincides with alternating states of RBR , it suggests that DNA replication may occur ( Fig 5 ) . Likely due to plant redundancy , some mutations do not produce an obvious impaired phenotype . Such is the case of KRP1 loss-of-function , in which loss-of-function simulation , a cyclic attractor identical to the original one is recovered , as is expected ( see Table 4 ) , because such mutants do not show an evident altered CC behavior ( Fig 5B ) [93] . Interestingly , the simulation of a constitutively active APC/C also converges to a single cyclic attractor , which corresponds to an endoreduplication cycle , since it has Gap and S phases , but lacks an M-phase configuration . This coincides with the experimental observation that the overexpression of one of the APC/C subunits ( CCS52A ) promotes entry to an endocycle [44] ( see Table 3 ) . Another interesting example is the gain-of-function mutation of E2Fa that yields two cyclic attractors , one corresponding to the normal CC cycle and the other one to an endocycle ( Table 3 ) . It has been shown that this gene is required for both processes [111] that are apparently exclusive , although in both processes the DNA replication occurs and among E2Fa targets there are genes required for S-phase . Thus our model suggests that the regulation of E2Fa at the end of G2 phase is decisive for CC exit and transition to endoreduplication . In this E2Fa gain-of-function simulation , we found an inconsistency with APC/C because this E3 ubiquitin ligase is decisive for endoreduplication , while in the simulated attractor is only present in one network configuration ( Fig 5D-right ) . Such behavior observed in the endoreduplication attractor for E2Fa gain-of-function leads to unstable activity in the CDK-cyclin complex ( Fig 5D ) , thus suggesting that the increase in APC/C is required for endoreduplication entry as well as its progression . In the attractor of the simulated APC/C gain-of-function , the states of the CYCD3;1 , SCF , E2Fb , E2Fc and MYB nodes are more stable than in endoreduplication attractors of CDKB1;1 loss-of-function or E2Fa gain-of-function , where E2Fb , E2Fc and MYB factors expression states alternate between “ON” and “OFF” ( Fig 5 ) . We highlight APC/C gain-of-function simulations , as it provides a possible mechanism for plant hormones action over the CC machinery and , thus how such key morphogens regulate cell proliferation patterns . Recently , Takahashi and collaborators reported a direct connection between cytokinins and CC machinery in A . thaliana root [131] . The authors showed that ARR2 , a transcriptional factor of cytokinins signaling , induces expression of APC/C activator protein CCS52A1 . Our simulated APC/C gain-of-function is congruent with that observation , since it reproduces the configuration attained by a cell entering an endocycle when APC/C activity is enhanced ( Fig 5C ) , as it happens at the elongation zone of A . thaliana root . Therefore , our model is able to recover the attractors of loss- and gain-of-function mutant phenotypes reported experimentally , and it thus provides a mechanistic explanation for observed patterns of expression in both normal CC and during endoreduplication cycles or endocycle . We test if the CC GRN recovers the periodic patterns observed in synchronization experiments of A . thaliana CC molecular components . Interestingly , the E2Fc repressor and KRP1 are regulators that have two short lapses of expression in the attractor recovered in the continuous model ( Fig 6 ) , and experimentally they also show two peaks of expression when synchronized with aphidicolin [74] . In such synchronization experiments , the expression of E2Fc increases from late S to middle G2 , but then it decreases dramatically in late G2 . In the model , E2Fc appears from S to G2 phase , and then a second increment of E2Fc expression in G2/M is observed . The latter correspondence is a further validation of the CC GRN model proposed here . Furthermore , synchronization experiments using sucrose have shown that KRP1 is expressed previous to G1/S transition and before mitosis [132] , in a similar way that occurs in the model . More recently it has been proposed that KRP1 has a role during G1/S and G2/M transitions [93]; the latter should be important for endoreduplication control [78] . Once again , such roles and expression profiles are consistent with the recovered active state of KRP1 in our model . In contrast with the consistent behaviors of E2Fc and KRP1 components to recovered results with our model , E2Fe results do not coincide with previous observations . In our model this E2F factor presents only one peak from S to early M phase , but according to synchronization experiments [69] , E2Fe has two peaks of expression . One of its peaks is due to regulation by other E2F family factors during S phase , while the G2/M peak could be due to MSA elements . Indeed , when the regulatory motifs for E2F binding are deleted from E2Fe , it can still be expressed although at lower levels [96] , suggesting that additional transcription factors regulate its expression . Such factors could belong to the MYB family as suggested for the A . thaliana CC GRN proposed here .
Our proposed GRN model suggests some predictions regarding the regulation of certain CC components in A . thaliana . Such predictions can be classified into two types . The first type pertains to those recovered by in silico promoter analysis . The predictions of the second type were inferred from data of other eukaryotes , because they seem to imply conserved components and some evidence from A . thaliana suggested that these interactions are part of the CC GRN in A . thaliana . Three interactions belong to the second type , E2Fb → SCF , CDKB1;1-CYCA2;3 ⊣ E2Fa and APC/C ⊣ SCF ( see Table 1 for a synthesis of hypothetical interactions ) . Although some evidence supports the idea that these interactions could exist in A . thaliana , they should be corroborated with additional experimental examination . Our model provides a dynamic explanation to the cyclic behavior of certain transcription factors and predicts a novel interaction for E2F and MYB regulators; they connect waves of periodic expression that seem to be key for the robust limit cycle attractor that characterizes CC behavior . Interestingly , previous studies have shown that such periodic transcription can be maintained even in the absence of S-phase and mitotic cyclins [4] , which underpin the role of a transcription factor network oscillator for the correct CC progression [137] . A regulatory interaction between E2F and MYB factors ( or among the equivalent regulators ) may be conserved among other eukaryotes ( e . g . mammals and yeast ) , but there is no experimental support yet for it in A . thaliana . After looking for the same direct evidence in A . thaliana and not finding it , we thought about an alternative regulatory mechanism that consists in transcription factors acting between E2F and MYB . Hence , we decided to analyze the important transcription factor families known so far , to find out if one of their members could be mediating the regulation between E2F and MYB . The TCP ( for Teosinte branched 1 , Cycloidea , PCF ) and the MYB family were chosen because they have been reported to be involved in CC regulation [42] . Based on their gene expression patterns and promoter sequence analysis , MYB77 was our best candidate: it is expressed at the beginning of M phase , and could be regulated by E2F and regulator of MYB ( see Table 1 ) . A second possibility might be that several tissue-specific transcription factors are involved in E2F-MYB genetic regulation ( e . g . GL3 , MYB88 , SHR/SCR [17] , MYB59 [138] or even members of the MADS box gene family could be implied ) . Indeed , we have recently documented that a MADS-box gene , XAL1 , encodes a transcription factor that regulates several CC components ( García-Cruz et al . , in preparation ) . Differences among eukaryotic CCs allow us to recognize or characterize alternative mechanisms for the regulation of CC . The first difference between GRN of A . thaliana CC and that of other eukaryotes , concerns the number of duplicates of some key regulators . A . thaliana has up to ten copies of some of the genes that encode for CC regulators ( e . g . families of cyclins or CDK ) , while yeast , mammals or the algae Ostreococcus tauri , have much fewer duplicates [20 , 139–141] . The only exception concerns the homologues of Retinoblastoma protein , of which there are three members in humans and mouse , and only one copy in A . thaliana [127] . Future models should address the explicit role of CC duplicated components in the plastic response of plant development to environmental conditions . Being sessile , such developmental adjustments , as plants grow under varying environments , are expected to be more important , complex and dynamic than in motile yeast and animals . One possibility is that different members of the same gene family are linked to different transduction pathways of signals that modulate CC dynamics . The second difference among A . thaliana and other CC was regarding the transcriptional regulation throughout the GRN underlying it . For instance , S . cerevisiae does not have RBR or E2F homologues , but instead has Whi5 , Swi4 , 6 and Mbp1 proteins which perform equivalent regulatory functions to the former CC components [142 , 143] . S . cerevisiae does not have any MYB transcription factors but it presents other transcriptional regulators , such as Fkh1/2 , Ndd1 and Mcm1 [142 , 144 , 145] , which regulate the G2/M transition in a similar way to MYBs in mammals . Contrary to the conservation in G1/S transition [15 , 67] , molecular components controlling G2/M transition seem to vary among different eukaryotes . It seems that molecules such as WEE1 kinase and CDC25 phosphatase are not conserved . In A . thaliana , CDC25-like has phosphatase and arsenate-reductase functions [146] , while A . thaliana WEE1 phosphorylates monomeric CDKA;1 in vitro [147] , and Nicotiana tabacum WEE1 inhibits CDK activity in vitro [148] . However the lack of any obvious mutant phenotype of CDC25 or WEE1 loss-of-function mutants predicts that these genes are not involved in the regulation of a normal CC . Additionally , although WEE1 has a role during DNA damage [146 , 149] , does not seem to have a CDKA;1 recognition domain [150] . CDC25-like does not have the required sites for CDKA;1 recognition [150] . In summary , the positive regulatory feedback between CDKA;1 and CDC25-like , as well as the mutual-inhibitory feedback loop between CDKA;1 and WEE1 , seem not to be conserved in A . thaliana . Given all that evidence for G2/M regulation , we integrated the regulatory interactions between stoichiometric CDK inhibitor ( KRP1 ) , B-type plant specific CDK and MYB transcriptional factors . It is not surprising that there are clear differences between plant G2 phase regulation and that of other organisms , because variations in this control point could define cell fate . Although differences among the A . thaliana CC GRN uncovered here and that of yeasts and animals have now become clear , we think that the basic regulatory CC module reported here , will be a useful framework to incorporate and discover new components of the CC GRNs in plants and also in other eukaryotes . Despite the fact that our CC GRN model recovers observed CC stage configurations and their canonical pattern of temporal transitions , it did not recover an alternative attractor that corresponds to the endocycle . We hypothesize that the same multi-stable GRN underlies both states , and additional components yet to be connected to the CC GRN will ensure a cyclic attractor corresponding to the complete CC , and another one with shorter period corresponding to the endocycle . In its present form , our model suggests that CYCD3;1 function , which has been associated with the proliferative state [108] and with a delay in the endocycle onset [23] , is important to enter the endocycle . Besides , it also has been reported that CYCD3;1 plays a role in G1/S transition [121] and regulates RBR protein during DNA replication [89] . Furthermore , the endoreduplication attractor obtained in some of our mutant simulations ( e . g . Fig 5A , 5C and 5D-right ) also supports the role of CYCD3;1 in entering an endocycle . The GRN model of A . thaliana CC could help to identify physiological or developmental interactions involved in the tight relationship between proliferation and differentiation observed during different stages of development [1 , 88 , 108 , 109 , 126] . Previous to cell division , the cell senses its intracellular and environmental conditions to arrest or promote CC progress . Such cues directly affect the CC machinery , which does not depend on a master or central regulator . CC control is the result of a network formed by feedback and feedforward loops between complexes of CDK-cyclin and its regulators . It is not evident how complex dynamical processes such as CC progression emerge from simple interactions among components acting simultaneously . The proposed CC GRN will be very helpful to study how cell proliferation/differentiation decisions and balance keeps a suitable spatio-temporal control of CC during plant growth and development .
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In multicellular organisms , cells undergo a cyclic behavior of DNA duplication and delivery of a copy to daughter cells during cell division . In each of the main cell-cycle ( CC ) stages different sets of proteins are active and genes are expressed . Understanding how such cycling cellular behavior emerges and is robustly maintained in the face of changing developmental and environmental conditions , remains a fundamental challenge of biology . The molecular components that cycle through DNA duplication and citokinesis are interconnected in a complex regulatory network . Several models of such network have been proposed , although the regulatory network that robustly recovers a limit-cycle steady state that resembles the behavior of CC molecular components has been recovered only in a few cases , and no comprehensive model exists for plants . In this paper we used the plant Arabidopsis thaliana , as a study system to propose a core regulatory network to recover a cyclic attractor that mimics the oscillatory behavior of the key CC components . Our analyses show that the proposed GRN model is robust to transient alterations , and is validated with the loss- and gain-of-function mutants of the CC components . The interactions proposed for Arabidopsis thaliana CC can inspire predictions for further uncovering regulatory motifs in the CC of other organisms including human .
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[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
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A Dynamic Gene Regulatory Network Model That Recovers the Cyclic Behavior of Arabidopsis thaliana Cell Cycle
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Alternative splicing amplifies the information content of the genome , creating multiple mRNA isoforms from single genes . The evolutionarily conserved splicing activator Tra2β ( Sfrs10 ) is essential for mouse embryogenesis and implicated in spermatogenesis . Here we find that Tra2β is up-regulated as the mitotic stem cell containing population of male germ cells differentiate into meiotic and post-meiotic cells . Using CLIP coupled to deep sequencing , we found that Tra2β binds a high frequency of exons and identified specific G/A rich motifs as frequent targets . Significantly , for the first time we have analysed the splicing effect of Sfrs10 depletion in vivo by generating a conditional neuronal-specific Sfrs10 knock-out mouse ( Sfrs10fl/fl; Nestin-Cretg/+ ) . This mouse has defects in brain development and allowed correlation of genuine physiologically Tra2β regulated exons . These belonged to a novel class which were longer than average size and importantly needed multiple cooperative Tra2β binding sites for efficient splicing activation , thus explaining the observed splicing defects in the knockout mice . Regulated exons included a cassette exon which produces a meiotic isoform of the Nasp histone chaperone that helps monitor DNA double-strand breaks . We also found a previously uncharacterised poison exon identifying a new pathway of feedback control between vertebrate Tra2 proteins . Both Nasp-T and the Tra2a poison exon are evolutionarily conserved , suggesting they might control fundamental developmental processes . Tra2β protein isoforms lacking the RRM were able to activate specific target exons indicating an additional functional role as a splicing co-activator . Significantly the N-terminal RS1 domain conserved between flies and humans was essential for the splicing activator function of Tra2β . Versions of Tra2β lacking this N-terminal RS1 domain potently repressed the same target exons activated by full-length Tra2β protein .
Almost all transcripts from genes encoding multiple exons are alternatively spliced , and correct patterns of alternative splicing are important for health and normal development [1] , [2] , [3] . Alternative splicing introduces new coding information into mRNAs , thereby increasing genome capacity to encode an expanded number of mRNAs and proteins from a finite number of genes [3] . Poison exons which introduce premature stop codons can also be alternatively spliced to target mRNAs for degradation through Nonsense Mediated Decay ( NMD ) [4] , [5] , [6] , [7] , . Alternative splice events are controlled in part by trans- acting RNA binding proteins which help establish patterns of alternative splicing through deciphering a splicing code embedded within the pre-mRNA sequence [9] , [10] , [11] . Tra2 proteins bind directly to target exons thereby activating splicing inclusion [12] , and have a modular organisation comprising a single central RNA recognition motif ( RRM ) which binds to target RNA sequences , flanked by arginine-serine rich ( RS1 and RS2 ) domains [13] , [14] . The N-terminal Tra2 RS1 domain is longer and contains more RS dipeptides than RS2 . The reason for this unique modular organisation is unknown , but is conserved in vertebrate and invertebrate Tra2 proteins and different from the classical SR super-family which have a single C-terminal RS domain [15] . Also unlike classical SR proteins , Tra2 proteins do not restore splicing activity to S100 extracts [12] . A single Tra2 protein is conserved in fruit flies , where it is essential for spermatogenesis and sex determination [16] . There are two mammalian Tra2 proteins called Tra2α ( encoded by the Tra2a gene on mouse chromosome 6 ) and Tra2β ( encoded by the Sfrs10 gene on mouse chromosome 16 ) which share 63% amino acid identity and similar RNA binding specificities [12] . NMR analyses have recently shown that the optimal core RNA target sequence for binding full length Tra2β protein is an AGAA motif , with each of the nucleotide residues being specifically recognized by the Tra2β RRM [17] , [18] . A key priority to understand the biological functions of Tra2β is to identify target RNAs which are functionally regulated within animal cells , and associated pathways of gene activity . Mice with ubiquitous deficiency of the Sfrs10 gene die at around 7 . 5 to 8 . 5 days of gestation [19] . Splicing of some Tra2β candidate target exons have been investigated using minigenes , but recently a well known regulated splice target exon ( SMN2 exon 7 ) was found to have the same splicing pattern within wild type mice and Smn−/−; SMN2tg/tg; Sfrs10−/− mouse cells which do not express Tra2β protein [19] . These data suggest Tra2β is not the key protein regulating physiological inclusion of SMN2 exon 7 within animal cells . The Sfrs10 gene itself is alternatively spliced to five mRNA isoforms encoding at least 2 protein isoforms [20] , [21] , [22] . The major isoform encodes full length Tra2β protein . Full length Tra2β protein regulates its own levels through activating splicing inclusion of a poison exon ( exon 2 ) into a second mRNA isoform , preventing protein translation ( Figure 1A ) [22] . A third mRNA isoform encodes just the C-terminus of the protein ( containing the RRM , glycine linker and the RS2 domain ) giving rise to the protein isoform Tra2beta-3 or Tra2βΔRS1 [20] , [21] , [22] . No distinct function has been assigned to the Tra2βΔRS1 isoform compared to full length Tra2β [17] , although this isoform is conserved in invertebrates so likely important . Tra2βΔRS1 expression is tissue specific in both flies and mammals , and is up-regulated by expression of Clk kinases and neural stimulation [20] , [21] , [22] , [23] . Male germ cell development is one of the few developmental pathways to continue into the adult . In the fly testis , Tra2 regulates splicing of Exuperentia and Att pre-mRNAs in male germ cells , as well as its own alternative splicing pathway [24] , [25] . Tra2β has been implicated in mammalian spermatogenesis through interaction with RBMY protein which is genetically deleted in some infertile men [26] , [27] , and regulates the splicing of the human testis-specific HIPK3-T exon through a switch-like mechanism [28] , [29] . Given its important role in Drosophila spermatogenesis and established interactions with proteins implicated in human male fertility we predicted that Tra2β-regulated alternative splicing events would control fundamental pathways in mammalian male germ cell development . We have tested this prediction here using a transcriptome-wide approach .
We analysed the expression of Sfrs10 mRNA in different adult mouse ( Mus musculus ) tissues by RT-PCR using primers in exons 1 and 4 . An RT-PCR product derived from Sfrs10 mRNA in which exons 1 and 3 were directly spliced ( skipping poison exon 2 ) was seen in every tissue indicating the Sfrs10 gene is ubiquitously expressed ( Figure 1A and 1B ) . A larger Sfrs10 RT-PCR product made from mRNAs including poison exon 2 was detected at high levels in just two tissues , testis and muscle , indicating that expression of Tra2β is particularly tightly controlled in these tissues [22] . Similar levels of expression of Hprt mRNA were observed in each tissue by multiplex RT-PCR . A polyclonal antiserum raised to Tra2β protein identified a single endogenous protein of around 40 KDa in both transfected and untransfected HEK293 cells corresponding in size to endogenous Tra2β ( Figure 1C ) . A Tra2β-GFP fusion protein was additionally detected within transfected cells , but no cross-reaction was detected with a Tra2α-GFP fusion indicating high specificity of the antiserum . We were also able to detect a GFP-fusion protein containing Tra2βΔRS1 , but not endogenous Tra2βΔRS1 protein suggesting that this particular isoform is expressed at low levels in these cells . Further probing of the same filter indicated that all the GFP fusion proteins were expressed at similar levels ( Figure 1C , lower panel ) . We used indirect immunohistochemistry to determine the cell type distribution of full length Tra2β in the adult testis ( Figure 1D and 1E ) . Tra2β was detected as a nuclear protein ( Figure 1E upper panel ) , and all staining was prevented by pre-incubation of the antisera with the immunising peptide ( Figure 1E lower panel ) . Tra2β was most highly expressed during mouse male germ cell development at the meiotic stage in spermatocytes ( abbreviated Spc ) , and afterwards in round spermatids ( abbreviated Rtd ) . Less intense Tra2β staining was detected within spermatogonia which contain the mitotically active stem cell population . No immunostaining was detected in elongating spermatids ( abbreviated Spd ) . This regulated expression pattern predicts that Tra2β might play a role in regulating meiotic and post-meiotic exon inclusion during male germ cell development . Outside the germline , Tra2β protein expression was detected in Sertoli cells ( indicated by red arrows on Figure 1E ) . To identify endogenous cellular RNA targets for Tra2β we carried out high throughput sequencing cross linking immunoprecipitation ( HITS-CLIP ) [30] . Adult mouse testis cells were used according to published procedures ( see methods for details ) to retrieve an average tag length of 40 nucleotides . These recovered CLIP tags correspond to specific RNA sequences bound and subsequently cross-linked to endogenous Tra2β protein within the testis . To identify frequent physiological Tra2β binding sites in mouse testis we searched for frequently occurring 6-mers in the retrieved CLIP tags , and normalised these to their background occurrence in the mouse genome and transcriptome using custom-written Python scripts ( Table S1 and Table S2 ) . Each of the most frequently recovered 6-mers was significantly enriched in the CLIP dataset compared to their representation in the mouse genome or mouse testis transcriptome . Strikingly , purine-rich sequences were preferentially recovered in our CLIP tags . In fact , 14 hexamers out of the top 30 recovered genome corrected hexamers in Table S1 have only purine residues , and 13 have only one pyrimidine . More specifically and consistent with the known RNA binding site for Tra2β [17] , [18] , GAA-containing sequences were frequently observed . The distribution of GAA-containing 6-mers in the overall population of CLIP tags was visualised by plotting the genomic ranking of 6-mer recovery ( X axis ) against their representation in the CLIP population ( Y axis ) ( Figure 2A: GAA-containing 6-mers are shown in red , with all other 6-mer sequences in blue ) . Of the 30 most frequently recovered 6-mers , 27 had a core GAA motif and the other 3 an AGA motif . The most frequent 6-mer ( the AGAAGA motif , 10° on the X axis of Figure 2A -equivalent to 1 ) was found in almost 20% of the recovered CLIP tags . The ten most frequently recovered 6-mers were found in more than 40% of the CLIP tags . Next we aligned full length CLIP tags to generate a transcriptome-wide consensus sequence . We anchored this line-up between CLIP tags using the trinucleotide GAA from the core binding motif which is essential for efficient RNA protein interactions [17] ( Figure 2B ) . Within this consensus alignment , an A residue followed by a T residue ( and less frequently a G residue ) was usually found upstream of the GAA motif ( position 1 in Figure 2B ) , consistent with reported in vitro RNA-protein binding data between the RRM of Tra2β and synthetic oligonucleotides [17] . Furthermore , a G residue ( and less frequently an A residue ) was preferentially selected at the position downstream of the GAA motif ( position 5 ) , and an A at the next nucleotide position downstream ( position 6 ) . This results in an extended AGAAGA consensus , in agreement with the sequence of the 3 top hexamers . Interestingly , when only a GAA triplet but not an AGAA core is present within a CLIP tag , 89% of the tags have a G residue immediately downstream ( GAAG ) , consistent with the important contribution of the G5 residue for efficient binding of Tra2β to its natural RNA targets . No further strong sequence bias was noticed in the sequences upstream and downstream of the AGAAGA hexamer . A similar consensus was obtained previously for SRSF1 protein [31] . However since SRSF1 has 2 RRMs with different RNA binding capacities and only one RS domain , it is most likely that its global specificity of RNA recognition and binding are broader than that for Tra2β and also depends on other ESEs within its individual target exons . To identify specific endogenous target transcripts CLIP tags were mapped onto the mouse genome sequence ( a full bed file of Tra2β CLIP tags is provided as Dataset S1 ) [32] . Overall , the distribution of Tra2β CLIP tags was predominantly intragenic: Around 69% of Tra2β binding sites were located within protein coding genes , even though genes contribute just 25% of the genome ( Figure 2C ) . Network analyses indicated the main functional properties associated withTra2β target transcripts were post-translational modification , the cell cycle , gene expression , RNA post-transcriptional modification and cell death ( Figure 2D ) . Top physiological systems associated with Tra2β target transcripts included reproductive system and nervous system development , and there was significant enrichment of signalling pathways in the top detected pathways ( Table S3 ) . Most intragenic CLIP tags mapped to transcripts in the sense orientation , but 7 . 5% of retrieved CLIP tags were antisense to known annotated genes . Only 1 . 3% of the mouse genome encodes exons ( 5′ UTR , ORF and 3′ UTR , based on mm9 annotation version ensembl59 ) . For Tra2β some 29% of Tra2β CLIP tags mapped within exons of protein coding genes ( Figure 2C ) which indicates the presence of numerous Tra2β-specific target exons . Similar CLIP-based transcriptome-wide analyses found that the SR protein SRSF1 also frequently binds to exonic sequences , while Nova and PTB target sites are mainly intronic in distribution [30] , [31] , [33] . Non-exonic Tra2β binding sites were found within deep intronic regions , within locations annotated as intergenic and within noncoding RNAs ( ncRNAs ) [34] . Within ncRNAs Tra2β binding sites were found within the small subunit rRNA ( also identified as a binding site for SRSF1 [31] ) and 7SK RNA . There were also Tra2β binding sites within the ncRNA Malat1 which is known to be localised in nuclear splicing speckles enriched in pre-mRNA splicing components ( Malat1 is also bound by SRSF1 [31] ) , and within microRNAs . These identified targets suggest that Tra2β might in fact be a somewhat multifunctional post-transcriptional regulator . Similarly diverse classes of target RNA ( including both coding and ncRNAs ) have been identified for a number of other RNA binding proteins by HITS-CLIP [30] , [31] , [33] , [35] , [36] . Tra2β bound to both constitutive and alternative exons and also to each different class of alternative events annotated on the mouse genome browser at UCSC . In particular , Tra2β binding sites mapped preferentially to cassette exons ( this is also the most frequent class of alternative splicing event in metazoans [37] ) ( Figure 2E ) . To test for splicing regulation of these identified target exons by Tra2β , a panel of seven cassette exons with high numbers of mapped CLIP tags , together with flanking intronic sequences , were cloned into an exon trap vector ( see Materials and Methods ) . The resulting minigenes were then transfected into HEK293 cells with expression constructs encoding either GFP , Tra2β-GFP , or GFP-tagged Tra2β deletion variants . Western blots indicated each of the GFP-fusion proteins were efficiently expressed in HEK293 cells ( Figure 3A ) , although the fusion protein without the RS1 domain was expressed at higher levels . Splicing patterns of pre-mRNAs were analysed using RT-PCR . We observed particularly strong splicing activation of a poison exon in the Tra2a gene in response to co-expression of Tra2β-GFP ( Figure 3B ) . Ectopic expression of both Tra2α and Tra2β were equally able to activate splicing of the Tra2a poison exon indicating that these two proteins are functionally equivalent in this assay ( Figure 3B , lanes 2 and 3 ) . No splicing activation of the Tra2a poison exon was observed with either Tra2βΔRRM-GFP or GFP alone , indicating a requirement for RRM-dependent binding by full length Tra2β proteins for splicing activation ( Figure 3B , lanes 1 and 4 ) . Full length Tra2β also mediated statistically significant splicing activation of a cassette exon annotated Nasp-T in the Nasp gene . Surprisingly , equally strong and highly statistically significant Nasp-T exon splicing activation was also observed in response to ectopic expression of Tra2βΔRRM-GFP protein ( Figure 3C , lanes 2 and 3 ) . Because of the high levels of splicing inclusion observed for the wild type Nasp-T exon at endogenous cellular concentrations of Tra2β ( Figure 3C ) , we also repeated these experiments using a mutated exon which is less efficiently spliced ( mutant M3+M4 –see below ) and again observed significant splicing activation by Tra2βΔRRM-GFP protein ( Figure 3D –in this case the effect of Tra2βΔRRM-GFP is clearer because of the lower levels of splicing inclusion of this mutated exon at endogenous cellular Tra2β protein concentrations ) . Together these data indicate that for some exons including Nasp-T , Tra2β can activate splicing through RRM independent interactions as well as being a direct splicing activator as previously described . The Sfrs10 locus encodes a second endogenous protein isoform called Tra2βΔRS1 [20] , [21] , [22] which lacks the RS1 domain . Surprisingly , after co-expression of a Tra2β-GFPΔRS1 protein isoform we observed significant splicing repression of both the Tra2a poison exon and Nasp-T exon ( Figure 3B–3D ) indicating that this protein isoform behaves as a potent splicing repressor , and of the same target exons recognised by full length Tra2β protein . Two further exons , Creb exon 2 and Lin28b exon 2 , did not detectably respond to ectopic expression of full length Tra2β or any of its derivatives ( Figure 3G and 3H ) and were already included at high levels in the absence of ectopically expressed Tra2β protein . No strong splicing repression of Creb exon 2 and Lin28b exon 2 was observed on co-expression of Tra2β-GFPΔRS1 . Full length Tra2β weakly but significantly activated splicing of two other target exons , Krba1 exon 9 and Pank2 exon 3 ( Figure 3E and 3F ) and splicing of these exons was also not significantly repressed by Tra2β-GFPΔRS1 ( compare lanes 1 and 3: notice slight repression which was not statistically significant ) . We also looked at two other exons which are spliced in the testis and which we independently characterised as being regulated by Tra2β . Minigene experiments indicated both the Crebγ and Fabp9 exons [38] , [39] were moderately activated by Tra2β , and were also co-ordinately moderately repressed by the Tra2βΔRS1 isoform ( Figure 3I and 3J , lanes 1 and 4 ) . Taken together these data are consistent with full length Tra2β protein activating specific target exons , and the Tra2βΔRS1 protein isoform specifically repressing exons which are at least moderately to strongly activated by full length Tra2β , but not acting as a general repressor of cellular splicing . We carried out further in silico and molecular analyses to correlate Tra2β binding with the observed patterns of exon regulation . We firstly looked for the occurrence of over-represented transcriptome-wide enriched 6-mer sequences ( k-mers ) [40] to identify putative Tra2β binding sites in the analysed target exons in silico ( Figure S1 ) . Both the Nasp-T and Tra2a poison exon had a high predicted content of 6-mers corresponding to putative Tra2β binding sites and consistent with their strong Tra2β regulation observed in vitro . We then directly measured Tra2β binding affinities using Electromobility Shift Assays ( EMSAs ) ( Figure 4: the positions of predicted binding sites within the RNA probes are shaded as in Table S1 . Notice the dark green corresponds to the top 5 most frequently recovered 6-mers , and lighter shades of green correspond to less frequently recovered 6-mers ) . Both Nasp-T and Tra2a poison exon probes were very efficiently shifted by even very low concentrations of Tra2β protein ( the Nasp-T probe was shifted into the well by only 50 ng of added Tra2β protein indicating formation of very large Tra2β protein-RNA complexes , and increasing molecular weight Tra2a RNA-protein complexes were observed with increasing concentrations of full length Tra2β protein ) . A series of increased molecular weight complexes also formed on the Crebγ exon RNA probe ( corresponding exon regulated in cellulo by Tra2β ) and on the Krba1 RNA probe ( weakly responsive in cellulo to Tra2β splicing activation ) . A single higher molecular weight complex formed on the Lin28 probe ( exon splicing not activated in vitro by Tra2β , and contains a single predicted Tra2β binding site ) . Much less efficient binding was observed for the non Tra2β-responsive Creb exon 2 ( which formed a single molecular weight complex only with 200 ng added Tra2β protein , compared with 50 ng for the Crebγ probe ) . An important measure of the functional importance of individual alternative splice events is evolutionary conservation [1] , [2] , [37] , [41] , [42] . Although many testis-specific exons are species-specific , phastcons analysis ( which measures phylogenetic conservation of sequences on a scale of 0 to 1 , with 1 being most conserved ) indicated very high levels of phylogenetic conservation for the Tra2a poison exon along with flanking intronic sequences ( Figure 5A–5C ) . Similar high levels of nucleotide conservation have been reported for poison exons in other genes encoding splicing regulator proteins including Sfrs10 itself [4] , [5] , [22] . The Tra2a poison exon , which is 306 nucleotides long , introduces stop codons into the reading frame of the Tra2a mRNA which encodes Tra2α protein . Despite the lack of protein coding capacity , 48% of nucleotides within the Tra2a poison exon are in fact conserved in all vertebrates ( Figure S2A: the nucleotide positions universally conserved in sequenced vertebrate genomes are shown in red ) . As a group , the 24 top most frequently recovered 6-mers from the entire transcriptome-wide screen were enriched in the nucleotide positions conserved between all vertebrates at levels much higher than would be expected by chance ( Figure S2A , p = 0 . 0075 , Fisher exact test: p = 0 . 0003 , Chi Squared test ) . These data are consistent with maintenance of multiple Tra2β-binding sites within the Tra2a poison exon since the radiation of vertebrates . When analysed by RT-PCR , the Tra2a poison exon was found to be particularly strongly alternatively spliced in the testis , with zero or much lower levels in other adult tissues ( Figure 5A–5C ) . Phastcons analyses also showed the Nasp-T cassette exon , which is also particularly long at 975 nucleotides , has been conserved since the last common ancestor of all vertebrates ( Figure 5D–5F ) . However neither the nucleotide or the peptide sequence encoded by Nasp-T are particularly highly conserved over the full length of the exon ( Figure 5E ) . The Nasp gene encodes a histone chaperone essential for mouse development [43] , and the Nasp-T exon introduces a peptide-encoding cassette exon generating a longer version of the Nasp protein . Similar to the Tra2a poison exon , 6-mers predicting Tra2β binding site sequences were found throughout the Nasp-T exon , and high frequency 6-mers mapped closely adjacent to CLIP tags ( Figure S2B ) . Within mammalian Nasp-T exons multiple Tra2β binding sites have been conserved . Extremely high levels of Nasp-T exon inclusion were detected by RT-PCR in the testis and heart . In gut , muscle and ovary , the Nasp-T exon inclusion isoform was also preferentially included but in other tissues it was frequently skipped ( Figure 5F ) . To experimentally address the function of multiple Tra2β binding sites in Nasp-T we used a combination of in silico and experimental analyses , and focused on an upstream portion of the exon ( from positions 117 to 271 ) . Using octamers predictive of splicing enhancers and silencers [44] , [45] , [46] , we firstly identified 3 strong putative ESEs ( Exonic Splicing Enhancers , ESE1 to ESE3 ) which we selected for further analysis , as well as other putative moderate ESEs ( Z score around 4 ) of which only one designated ESE4 was further studied ( Figure 6A ) . Each of these putative ESEs directly overlapped with Tra2β binding sites initially identified through 6-mers derived from the transcriptome-wide CLIP analysis . To experimentally test the need for individual Tra2β binding sites in splicing regulation , individual sites were mutated within the minigenes without creating Exonic Splicing Silencer ( ESS ) sequences ( Figure 6A ) [28] , and the splicing effect monitored . Mutation of single Tra2β binding sites had only a minor effect on Nasp-T splicing inclusion at endogenous cellular concentrations of Tra2β . However , pre-mRNAs containing double mutations affecting Tra2β binding sites ( M2+M3 , M1+M2 and M3+M4 ) had strongly reduced Nasp-T exon splicing inclusion compared to their wild type counterparts at normal endogenous cellular concentrations of Tra2β ( Figure 6B ) . Mutation of different Tra2β binding sites within Nasp-T also had distinct outcomes on exon inclusion , indicating underlying combinatorial effects between different patterns of Tra2β binding . In particular , mutant M3+M4 reduced exon inclusion levels to 20% of wild type at endogenous cellular levels of Tra2β , whereas double mutations comprising M2 and M3 reduced Nasp-T exon inclusion to just below 60% ( Figure 6B ) . Although they showed decreased exon inclusion at normal cellular concentrations of Tra2β , each of the double mutated Nasp-T exons gave at least 80% splicing inclusion after Tra2β protein was ectopically expressed . This suggested a requirement for higher levels of ectopic Tra2β protein for splicing inclusion . To test this , we co-transfected cells with minigenes containing either wild type Nasp-T exon or the M3+M4 mutant derivative , and a concentration gradient of Tra2β ( Figure 6C ) . Splicing inclusion of the wild type Nasp-T exon was already 90% without over-expression of Tra2β and was maximal after co-transfection of no more than 30 ng Tra2β expressing plasmid . In contrast , levels of inclusion of the M3+M4 NaspT exon derivative increased more slowly over the whole concentration gradient , indicating decreased splicing sensitivity to Tra2β after removal of just two binding sites . This is particularly striking since the M3+M4 NaspT exon retains multiple other Tra2β binding sites ( both experimentally confirmed sites in the case of ESEs 1–4 , and further predicted sites throughout the exon shown in Figure S1 ) . We used EMSAs to directly analyse RNA-protein interactions using both wild type and mutated versions of the Nasp-T RNA probe ( Figure 7 ) . While wild type Nasp-T and the single mutant M2 RNA were efficiently shifted , the average size of the M3+M4 RNA-protein complex was only slightly smaller ( the average size of the shifted complexes is indicated by a red asterisk on Figure 7 ) . Hence even a moderate change in in vitro RNA-protein interactions translates to a detectable change in splicing inclusion within cells . Mice with clearly reduced expression levels of Sfrs10 would be a prerequisite to enable detection of altered splicing patterns in Tra2β- targeted transcripts identified by CLIP . Since ubiquitous Sfrs10 deletion leads to embryonic lethality [19] , we generated a neuronal specific Sfrs10-depleted mouse by crossbreeding Sfrs10fl/fl mice with Sfrs10fl/+ mice carrying the Nestin-Cre transgene ( Nestin-Cretg/+ ) . In Sfrs10fl/fl; Nestin-Cretg/+ offspring the Cre recombinase would be specifically activated in neuronal and glial precursor cells from embryonic day 11 [47] to generate animals with a homozygous Sfrs10 knockout in the developing central nervous system ( CNS ) . Homozygous neuronal Sfrs10 mice died immediately after birth at postnatal day 1 ( PND1 ) whereas heterozygote mice had normal lifespans . Neuronal specific Sfrs10-depleted embryos showed severe malformations of the brain including strong dilation of the third and lateral ventricles as well as degeneration of cortical structures ( Figure 8A , right panel and data not shown ) whereas heterozygous knockout mouse embryos ( Sfrs10fl/wt; Nestin-Cretg ) had normal brain morphology ( Figure 8A , left panel ) . This indicates Tra2β protein is functionally very important for brain development in the mouse . As the liquid filled ventricles make up the majority of the whole brain volume , the brain morphology is heavily altered and the proportion of intact tissue is heavily reduced . Immunohistochemical analysis of whole brain paraffin-embedded cross-sections showed strongly decreased expression of Tra2-β with some Tra2-β positive cell areas in the cortical plate zone ( Figure 8A , right panel ) . These residual Tra2-β positive cells likely do not express Cre from the Nestin promoter and are likely of non-neuronal origin , or may represent mosaicism of Nestin-Cre expression . Furthermore , Western blots from whole brain also demonstrated a clear down-regulation of Tra2-β in neuronal specific Sfrs10-depleted embryos compared to controls and heterozygous knockout animals at 16 . 5 dpc ( Figure 8B ) . In control animals the Sfrs10 mRNA levels remained largely unchanged during development ( 16 . 5 dpc , 18 . 5 dpc and PND1 ) ( Sfrs10fl/fl n = 10; Sfrs10fl/+ n = 6; data not shown ) . Expression analysis of whole brain RNA from neuronal Sfrs10-depleted embryos at 16 . 5 dpc and 18 . 5 dpc and mice at PND1 showed clearly reduced Sfrs10 mRNA levels compared with brains of control littermates ( Sfrs10fl/fl , Sfrs10fl/+ or Sfrs10fl/+; Nestin-Cretg/+ ) ( Figure 8C ) . Regardless of the developmental stage the majority of Sfrs10fl/fl pups exhibited somewhat reduced Sfrs10 expression levels compared with heterozygously floxed mice , which suggested that the integration of the floxed allele has a slightly negative influence on Sfrs10 expression . Therefore for statistical analysis the expression levels of splice isoforms of Sfrs10fl/fl; Nestin-Cretg/+ mice were always compared with Sfrs10fl/+ and not Sfrs10fl/fl mice . Tra2-β regulates its own expression level via alternative splice regulation in an autoregulatory feedback-loop . Inclusion of poison exon 2 into Sfrs10 transcripts introduces a premature stop codon which leads to a non-functional protein and thus a reduction in Tra2-β levels [22] . Isoform specific qRT-PCR indicated a highly significant down-regulation of both individual mRNA splice isoforms and total length Sfrs10 mRNA in neuronal specific Sfrs10-depleted mice Sfrs10fl/flNestin-Cretg/+ ) compared to controls at 16 . 5 dpc ( Figure 8C ) . In contrast , in heterozygous knockout animals ( Sfrs10fl/+Nestin-Cretg/+ ) down-regulation of the functional isoform ( − exon 2 ) was less effective than for the non-functional ( + exon 2 ) isoform indicating the involvement of the autoregulatory feedback loop which counteracts any decrease in functional Tra2β protein in neuronal cells . We next set out to determine whether the Tra2a poison exon and Nasp-T cassette exon were true physiological target exons regulated by Tra2β in vivo . Correlating with an important regulatory role for Tra2β protein , splicing inclusion of the poison exon into the Tra2a mRNA was reduced 3-fold in neuronal Sfrs10-depleted mouse brains compared to controls at 16 . 5 dpc ( Figure 8E ) . Surprisingly , this decrease in poison exon inclusion could not be detected at later developmental stages like 18 . 5 dpc or PND1 ( data not shown ) . To determine whether low Tra2β levels directly affect the splicing of the Nasp-T exon , qRT-PCR was carried out on whole brain RNA of 16 . 5 dpc and PND1 pups . The levels of the T-exon isoform of Nasp mRNA ( Nasp-T ) were 4-fold reduced in brains of neuronal Sfrs10-depleted mice compared to controls at 16 . 5 dpc ( Figure 8D ) and PND1 ( data not shown ) . Given the 4-fold reduction of the Nasp-T isoform in Sfrs10-depleted tissue , we conclude that Tra2β protein is likely to be an important in vivo activator of Nasp-T exon inclusion during mouse development . These data correlate a defect in splicing regulation of Nasp-T and Tra2a with Sfrs10 depletion but do not necessarily imply a causal relationship , because of the differences in cell types present after Sfrs10 depletion which result from the physiological importance of Tra2β for brain development . To address this further we compared overall patterns of expression of the Nasp and Tra2a genes in wild type and knockout mice , by quantifying levels of the somatic Nasp and Tra2a mRNA isoforms . Consistent with no significant changes in overall Tra2a gene expression resulting from changes in the cell type population of the knockout brains , no statistically significant changes in functional Tra2a or Nasp expression were seen when comparing brain RNA of Sfrs10fl/+ mice with RNA of Sfrs10fl/fl; Nestin-Cretg/+ mice ( Figure 8D and 8E ) . These results are consistent with essentially similar patterns of Nasp and Tra2a gene expression in the mutant and wild type brains despite any differences in cellular composition , while in contrast the Tra2β-regulated splice isoforms from these same genes are very different between the wild type and mutant mice .
Here we have identified ( for the first time to the best of our knowledge ) physiological target exons regulated by Tra2β during mouse development . Identification is based on the criteria of in vivo cross-linking of endogenous RNAs and proteins , in cellulo experiments using transfected minigenes and proteins , RNA-protein interaction assays and genetic analysis using a newly derived conditional mouse strain which does not express Tra2β protein in neurons and has significant abnormalities in brain development . Our analyses reveal important pathways regulated by Tra2β protein in vivo which likely contribute both to prenatal death in Sfrs10−/− embryos and also to normal germ cell development [19] . Nasp protein is a histone chaperone required for nuclear import of histones at the G1-S phase transition of the cell cycle , and is essential for cell proliferation and embryonic survival [43] . Nasp functions in chromatin remodelling after DNA repair , and links chromatin remodelling to the cell cycle machinery after S phase [48] . The T exon is also spliced in embryos , and within the testis alternative splicing inclusion of the Nasp-T cassette exon generates the testis-enriched tNASP protein isoform . Timing of tNASP protein expression during male adult germ cell development [48] , [49] exactly parallels the expression of Tra2β protein . The tNASP protein isoform localises to the synaptonemal complex of meiotic chromosomes where it may help monitor double strand DNA break repair [43] , [48] , [50] . Tra2α and Tra2β are very similar proteins , and are interchangeable in our in cellulo splicing assays . Tra2β protein helps regulate overall Tra2 protein levels through both activating splicing inclusion of a poison exon into its own Sfrs10 mRNA , and also activating splicing inclusion of a poison exon into Tra2a mRNA which encodes Tra2α protein . In vivo experiments described here show that reduced inclusion of the poison exon does indeed help buffer the effect of decreased gene dosage in Sfrs10 heterozygote mice . However , down-regulation of Tra2a poison exon inclusion in Sfrs10−/− cells does not lead to an increase in Tra2a mRNA levels sufficient to restore splicing patterns of Tra2β target exons , perhaps suggestive of unique functions for the Tra2α and Tra2β proteins . In flies , auto-regulation of splicing by Tra2 protein of its own pre-mRNA has been shown to be critical for spermatogenesis , indicating that it might be a highly conserved feature for germ cells to tightly maintain expression levels of this class of splicing regulator [24] , [25] , [51] . Since Tra2α regulates Tra2a poison exon in cellulo , it is likely that it also autoregulates its own mRNA levels in vivo through activation of this same poison exon . An important current question is how RNA binding proteins like Tra2β achieve sequence specificity in target sequence selection despite having fairly short target sequences [15] . Here we have found a short consensus binding motif for Tra2β ( AGAAGA , Figure 2A ) which matches perfectly with specific motifs obtained both by classical SELEX analysis [12] and from identification of Tra2β specific ESEs in various genes [22] , [29] , [52] , [53] , [54] , [55] , [56] , [57] . Parallel genome-wide mapping showed that Tra2β primarily binds to exonic sequences . An explanation for exonic enrichment despite the short binding site would be if Tra2β binds to exons cooperatively with adjacent exonic RNA binding proteins . In the case of SMN2 exon 7 , the Tra2β binding site is flanked by cooperative binding sites for SRp30c and hnRNP G [17] , [53] , [58] . For Nasp-T and Tra2a there are instead arrays of exonic Tra2β binding sites . Removal of more than one binding site negatively affects exon activation by Tra2β , indicating for Nasp-T and Tra2a adjacent binding and assembly of homotypic Tra2β protein activation complexes play important roles in splicing activation . A model of splicing activation for the Nasp-T and the Tra2a poison exon which depends largely on sole binding of Tra2β protein might explain why these exons are particularly sensitive to depletion of Tra2β in vivo compared with SMN2 exon 7 ( splicing of which is not affected after deletion of Sfrs10 , and which has a single Tra2β binding site , Figure S1 ) . The human testis-specific HIPK3-T exon [50] also requires multiple Tra2β binding sites to enable splicing activation of a weak 5′ splice site in vitro [28] , and the Sfrs10 poison exon also has multiple Tra2β binding sites [22] . Other than Tra2a and Nasp-T , the remaining target exons we analysed using minigenes here have less dense coverage of Tra2β binding sites ( Figure S1 ) . These remaining exons also responded less robustly to Tra2β protein expression in vitro in transfected cells , and it is likely that RNA binding proteins other than Tra2β might also be more important for their splicing regulation in vivo . We also found that full lengthTra2β protein activates splicing of the Nasp-T exon at a lower level through its RS1 and RS2 domains only ( i . e . without the RRM and so without direct RNA binding ) . Mechanistically the RS domains of Tra2β might activate splicing by helping assemble other RS-domain containing splicing regulators and components of the spliceosome into functional splicing complexes . Although both RS domains could co-activate splicing when present together , removal of the RS1 domain completely disabled Tra2β-mediated splicing activation of the physiological target exons identified here . The observed functional importance of RS1 provides a mechanistic explanation why this N-terminal RS domain structure is maintained for Tra2 proteins in both vertebrates and invertebrates . Surprisingly Tra2β molecules without the RS1 domain were not just neutral for splicing inclusion in cellulo , but for some exons actually functioned as potent splicing repressors . Since the Tra2βΔRS1 isoform contains a functional RRM sequence , splicing repression could be due to competitive inhibition through this shorter Tra2β protein binding to the same RNA targets , but then being unable to assemble functional splicing complexes with other Tra2β proteins in the absence of the RS1 domain . Detection of such a competitive inhibitory function might have been helped by the increased levels of the Tra2βΔRS1 isoform expressed in our experiments . In vivo , the Tra2β-3 protein which lacks the N-terminal RS1 domain might also operate as a natural splicing repressor isoform [20] , [21] , [22] , depending on its level of expression being enough in specific cell types or tissues . Tra2βΔRS1 actually activates SMN2 exon 7 rather than being a repressor as seen for the physiological target exons we describe in this report [17] . Although the biology of SMN2 exon 7 has been an area of controversy in the literature [59] , [60] , a possible mechanistic explanation for this difference might be if Tra2β binding to SMN2 exon 7 blocked the action of an adjacent Exonic Splicing Silencer , rather than directly activating splicing by itself . Our analysis shows that the RNA targets identified for Tra2β in developing adult germ cells can predict patterns of splicing regulation by Tra2β in the developing brain . However , our data further suggest that splicing regulation by Tra2β is temporally restricted during development and also differentially regulated between various Tra2β targets . This is highlighted by Tra2a poison-exon splicing , which is affected by neuronal specific Sfrs10 knockout only at a defined developmental stage , while Nasp-T exon inclusion is perturbed by Sfrs10 knockout in all analyzed situations . Both the Nasp-T and the Tra2a poison exon are biologically important: they are conserved in all vertebrates for which genome sequences are available; have known functional roles; and like other phylogenetically conserved exons are spliced at high levels in at least some tissues [4] , [37] , [41] . The tNASP protein has been identified immunologically after the leptotene stage of meiosis in both rabbits and mice , indicating that this exon is meiotically expressed in both species [48] , [49] . In addition , although a high frequency of alternative splicing events in the testis are species-specific [61] , the high conservation of binding sites in the Tra2a poison-exon suggests regulation by Tra2β has been conserved since the radiation of vertebrates . Overall our data indicate maintenance of ancient patterns of splicing regulation controlled by this RNA binding protein , consistent with its observed key role in development [19] .
mRNA levels were detected in total RNA isolated from different mouse tissues using RT-PCR and standard conditions . RT-PCR products were analysed both by normal agarose gel electrophoresis ( not shown ) and capillary gel electrophoresis [62] , [63] . Sfrs10 primers were specific to sequences in exons 1 and 4 respectively ( 5′-GAGCTCCTCGCAAAAGTGTG-3′ and 5′-CAACATGACGCCTTCGAGTA-3′ ) . Tra2β protein was detected using immunohistochemistry in the mouse brain as previously described [64] and in the mouse testis using Abcam polyclonal Tra2β antibody ab31353 [28] as previously described [26] . Different Tra2a mRNA isoforms mRNA were detected by multiplex RT-PCR using Tra2aF ( 5′-GTTGTAGCCGTCGCCTTC T-3′ ) , Tra2aB ( 5′-TGGGATTCAGAATGTTTGGA-3′ ) and Tra2a poison ( 5′-TTCAAGTGCTTCTATCTGACCAA-3′ ) . Different Nasp-T mRNA isoforms were detected by RT-PCR using Nasp-TF ( 5′-AATGGAGTGTTGGGAAATGC-3′ ) , Nasp-TB ( 5′-TTGGTGTTTCTTCAGCCTTG-3′ ) and Nasp-TC ( 5′-TGCTTTGAAGTCGGTTCAACT-3′ ) . Hprt expression was detected using primers HrptF ( 5′-CCTGCTGGATTACATTAAAGCACTG-3′ ) and HprtR ( 5′-GTCAAGGGCATATCCAACAACAAAC-3′ ) . HITS-CLIP was performed as previously described [30] using an antibody specific to Tra2β [65] . The specificity of the antibody to Tra2β was confirmed by the experiment shown in Figure S3 , as well as the additional characterization already described [65] . In short , for the CLIP analysis mouse testis was sheared in PBS and UV crosslinked . After lysis , the whole lysate was treated with DNase and RNase , followed by radiolabelling and linker ligation . After immunoprecipitation with purified antisera specific to Tra2β [65] , RNA bound Tra2β was separated on SDS-PAGE . A thin band at the size of 55 kDa ( Tra2β migrates at around 40 kDa and MW of 50 nt RNA is about 15 kDa ) was cut out and subject to protein digestion . RNA was recovered and subject to sequencing which was carried out on the Newcastle University Roche 454 GS-FLX platform . Mapping was done with Bowtie [66] , allowing for two mismatches ( parameter –v 2 ) . 297070 reads were processed , of which 177457 ( 59 . 74% ) aligned successfully to the mouse genome ( Mm9 ) . 74476 ( 25 . 07% ) failed to align , and 45137 ( 15 . 19% ) reads were suppressed due to multiple hits on the mouse genome . K-mer analysis was carried out using custom written scripts in Python . Briefly , we calculated the frequency of occurance of each possible 6-mer sequence in the following: our CLIP dataset , the mouse genome ( mm9 ) and in the mouse testis transcriptome ( http://www . ncbi . nlm . nih . gov/projects/geo/query/acc . cgi ? acc=GSM475281 ) . The genome and transcriptome corrected frequencies were obtained by subtracting the background ( genome and transcriptome frequencies respectively ) from the signal ( frequency in CLIP dataset ) . CLIP reads were filtered to remove duplicates including overlapping reads . Statistical significance was determined using a Chi-squared test . The weblogo was derived from tags containing a GAA sequence by analysing the sequence composition surrounding the fixed sequence , using custom written scripts to generate an input for the freely available program weblogo ( http://weblogo . berkeley . edu/ ) . In our in vivo splicing study we utilized a previously established Sfrs10 mouse model on pure C57BL/6 background as described [19] . Genotyping was performed using tail DNA according to established protocols [19] . To induce a conditional Sfrs10 knock-out in the central nervous system we crossbred Sfrs10fl/fl mice with a Nestin-Cretg/+ mouse line . These mice express Cre recombinase under control of the rat nestin ( Nes ) promoter and enhancer [47] . Therefore Cre recombinase is expressed in neuronal and glia cell precursors from embryonic day 11 as well as in neurogenic areas of the adult brain [47] , [67] . For our analyses the presence of the Nestin transgene was determined by a standard PCR using the oligonucleotides 5′–CGCTTCCGCTGGGTCACTGTCG-3′ ( forward ) and 5′–TCGTTGCATCGACCGGTAATGCAGGC-3′ ( reverse ) at an annealing temperature of 58°C producing a 300 bp amplicon . Whole brain RNA was isolated from 16 . 5 dpc , 18 . 5 dpc and PND1 mice using the RNeasy Lipid Tissue Mini Kit ( Qiagen , Hilden , Germany ) . RNA concentration was determined by Quant-iT RiboGreen RNA Reagent and Kit ( Invitrogen , Darmstadt , Germany ) and equal amounts of RNA were used for first strand cDNA synthesis utilizing the QuantiTect reverse Transcription Kit ( Qiagen , Hilden , Germany ) . Quantitative real-time PCR was carried out using the Roche LC FastStart DNA Master SYBR green Kit ( Roche , Mannheim , Germany ) on the Roche LightCycler 1 . 5 . For realtime quantification total Sfrs10 transcripts were amplified using the oligonucleotides 5′-TAGAAGGCATTATACAAG-3′ ( forward ) and 5′′-CTCAACCCAAACACGC-3′ ( reverse ) at 3 mM MgCl2 and an annealing temperature of 63°C producing a 186 bp bp amplicon . To quantify Sfrs10 isoforms specifically we used the oligonucleotides 5′-AGAACTACGGCGAGCGGGAATC-3′ ( forward ) and 5′-CCTTGTATAATGCCTTCTAGAACTTCTTC-3′ ( reverse ) for the functional isoform and 5′-GAACTACGGCGAGCGGGTTAATG-3′ ( forward ) and 5′-CAAGTGGGACTTCTGGTCTGATAATTAGC-3′ ( reverse ) for the non-functional isoform . Both were run at annealing temperatures of 64°C resulting in amplicons of 191 bp and 161 bp , respectively . For the quantification of different target splice variants single isoforms were amplified separately . For the Nasp-T exon containing isoform the oligonucleotides 5′-GGAGTGCATGTAGAAGAGG-3′ ( forward ) and 5′-CGTCATAAACCTGTTCTCTC-3′ ( reverse ) were used at 1 mM MgCl2 and annealing at 65°C producing a 115 bp amplicon . The somatic isoform of Nasp was amplified using 5′-AATGGAGTGTTGGGAAATGC-3′ ( forward ) and 5′-CTGAGCCTTCAGTTTCATCTAC-3′ ( reverse ) at 3 mM MgCl2 , 62°C annealing while producing a product of 118 bp length . The functional Tra2a transcript was amplified using the oligonucleotides 5′-GTTGTAGCCGTCGCCTTCT-3′ ( forward ) and 5′-GAGACTCTCTGCCCTCGAAG-3′ ( reverse ) at 3 mM MgCl2 and 66°C annealing resulting in a 155 bp product . For the poison exon-containing isoform we used the same forward oligonucleotide as for the functional isoform and 5′-CTTGATTTATCTTCCACATTCTTGG-3′ ( reverse ) at 3 mM MgCl2 and 64°C annealing producing a 206 bp amplicon . All quantification data was normalized against Gapdh . Amplification was performed using the oligonucleotides 5′-GGCTGCCCAGAACATCATCC-3′ ( forward ) and 5′-GTCATCATACTTGGCAGGTTTCTC-3′ ( reverse ) at 3 mM MgCl2 and 63°C annealing producing a 169 bp amplicon . Agarose gel electrophoresis and basic melting curve analysis was performed to confirm PCR product specificity . For quantification a dilution series of cDNA was used to generate a standard curve for each isoform . Therefore the cycle threshold was plotted versus the logarithm of the concentration and the standard curve was determined by linear regression . This curve was then utilized to calculate the template concentration of unknown samples . All samples were measured in duplicates . Individuals of a genotype were averaged using the arithmetic mean . Fluctuations are displayed by the standard error of the mean , and these are indicated on the bar charts by error bars . The significance of differences between genotypes was verified using student's t-test . Candidate alternatively spliced exons identified by HITS-CLIP and approximately 240 nucleotides of intronic flanking region at each end were amplified from mouse genomic DNA with the primer sequences given below . PCR products were digested with the appropriate restriction enzyme and cloned into the Mfe1 site in pXJ41 [68] , which is exactly midway through the 757 nucleotide rabbit β-globin intron 2 . PCR products were made using the following primers: Krba1L: 5′-AAAAAAAAGAATTCtggggatcctagcaggtaca -3′ Krba1R: 5′-AAAAAAAAGAATTCccaaggatgtgataagcagga -3′ CREB2U: 5′-AAAAAAAACAATTGgggaccattcctcatttcct -3′ CREB2D: 5′-AAAAAAAACAATTGaaggcagttgtcatcattgc -3′ LIN28F: 5′-AAAAAAAAGAATTCccagcctggtctttaagagagt -3′ LIN28B: 5′-AAAAAAAAGAATTCcatacagtgaattatttgaaaacacc -3′ PankF: 5′-AAAAAAAAGAATTCcacatctgtgggtgcacttt -3′ PANKR: 5′-AAAAAAAAGAATTCttcaaaggactatttggttaacagc -3′ FABP9F 5′-AAAAAAAACAATTGtggcattcctttctcacctt -3′ FABP9R 5′-AAAAAAAACAATTGgagccttcctgtgtgggtat -3′ CREBGammaF: 5′-AAAAAAAACAATTGcaaacttctagatggtagaatgatagc -3′ CREBGammaR: 5′-AAAAAAAACAATTGtagccagagaacggaaccac -3′ NaspTF: 5′-AAAAAAAACAATTGtccttggaggacttctgttttc-3′ NaspTR: 5′-AAAAAAAACAATTGggcatgcctgcttaagtgta-3′ Tra2aF: 5′-AAAAAAAAGAATTCattagggactaggatggaacatga -3′ Tra2aR: 5′-AAAAAAAAGAATTCgcatgatggcacatgacttt-3′ ESE mutations within Nasp-T were made by overlap PCR with the additional primers NASPM1-S ( 5′-GGGTGGACGATAAGACAT GG-3′ ) and its complementary primer ( 5′-CCATGTCTTATCGTCCAC CC-3′ ) ; NASPM2-S ( 5′-GTGAGCCTCAAGAGTAGCTCC-3′ ) and its complementary primer 5′-GGAGCTACTCTTGAGGCTCAC-3′; NASPM3-S ( 5′-GAATCCTCTGCATAGGCAAAAG-3′ ) and its complementary primer ( 5′-CTTTTGCCTATGCAGAGGATT C-3′ ) ; NASPM4-S ( 5′-GGACTGACTCAAGTTGAGGTCGC-3′ ) and its complementary primer ( 5′-GCGACCTCAACTTGAGTCAGTCC-3′ ) . Analysis of splicing of pre-mRNAs transcribed from minigenes was carried out in HEK293 cells as previously described using primers within the β-globin exons of pXJ41 [29] . Because of the length of the regulated exons , additional internal primers were included in multiplex to detect inclusion of the Nasp-T cassette exon ( 5′-TGCTTTGAAGTCGGTTCAACT-3′ ) and Tra2a poison exon ( 5′-TTCAAGTGCTTCTATCTGACCAA-3′ ) . EMSAs were carried out as previously described [28] using full length Tra2β protein and in vitro translated RNA probes made from constructs containing amplified regions of the mouse genome cloned into pBluescript . Regions of the mouse genome were amplified using the following primers: Nasp1TraGSF 5′-AAAAAAAAGGTACCGAAGTGGAGAAGGGTGGAAG-3′ Nasp1TraGSB 5′-AAAAAAAAGAATTCGAAGCGACCTCATCTTCATTC-3′ Krba1GSF 5′-AAAAAAAAGGTACCGACTCCTCCCCACCCTAGTC-3′ Krba1GSR 5′-AAAAAAAAGAATTCGCCCAGCCATCTTCTACCTT-3′ Tra2aGSF 5′-AAAAAAAAGGTACCTTAATGTTCGTGAAGAAATTGAAGAG-3′ Tra2aGSR 5′-AAAAAAAAGAATTCTCATTAGCCTTCTTTTATCTTGATTTA-3′ Lin28GSF 5′-AAAAAAAAGGTACCCTTGAACTCTCTGATTTTAGGTTCTTC-3′ Lin28GSR 5′-AAAAAAAAGAATTCAACAGACTAACCTGGGGCTGA-3′ CrebγF 5′-AAAAAAGGTACCTCATTGTTCTAGGTGCTATCAAAGG-3′ CrebγR 5′-AAAAAAGAATTCCTGACATATTTTATTTTCTCATAGTAT GTCTCTC-3′ Creb2F 5′-AAAAAAGGTACCGTAACTAAATGACCATGGAATCTGGAGCA-3′ Creb2R 5′-AAAAAAGAATTCCTGGGCTAATGTGGCAATCTGTGG-3′
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Alternative splicing amplifies the informational content of the genome , making multiple mRNA isoforms from single genes . Tra2 proteins bind and activate alternative exons , and in mice Tra2β is essential for embryonic development through unknown target RNAs . Here we report the first target exons that are physiologically regulated by Tra2β in developing mice . Normal activation of these regulated exons depends on multiple Tra2β binding sites , and significant mis-regulation of these exons is observed during mouse development when Tra2β is removed . As expected , Tra2β activates splicing of some target exons through direct RNA binding via its RNA Recognition Motif . Surprisingly , for some exons Tra2β can also activate splicing independent of direct RNA binding through two domains enriched in arginine and serine residues ( called RS domains ) . The N-terminal RS1 domain of Tra2β is absolutely essential for splicing activation of physiological target exons , explaining why this domain is conserved between vertebrates and invertebrates . Surprisingly , Tra2β proteins without RS1 operate as splicing repressors , suggesting the possibility that endogenous Tra2β protein isoforms may differentially regulate the same target exons .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genome",
"expression",
"analysis",
"animal",
"genetics",
"functional",
"genomics",
"gene",
"function",
"animal",
"models",
"developmental",
"biology",
"model",
"organisms",
"molecular",
"genetics",
"gene",
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"gene",
"splicing",
"biology",
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2011
|
Identification of Evolutionarily Conserved Exons as Regulated Targets for the Splicing Activator Tra2β in Development
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Integration of human papillomavirus ( HPV ) genomes into cellular chromatin is common in HPV-associated cancers . Integration is random , and each site is unique depending on how and where the virus integrates . We recently showed that tandemly integrated HPV16 could result in the formation of a super-enhancer-like element that drives transcription of the viral oncogenes . Here , we characterize the chromatin landscape and genomic architecture of this integration locus to elucidate the mechanisms that promoted de novo super-enhancer formation . Using next-generation sequencing and molecular combing/fiber-FISH , we show that ~26 copies of HPV16 are integrated into an intergenic region of chromosome 2p23 . 2 , interspersed with 25 kb of amplified , flanking cellular DNA . This interspersed , co-amplified viral-host pattern is frequent in HPV-associated cancers and here we designate it as Type III integration . An abundant viral-cellular fusion transcript encoding the viral E6/E7 oncogenes is expressed from the integration locus and the chromatin encompassing both the viral enhancer and a region in the adjacent amplified cellular sequences is strongly enriched in the super-enhancer markers H3K27ac and Brd4 . Notably , the peak in the amplified cellular sequence corresponds to an epithelial-cell-type specific enhancer . Thus , HPV16 integration generated a super-enhancer-like element composed of tandem interspersed copies of the viral upstream regulatory region and a cellular enhancer , to drive high levels of oncogene expression .
High oncogenic-risk human papillomaviruses ( HPVs ) are the etiological agent responsible for virtually all cervical cancers [1] , and a growing number of other anogenital and oropharyngeal cancers [2] . During the normal viral life cycle , HPVs are maintained as extrachromosomal elements within the cell nucleus . However , during persistent infection with oncogenic HPVs the viral genome can become accidently integrated into the host chromatin [3]; this is a key event in many HPV-associated cancers . For the most part , HPV integration sites are randomly distributed throughout the host genome but preferentially associate with common fragile sites [4 , 5] and transcriptionally active regions [6 , 7] . The viral genome is usually found integrated as either a single copy or multiple , tandem , head-to-tail repeats at a single locus within the host genome [8] . In addition , there are often associated focal amplifications and rearrangements of the surrounding cellular flanking sequences [9–12] . These features result in a range of integration sites that are unique in terms of their genetic/epigenetic composition and transcriptional potential . There are several mechanisms through which HPV integration can promote oncogenesis ( reviewed in [13] ) . Often there is disruption of the viral E2 gene , which eliminates its repressive function over the E6/E7 oncogenes [3] . Dysregulation of E6/E7 expression promotes cellular proliferation , abolishes cell cycle checkpoints and induces progressive genetic instability at the integrated locus [14] . Insertional breakpoints can also occur within the E1 gene , eliminating downstream E2 function and the growth suppressive properties of the E1 protein [15 , 16] . Furthermore , upon integration , the viral E6/E7 oncogenes are expressed from the early viral promoter as a viral-host fusion transcript [17 , 18] . Such fusion transcripts have increased stability , resulting in increased E6/E7 oncogene expression compared to that expressed from extrachromosomal HPV genomes [8] . Less frequently , HPV integration occurs within host cancer-associated gene loci or pathways and modifies target gene expression [10 , 11 , 19–21] , which can further support neoplastic progression . We have recently demonstrated that tandemly integrated HPV16 in the cervical-derived cell line 20861 can form a Brd4-dependent super-enhancer-like element [22]; this is a novel mechanism that drives viral oncogene expression from the integrated locus . Super-enhancers are large clusters of enhancer elements that are highly occupied by the transcriptional machinery and often develop to drive oncogene expression in cancer [23–26] . Treatment of 20861 cells with BET-inhibitors disrupts Brd4 binding to this de novo super-enhancer , resulting in downregulation of E6/E7 and induction of cellular senescence [22] . In this follow up study , we have characterized the genomic architecture of the integration site in 20861 cells to elucidate the genetic and epigenetic mechanisms operating at this locus . Using whole genome sequencing ( WGS ) , hybrid capture sequencing , RNA-seq , ChIP-seq and fiber-FISH ( fluorescent in situ hybridization ) , we show that the HPV16 genome is integrated into an intergenic region in chromosome 2 and both the viral genome and approximately 25 kb of flanking cellular DNA are amplified to about 26 copies . The combination of the viral upstream regulatory region ( URR ) and a basal cellular enhancer in the flanking cellular sequence results in strong enrichment of super-enhancer markers at the integration locus and high expression of a fusion transcript encoding E6/E7 , which is spliced into a cryptic acceptor site in the cellular sequence . This hijacking of a basal cellular enhancer , resulting in the formation of a viral-cellular super-enhancer , is a novel mechanism by which HPV integration can promote neoplasia .
The original W12 cell line , derived from a cervical intraepithelial lesion , contained mostly extrachromosomally replicating HPV16 genomes [8] . However , cells containing integrated HPV16 genomes were found to frequently outgrow the culture and so the Lambert laboratory isolated a series of clones that contained either extrachromosomal or integrated viral genomes [8] . One of these clones , 20861 , has been well characterized and is frequently used as a representative cell line with integrated HPV16 DNA , while the 20863 clone contains extrachromosomal genomes [8 , 17] . We have recently shown that viral oncogene expression in 20861 cells is driven by a super-enhancer-like element [22] . Here we further characterize the HPV16 integration site in 20861 cells in several ways . First , the overall arrangement of the locus was analyzed by Southern blot . Total genomic DNA was extracted from 20861 and 20863 cells . The DNA was digested with either HindIII , which does not cut the HPV16 genome and therefore allows visualization of the supercoiled and relaxed circle forms of the extrachromosomal genome , or with BamHI , which linearizes the viral DNA . As shown in Fig 1A , 20863 showed the expected forms of extrachromosomal DNA . However , the pattern of bands observed in 20861 DNA indicated that there was rearrangement of viral and cellular DNA at the integration locus , as almost no unit length genomes were observed . The observed pattern is virtually identical to the original analysis of the W12 clones determined by Lambert and colleagues [8] . Quantitation of the Southern blot shown in Fig 1A determined that there were ~24 copies of the integrated viral genome in 20861 cells and ~230 extrachromosomal HPV16 copies in 20863 cells , similar to the copy numbers previously defined [8 , 22] . To determine the location of the integration site within the cellular genome , we initially used a technique called Amplification of Papillomavirus Oncogene Transcripts ( APOT ) . This technique amplifies sequences derived from polyadenylated RNA consisting of viral E6/E7 sequences spliced to host derived transcripts [27] . RNA was isolated from 20861 cells and reverse transcribed using an oligo-dT primer . The viral-host fusion transcript was amplified from the cDNA by PCR using forward primers from the E6/E7 region and oligo-dT reverse primers , and the resulting PCR products were purified and sequenced . As shown in Fig 1B , this identified a fusion transcript that had spliced into a cryptic acceptor at chr2: 28 , 595 , 675 ( hg19 ) . The cellular exon spanned nucleotides 28 , 595 , 425–28 , 595 , 675 and did not align with any annotated genes , or contain an open reading frame ( ORF ) . The size of the fusion transcript was shorter than that observed by Jeon and colleagues using Northern blot analysis [8] and , as we show below , is a minor , shorter RNA species that uses the same viral-host splice sites . To verify the location of the viral integration site , a FISH probe was generated from a cellular BAC clone RP11-1140H22 ( chr2: 28 , 504 , 596–28 , 660 , 966; hg19 ) , which spanned the integration locus . As shown in Fig 2A , hybridization of 20861 cells with the cellular genomic DNA BAC probe , along with an HPV16 probe , revealed that both probes colocalized in >70% of cells . As expected , the cellular probe gave rise to two signals in HPV-negative NIKS human keratinocyte control cells . However , the signal that overlapped the HPV16 integration locus in 20861 cells was far stronger . This indicated that cellular sequences flanking the HPV16 integration site had been co-amplified along with the viral genome . In addition , Brd4 colocalized with the 20861 HPV integration site as previously shown [22] . To confirm the amplification of cellular sequences flanking the integration site , we used quantitative PCR ( qPCR ) to measure the copy number of the sequences flanking the HPV16 integration site relative to the ACTB gene . This showed that there were approximately 20 copies of the region flanking the integration site relative to HPV16 in W12 20861 cells ( Supplementary S6D Fig ) . Therefore , cellular sequences flanking the integration sites are co-amplified along with the HPV16 sequences . To define the amplification at the HPV16 integration site in 20861 cells , 2 x 150 bp paired-end libraries were generated from total genomic DNA and subjected to WGS using the HiSeq 4000 platform ( Illumina Genome Network ) . WGS data were aligned to the human reference genome ( hg19 ) with ~7X sequencing coverage ( Table 1 ) . This showed a 12 bp deletion at chr2: 28 , 595 , 730–28 , 595 , 741 that marked the main HPV insertional breakpoints ( Supplementary S1 Table ) . The flanking amplified cellular sequence spanned 25 . 2 kb ( chr2: 28 , 583 , 052–28 , 608 , 216; approximately 12 . 5 kb either side of the integrated viral genome ) , and overlapped with 941 bp of exon 2 of the human non-coding RNA AK055918 ( Fig 2B ) . The depth of coverage over this region was increased 28-fold along with the integrated HPV16 genome ( very similar to the 24–26 copies of viral genome measured by Southern blot analysis and qPCR ) . We identified a gap in the aligned HPV sequence reads in the WGS analysis of the cell line 20861 , within the E2 gene ( Fig 2C ) . This gap was not seen in the 20863 cells derived from the same parental W12 cells . The boundaries of this coverage gap defined a 101 bp deletion at the HPV genome nt position 3052–3152 ( Fig 2C ) . We also observed a minor fraction of sequence reads in 20861 cells that still aligned to these deleted HPV sequences , suggesting that those viral sequences originated from a different , minor integration site ( see below , Supplementary S1 Table , and S1 Fig ) . To confirm the precise viral-host junctions , we re-sequenced the viral and viral-host insertional breakpoint sequences in 20861 and 20863 genomic DNA , using custom hybrid capture baits ( Supplementary S1 Table ) . This much more sensitive approach verified that the major HPV16 insertional breakpoints were at chr2:28 , 595 , 730 and at chr2:28 , 595 , 741 . The viral-host fusion reads confirmed that the viral genome was integrated within the E2 gene at the position of the deletion ( HPV genome nt position 3052–3152 ) . The reads also confirmed the 12 bp deletion at the host insertion site in chr2 ( 28 , 595 , 730–28 , 595 , 741 ) . Additional viral insertional breakpoints were detected in both 20861 and 20863 reads , but these sequences were only supported by a few reads each ( out of many millions of reads per sample ) , suggesting that they were minor integration sites likely representing minor sub-clonal cell populations ( Supplementary S1 Table ) . Another explanation for their relatively low counts was that they represented non-specific technical artifacts related to the very sensitive hybrid capture method , PCR amplification of libraries , or deep sequencing of the libraries . Integration events can be classified as Type I , in which a single viral genome is found integrated into the host DNA; or Type II , in which multiple tandemly repeated viral genomes are integrated at a single locus [8] . It has been proposed previously that approximately 30 copies of HPV16 are tandemly integrated at a single locus in 20861 cells in what was presumed to be a Type II pattern [8 , 17] . However , we show in Fig 2 that there is co-amplification of the flanking cellular sequences to a copy number similar to that of the integrated HPV16 genome . To further determine the organization of the integration site , we performed molecular combing/fiber-FISH to detect HPV16 genomes on stretched , individual DNA molecules . As shown in Fig 3 , measurement of linear fluorescent signals showed that the HPV16 genome was repeated in tandem units with interspersing cellular DNA of about 27 kb , in good agreement with the 25 . 2 kb amplified region measured by sequencing . We have designated this integration pattern of interspersed viral and host DNA as a Type III integration event , to distinguish it from Type II events in which no intervening host sequence is present . Analysis of 75 independent DNA fibers containing the Type III 20861 integration site showed that up to 28 copies of interspersed viral DNA could be detected on a single fiber ( Fig 3B ) . Given the low probability of combing a complete integration site , the largest repeated number of genomes ( 28 copies , N = 2 ) is likely to be the actual size of the site , and is consistent with our genomic sequencing data . The signal length of the integrated HPV16 genome measured on average 7 . 0 kb ± 1 . 6 kb ( Fig 3C ) , whilst the intervening space between the tandemly repeated viral copies measured approximately 27 . 3 kb ± 9 . 6 kb ( Fig 3D ) . Discrepancies in unit length measurements across individual fibers can be accounted for by the inherent limit of resolution for this technique , which is about 1 kb [28] . The unit length of the intervening host sequence closely matched the 25 . 2 kb amplified cellular region identified from WGS , suggesting that the region flanking the integration site had amplified into a viral-host concatemer . To confirm this , and further validate the location of the integration site within chromosome 2 , two-color fiber-FISH was performed on the combed DNA strands [29] . Using DNA probes against HPV16 ( red signal ) , and the amplified region at chr2: 28 , 583 , 052–28 , 608 , 216 ( green signal ) , we confirmed that the repeating unit was indeed a viral-host concatemer ( Fig 3E ) . In addition to the major Type III integration event represented in Fig 3 , we observed a second integration pattern in 20861 cells that appeared to be a Type II integration event , as it contained tandemly integrated HPV16 genomes without interspersed cellular sequence ( Supplementary S1 Fig ) . This integration pattern accounted for ~30% of the total number of DNA fibers containing integrated HPV16 compared to the Type III integration event . The location of this integration event is not clear , but it is likely close to the site on chromosome 2 ( see Supplementary S1 Table ) as there are no other prominent foci apparent by FISH of interphase cells ( Fig 2A ) . To further address transcription from the integrated locus we performed RNA-seq analysis and aligned sequencing reads to a custom reference assembly that contained the inverted HPV16 reference sequence incorporated at the junctional breakpoints identified from WGS . Alignment of RNA-seq reads to both the human ( hg19 ) and HPV type 16 isolate 16W12E ( AF125673 . 1 ) reference genomes [30] showed high expression of a fusion transcript spliced into the same acceptor site ( chr2: 28 , 595 , 675; hg19 ) as that identified from APOT ( Fig 4 ) . Alignment of reads to the host sequence showed three strong peaks . The largest mRNA species was approximately 2 . 4 kb , >1 . 5 kb longer than the transcript isolated from APOT , which corresponded to the first cellular peak , but similar in size to the transcript first noted by Jeon and colleagues using Northern blot analysis [8] . This suggests that several RNA species which utilize the same viral-host splice sites are encoded by the Type III integration site in 20861 cells . We have shown previously that integration of HPV16 in 20861 cells results in the formation of a super-enhancer-like element that drives viral E6/E7 mRNA expression from the region [22] . This element was originally detected because of extremely high levels of H3K27ac and binding of MED1 and Brd4 at the HPV integration locus . To address exactly where these super-enhancer markers were binding with respect to the integrated viral genome and flanking cellular sequences , we performed ChIP-seq analysis using antibodies against Brd4 and H3K27ac . Alignment of paired-end reads immunoprecipitated with these antibodies to the human reference genome ( hg19 ) showed strong enrichment of Brd4 and H3K27ac signals adjacent to the integration locus in 20861 cells relative to levels observed in 20863 cells , which do not contain integrated HPV16 at this locus ( Fig 5A ) . To address enrichment patterns across the entire integrated locus ( viral and cellular sequences ) in 20861 cells , we aligned the ChIP-seq reads to our custom reference assembly of the integration locus . This showed very strong enrichment of the super-enhancer markers across both viral and cellular sequences ( Fig 5B ) , even after accounting for amplification of this region . Two peaks were observed , one that was centered on the viral URR and another in the cellular flanking sequence about 6 kb upstream . Analysis of the same cellular region in 20863 cells showed only minimal enrichment of H3K27ac at this locus . To determine whether there was similar enrichment of the super-enhancer markers at other HPV16 integration sites we examined in detail two additional W12 clones , 20831 and 20862 . We generated and analyzed WGS data at comparable depths of sequencing coverage for these additional clones ( see Table 1 and Supplementary S2 Fig ) . These clones were originally reported to contain approximately 60 copies of tandemly integrated HPV16 [8 , 17] , however WGS and qPCR ( Table 1 ) indicated a lower copy number , suggesting that viral genome copies had been lost over time . Chromatin immunoprecipitation ( ChIP ) followed by qPCR showed that strong enrichment of super-enhancer markers over the viral URR was not observed in 20831 and 20862 cells ( Fig 6 ) , and , unlike in 20861 cells , the prominent nuclear BRD4 focus was not characteristic of these cells by immunofluorescence ( Supplementary S3 Fig ) . We propose that super-enhancer function is likely dependent on the genetic and epigenetic architecture of the locus . The 20831 and 20862 cells were also subjected to RNA-seq and ChIP-seq with Brd4 and H3K27ac antibodies . This revealed that in both cell lines the virus was integrated at HPV16:3740-chr3:189 , 662 , 800 , indicating that this integration event likely occurred prior to the isolation of individual clones . This is consistent with the studies of Jeon and Lambert that indicated that these clones had very similar properties [8 , 17] . This Type III integration site was not present in the 20861 or 20863 cells , however ( Supplementary S1 Table ) . Additional integration breakpoints were undetectable in 20831 and 20862 cells through WGS . Unlike 20861 and 20863 cells , these clones were not subjected to hybrid capture . Fiber-FISH analysis of 20831 and 20862 cells showed that , as with 20861 cells , both cell lines contained Type II and Type III integration patterns ( Supplementary S4 and S5 Figs ) . This was further substantiated by Southern blot analysis ( shown in Supplementary S6 Fig ) . When digested with a restriction enzyme that cleaves the viral genome once , Type II integration gives a viral genome unit length of ~7 . 9 kb , while Type III integration results in bands comprised of the viral-host junctional sequences . Quantitation of the Type II and III fragments gives an approximation of the relative proportion of viral copies at each integration site . A combination of Southern blot analysis , WGS and qPCR of viral sequences demonstrated that the 20831 and 20862 cells contained less than the original estimated 60 genome copies . We believe that the copy number of viral genomes at the tandem integration sites is mutable , likely because of recombination events at these loci . A summary of the relative genomic properties of the HPV integration sites in 20861 , 20831 and 20862 cells is presented in Supplementary S6C Fig . In 20831 and 20862 cells , the viral genome was integrated in an intergenic region between the TP63 and LEPREL1 genes on chromosome 3 , and it appeared that approximately 89 kb of the flanking cellular sequence ( chr3: 189 , 573 , 774–189 , 662 , 800; hg19 ) , which also overlapped with 41 . 3 kb of the TP63 gene , had been amplified around 6 . 6- and 2 . 5-fold in 20831 and 20862 cells , respectively . RNA-seq identified a viral-cellular fusion transcript of approximately 1 . 5 kb transcribed from the integration locus in both W12 sub-clones ( Supplementary S7A Fig ) , the expression of which was approximately 2 . 2-fold ( 20831 cells ) and 6 . 6-fold ( 20862 cells ) lower than the fusion transcript expressed from the 20861 Type III integration site ( Fig 4 ) . Alignment of ChIP-seq reads to the sequences flanking the integration sites in 20831 and 20862 cells showed similar levels of Brd4 binding and H3K27ac modification to those observed in the same region of 20861 and 20863 cells , which do not have HPV integrated in this region ( Supplementary S7B Fig ) . Similarly , there was not a strong enrichment of super-enhancer markers across the integration site in 20831 and 20862 cells when signals were aligned to our custom reference genome ( Supplementary S7C Fig ) . It should also be noted that the higher copy number of viral genomes relative to the amplified cellular flanking sequence is the result of additional ChIP-seq signals that originate from the Type II integration sites that also exist in these two sub-clones ( Supplementary S4 and S5 Figs ) . In conclusion , the formation of an HPV induced super-enhancer at the site of integration is not a universal event . Notably , the high peak of H3K27ac activity over the amplified cellular sequence at the 20861 HPV integration locus overlapped with an epithelial-specific enhancer identified from ENCODE data ( Bernstein Lab , Broad Institute; Farnham and Snyder Labs , Stanford University ) [31–33] , as shown in Fig 7 . A small peak of H3K27ac activity at this enhancer region was also observed in the 20863 control cells ( Fig 5A ) , supporting its role as a regulatory element in keratinocytes . Furthermore , the ENCODE data also shows this peak to be enriched in H2A . Z , H3K9ac , H3K4me1 , and H3K4me2 in NHEK ( normal human epidermal keratinocytes ) . This indicates that HPV16 integrated adjacent to , and multimerized , a basal cellular enhancer , which subsequently synergized with the viral URR enhancer to create a super-enhancer-like element in 20861 cells .
We have recently shown that tandem copies of integrated HPV16 DNA formed a super-enhancer-like element that drives expression of the viral oncogenes in the cervical neoplasia derived cell line , 20861 [22] . In this follow-up study , our aim was to characterize the genetic and epigenetic architecture at this integration locus to better understand the mechanisms that led to the formation of a de novo super-enhancer . Early studies of HPV integration had defined the sites of viral integration as either Type I ( a single integrated copy ) , or Type II ( tandem head-to-tail repeats of multiple viral genomes ) [8] . Subsequent studies have shown that the flanking cellular sequences are often co-amplified and rearranged [9–12] and so here we designate tandem copies of the HPV genome interspersed with cellular DNA as Type III integrants . WGS of 20861 DNA showed that the viral genome was integrated into chromosome 2 ( chr2:28 , 595 , 730–28 , 595 , 741 ) and had been amplified about 26-fold along with 25 kb of flanking cellular DNA , forming a Type III viral-host concatemer . This indicates that co-amplification of viral and host DNA occurred after the initial integration event . The initial amplification of virus-host sequences could happen if the viral E1 and E2 replication proteins were still expressed from intact genomes and could initiate DNA synthesis from the integrated viral origin . This could result in onion skin replication and/or rolling circle type replication , which could give rise to viral-host concatamers [9 , 10 , 34 , 35] . Homologous recombination could result in further multimerization of the integration locus [9 , 36] . The Type III viral-host concatemer in 20861 cells was further confirmed using fiber-FISH analysis of the integration site with DNA probes against HPV16 and the amplified cellular flanking sequence . This powerful technique allowed us to verify and visualize the repeating tandem units of viral DNA and interspersing cellular DNA at the molecular level . The transcriptional and epigenetic landscape of the 20861 Type III integration site was analyzed using next-generation sequencing techniques . We initially identified a viral-host fusion transcript by APOT and , as is commonly found , the HPV16 splice donor at nt 880 was joined to a cryptic splice acceptor in the adjacent cellular DNA . RNA-seq analysis confirmed the use of this viral-host splice site , but also identified longer transcripts derived from this locus . This is all consistent with previous RNA analysis of 20861 cells using Northern blots and S1 nuclease mapping analyses [17] . ChIP-seq analysis showed two strong peaks of enrichment of the super-enhancer markers Brd4 and H3K27ac at both the viral URR and adjacent cellular sequence at the integration locus , the latter of which was not observed in the same region of cellular DNA in the W12 20863 sub-clone that harbors extrachromosomal viral DNA . The strong peak of H3K27ac modification within the amplified cellular flanking sequence overlapped with an epithelial-specific enhancer ( also defined by H3K27ac ) in the ENCODE datasets [31–33] . The identified enhancer was shown to be particularly active in normal human epidermal keratinocytes ( NHEK ) , as well as HeLa cervical carcinoma and MCF-7 mammary gland adenocarcinoma cells , suggesting that it may play a role in regulating pathways relevant to epithelial cell function and/or cancer . We postulate that it is synergy between this enhancer element and the viral URR that led to the development of a super-enhancer-like element that drives high-level expression of the viral E6 and E7 oncogenes . We suspect that any enhancer that is active , or could be activated , could be hijacked and amplified and under the correct circumstances develop into a super-enhancer . HPV integration can occur randomly throughout the entire human genome , but has a tendency towards transcriptionally active regions [6 , 7] . Integration may also occur in transcriptionally inactive regions , but it is likely that these do not promote strong expression of E6 and E7 leading to the outgrowth of a clonal population . Integration into transcriptionally active regions could enhance viral gene expression in general , but the hijacking of an adjacent enhancer element could further promote viral oncogene expression . We propose that in 20861 cells , the virus integrated adjacent to , and hijacked , a basal tissue-specific enhancer . Subsequent co-amplification of the viral genome and surrounding cellular sequences resulted in the development of a viral-cellular super-enhancer-like element to strongly drive oncogenic progression . To show that the development of the 20861 super-enhancer was most likely due to synergy between the viral URR and the adjacent cellular enhancer , we examined two other W12 sub-clones , 20831 and 20862 , that were originally described to have ~60 tandem copies of integrated HPV16 DNA . These clones did not show any enrichment of the super-enhancer marker Brd4 at the integration locus by immunofluorescence . Furthermore , ChIP-seq signals for super-enhancer markers at the 20831/20862 integration site were similar to patterns observed in 20861 and 20863 cells , which do not contain integrated HPV at this locus . Correspondingly , these clones had previously been reported to express lower levels of E6/E7 mRNA [8 , 17] . We discovered that these clones were related to each other as they both contained HPV16 integrated into the same site in chromosome 3 , which is very consistent with earlier analyses [8 , 17] . However , we noted that the number of viral genomes were less than originally reported , and our preliminary observations suggest that the copy numbers at each integration site are unstable/dynamic . Focal amplification was significantly greater at the Type III integration site in 20861 cells relative to the other integrated W12 sub-clones , which may be the result of increased genomic instability at the locus due to higher E6/E7 oncogene expression . We also examined an additional six cervical carcinoma derived cell lines ( C-33A , C-411 , CasKi , HeLa , ME-180 and SiHa ) for a prominent Brd4 focus but none was apparent . Therefore , the development of a strong super-enhancer at the site of HPV integration is not universal and likely results from a combination of factors . The location of HPV insertional breakpoints within the human genome can influence the transcription of neighboring genes [10 , 20 , 37] and HPV integration into , or adjacent to , cancer-associated genes has been reported to be a potential mechanism for HPV-mediated oncogenic progression in some cases [11 , 19 , 38] . Altered transcription can be restricted to the disrupted gene with minimal or no effects on neighboring transcripts [20] , or can extend to contiguous genes [10 , 20] . However , in almost all cases oncogenesis is primarily driven by , and dependent on , the E6 and E7 viral oncogenes . At the 20861 Type III integration locus , the HPV16 genome integrated into an intergenic region on chromosome 2 , and the subsequent focal amplification of the surrounding cellular sequence overlapped with a non-coding RNA located downstream of the integration site . Integration also resulted in upregulation of FOSL2 ( 3 . 5-fold , adjusted p-value 9 . 2 x 10−6 ) , which is located approximately 20 kb downstream of the HPV integration site , just outside the amplified region ( GEO accession number GSE75987 ) [22] . Therefore , in addition to driving high E6/E7 expression , the super-enhancer increased transcription of neighboring genes . We previously showed that disruption of Brd4 from the 20861 super-enhancer resulted in down-regulation of E6 and E7 gene expression and induction of cellular senescence [22] . However , FOSL2 levels were only minimally reduced , indicating that 20861 cell proliferation is completely dependent on expression of the E6 and E7 oncogenes . Southern blot analysis of the W12 cell clones had indicated that they contained both unit length genomes ( most likely head-to-tail copies of the viral DNA; Type II integration ) as well as amplified viral-cellular junctional DNA ( Type III integration ) [8] . The fiber-FISH experiments presented here confirmed that the clones examined all contained at least two independent integration events . Each clone examined contained a Type II and a Type III integration site located at different regions of the host genome . Although each clone contains Type II and Type III integration sites , our data suggests that only the Type III integration site is transcriptionally active . RNA-seq analysis detected only transcripts from the Type III viral-host junctional sequences in all three clones . We confirmed this by absolutely quantifying the amount of mRNA containing the viral E6*I splice with that of the downstream cellular sequences ( Supplementary S6E–S6G Fig ) . This is in line with previous studies , which reported that fusion mRNAs were expressed from a single integration site in tumors containing multiple integration loci [39 , 40] . This is also consistent with initial studies that showed that no RNA sequences from the 3’ end of the HPV16 early region could be detected in 20861 , 20831 and 20862 cells [8 , 17] . Distinct from 20861 cells , the 20831/20862 fusion transcript contained the HPV16 E1^E4 splice ( nt 880^3358 ) before running into the cellular sequence at nt 3741 . It has been shown that viral sequences from the HPV16 3’ early region decrease the steady-state expression , stability and translation of early mRNAs [17 , 41] and replacement of these sequences with host sequences can increase expression of E6 and E7 . In normal cells , repetitive DNA sequences are silenced through methylation to prevent genomic instability at these regions [42] . Kalantari et al . have previously shown that tandemly integrated viral DNA is hypermethylated in CasKi , 20831 and 20862 cells , whereas viral DNA is hypomethylated in 20861 cells [43] . We show here that the majority of viral genome copies are located in the transcriptionally active Type III integration locus in 20861 cells , while in 20831/20862 cells most viral genomes are in the Type II integration locus . It is possible that the head-to-tail tandem copies of the viral genome at Type II integration sites are recognized and silenced through spreading methylation [44] , whereas Type III integration events can escape such epigenetic silencing due to the intervening cellular sequences . There are several ways in which integration of the HPV genome into the host can deregulate expression of the E6 and E7 oncogenes and promote oncogenesis [13] . Here , we describe a new way in which the virus hijacks an adjacent cellular enhancer to drive strong , deregulated expression of E6/E7 . Amplification of the viral-host DNA at this site resulted in the formation of a strong super-enhancer-like element . Thus , there is no universal mechanism by which integration of an oncogenic HPV promotes oncogenesis; instead it depends on many stochastic factors such as viral copy number , associated amplification and rearrangements , the existence of a convenient host splice acceptor and/or polyadenylation site , and the epigenetic signature of the locus .
W12-derived subclones [8 , 17] and NIKS human keratinocyte [45] cell lines were maintained in F-medium ( 3:1 [vol/vol] F-12–Dulbecco’s modified Eagle’s medium , 5% fetal bovine serum , 0 . 4 μg/ml hydrocortisone , 5 μg/ml insulin , 8 . 4 ng/ml cholera toxin , 10 ng/ml epidermal growth factor , 24 μg/ml adenine , 100 U/ml penicillin and 100 μg/ml streptomycin ) . All cells were grown in the presence of irradiated 3T3-J2 feeder cells . C-33A , C-411 , CasKi , HeLa , ME-180 and SiHa cervical carcinoma derived cell lines were maintained in Dulbecco’s modified Eagle’s medium , 10% fetal bovine serum , 100 U/ml penicillin and 100 μg/ml streptomycin ) . APOT was performed as described previously [27] . See Supplementary S2 Table for primer sequences . The resulting PCR product band was gel purified using Roche High Pure PCR Product Purification Kit and sequenced . The cellular DNA fused to HPV16 was identified using BLAT Search on the UCSC Genome Browser . Total DNA was harvested with the DNeasy Blood and Tissue kit ( Qiagen ) . 1 μg total DNA was digested with either a single-cut linearizing enzyme ( BamHI ) for the HPV16 genome or a non-cutter ( HindIII ) to linearize cellular DNA . After digestion , samples were separated on 0 . 8% Tris-acetate-EDTA ( TAE ) agarose gels . A known quantity of linear HPV16 DNA was run on the same gel as the samples . DNA was visualized with 0 . 5 mg/ml ethidium bromide and transferred onto nylon membranes with a TurboBlotter downward transfer system ( Whatman ) . Membranes were UV cross-linked , dried , incubated with prehybridization blocking buffer ( 3× SSC [1× SSC is 0 . 15 M NaCl plus 0 . 015 M sodium citrate] , 2% SDS , 5× Denhardt’s solution , 0 . 2 mg/ml sonicated salmon sperm DNA ) , and then incubated overnight with [32P]-dCTP-labeled HPV16 DNA in hybridization buffer ( 3× SSC [1× SSC is 0 . 15 M NaCl plus 0 . 015 M sodium citrate] , 2% SDS , 5× Denhardt’s solution , 0 . 02 mg/ml sonicated salmon sperm DNA , 25 ng [32P]-dCTP-labeled HPV16 DNA ) . Radiolabeled probe was generated from 50 ng of gel-purified linear HPV16 DNA with a Random Prime DNA labeling kit ( Roche ) . Hybridized DNA was visualized and quantitated by phosphorimaging on a Typhoon Scanner ( GE Bioscience ) . Total cellular DNA was purified from 2 x 106 W12 cells using the DNeasy Blood and Tissue kit ( Qiagen ) and sequenced ( 2 x 150 bp paired-end reads ) using the Illumina HiSeq 4000 platform ( Genomics Resource Center , Institute for Genome Sciences , University of Maryland ) . Alignment of WGS data was performed using Bowtie 2 ( version 2 . 2 . 5 ) [46 , 47] . To confirm virus-host insertional breakpoints , we designed a custom panel of Agilent SureSelect XT v . G . 1 HPV16 genome sequences to detect viral genomic sequences and flanking host genomic DNA sequences in various samples ( manuscript in preparation , Symer et al . , similar in overall approach to [48] ) . We performed the enrichment and library preparation using a protocol on the Agilent Bravo following the manufacturer’s protocol in the Genomics Shared Resource , Ohio State University Comprehensive Cancer Center . Libraries were sequenced using a HiSeq 2500 ( 2 x 150-bp paired-end reads ) at Nationwide Children’s Hospital . More than 5 million reads were sequenced per sample ( i . e . for cell line 20861 , we generated 5 . 5 million reads , and for 20863 , 23 million reads; see Supplementary S1 Table ) . Reads were aligned against a custom reference genome assembly ( i . e . human genome sequence ( UCSC hg19 genome ) + HPV16 genome ) using BWA aligner [49] . Discordant sequence read pairs having one end aligned to the HPV16 viral genome and the other end aligned to the human genome were re-aligned using the GSNAP aligner [50] . After removing the duplicate pairs from these reads , we used a custom pipeline involving Hydra [10 , 51] to identify and confirm HPV insertional breakpoints in the human genome . Many of the breakpoints observed in chromosome 2 in the 20863 cell line were located at simple repeat regions , so those calls were supported by low numbers of paired end reads and were made at low confidence ( Supplementary S1 Table ) . Cells were cultured on coverslips and fixed at room temperature in 4% paraformaldehyde ( PFA ) –PBS for 20 minutes . Cells were permeabilized in 0 . 1% Triton X-100 and stained with primary and fluorescent secondary antibodies using standard procedures . Coverslips were mounted in ProLong Gold containing 4’ , 6-diamidino-2-phenylindole ( DAPI ) for analysis by confocal microscopy . Anti-BRD4 CW152 rabbit polyclonal antibody was used at 1:100 dilution [52] . Cells grown on coverslips were fixed in cold methanol-acetic acid ( 3:1 ) for 1–3 minutes followed by 4% paraformaldehyde–PBS for 10 minutes . Cells were treated with RNase A for 1 hour at 37°C and dehydrated in a 70% , 90% , and 100% ethanol series for 3 minutes each . Green 5-Fluorescein dUTP labelled BAC clone RP11-1140H22 ( chr2: 28 , 504 , 596–28 , 660 , 966; hg19 ) was purchased from Empire Genomics . DNA-FISH probe against full-length HPV16 DNA was fluorescently labeled using the Alexa Fluor-594 FISH Tag DNA Multicolor Kit ( Life Technologies ) following manufacturer’s protocol . To each coverslip , 40–50 ng labeled DNA FISH-probe in hybridization buffer ( Empire Genomics ) supplemented with 50 μg/ml human Cot-1 DNA ( Invitrogen ) was added . DNA denaturation was performed at 75°C for 5 minutes , followed by hybridization at 37°C overnight . Cells were washed for 5 minutes each with 1x phosphate-buffered detergent ( Qbiogene ) at room temperature , 1x wash buffer ( 0 . 5x SSC , 0 . 1% SDS ) at 65°C , and 1x phosphate-buffered detergent at room temperature . Coverslips were mounted in ProLong Gold ( ThermoFisher ) containing DAPI for analysis by confocal microscopy . Cells grown on coverslips were fixed in cold methanol-acetic acid ( 3:1 ) for 1–2 minutes followed by 4% paraformaldehyde–PBS for 10 minutes . Cells were processed for immunofluorescence staining followed by DNA FISH using methods described above and as described previously [53] . W12 cells were processed as previously described [22] . Briefly , 5 x 107 cells were crossed-linked with 1% formaldehyde and sheared to 100–500 bp DNA fragments using a Bioruptor sonicator ( Diagonode ) on high power settings . An aliquot of the lysate ( 5% ) was processed for input control . Chromatin samples ( 20 μg per ChIP ) were incubated overnight at 4°C with antibodies against Brd4 ( Bethyl Laboratories A301-985A , 3 μg ) and H3K27ac ( Millipore 07–360 , 3 μl ) . No-antibody controls were included to measure non-specific binding . Chromatin-immunocomplexes were precipitated for 1 hour at 4°C with blocked Dynabeads Protein G ( Invitrogen ) , subjected to multiple wash steps and the chromatin eluted . Recovered DNA was reverse cross-linked overnight at 65°C with 5 M NaCl , followed by RNase A and proteinase K treatment , and purified using the ChIP DNA Clean & Concentrator kit ( Zymo Research ) . ChIP DNA was analyzed by real-time qPCR , using primers listed in Supplementary S2 Table . Real-time qPCR was performed using the ABI Prism 7900HT sequence detector ( Applied Biosystems ) and SYBR green PCR master mix ( Applied Biosystems ) . All reactions were run in triplicate and compared to standard curves of input chromatin DNA or cloned HPV16 genome from W12 cells , for determination of viral copy number , to generate a standard curve of threshold cycle ( CT ) versus log10 quantity ( fg ) . W12 cells were processed as described above for ChIP-qPCR . ChIP DNA samples were pooled and sequenced ( 2 x 150 bp paired-end reads ) using the Illumina HiSeq 4000 platform ( Genomics Resource Center , Institute for Genome Sciences , University of Maryland ) . Alignment of ChIP-seq data was performed using Bowtie 2 ( version 2 . 2 . 5 ) [46 , 47] . Total RNA was purified from W12 cells using the RNeasy Mini Kit ( Qiagen ) and RNA integrity determined using the Bioanalyzer 2100 ( Agilent Technologies ) . Polyadenylated RNA was sequenced ( 2 x 150 bp paired-end reads ) using the Illumina HiSeq 4000 platform ( Genomics Resource Center , Institute for Genome Sciences , University of Maryland ) . RNA-seq data was aligned to reference genomes using TopHat2 ( version 2 . 1 . 0 ) [54] and expression analysis of known transcripts ( hg19_refseq_16_08_01_v2 ) performed using Cufflinks ( version 2 . 2 . 1 ) [55] . HPV16 DNA inserted into the BamHI site of pUC18/19 was digested with BamHI and extracted from a 0 . 8% agarose gel to remove pUC18/19 . Biotinylated HPV16 genome probe was synthesized with BioPrime DNA Labeling System ( Invitrogen , 18094011 ) using 500 ng of digested HPV16 DNA incubated at 37°C for 4 hours . Biotinylated probe was purified using QIAquick PCR Purification Kit ( Qiagen , 28104 ) . W12 cells were harvested , pelleted for 5 minutes at 1 , 000 rpm , and resuspended in cold 1x PBS to a dilution of 1 x 106 cells per ml . Diluted cells were warmed to 37°C for 1–2 minutes , and an equal volume of 1 . 5% SeaPlaque agarose ( Lonza , 50100 ) in 1x PBS , warmed to 45°C , was added . Cell/agarose mixture was aliquoted into plug mold wells ( Biorad , 1703713 ) and solidified for 20 minutes at 4°C . Solid plugs were ejected into lysis buffer ( 1 mg/ml proteinase K [added directly before use] , 100 mM EDTA , 1% N-lauroylsarcosine , 10 mM Tris-HCl , pH 8 . 0 , and incubated overnight in a 50°C water bath . Lysis buffer was discarded and plugs were rinsed with 1x TE . Rinsed plugs were washed three times for 1 hour in 1x TE . Washed plugs were stored for several months in 1x TE at 4°C . The washed plug was transferred to a 2 ml tube and melted for 20 minutes at 70°C in 0 . 1 M MES ( pH 6 . 5 ) . Melted plug was cooled for 5 minutes in a 42°C water bath . 3 units of β-agarase ( New England BioLabs , M0392S ) were added to cooled DNA solution and incubated overnight in a 42°C water bath . DNA solution was carefully poured into a 2 ml Teflon reservoir . A silanized coverslip was inserted into the DNA solution and incubated for 5 minutes . The coverslip was removed and adhered to a glass slide with cyanoacrylate glue . A test coverslip was pulled , stained with YOYO-1 ( 2:10 , 000 YOYO-1 in 0 . 1 M MES ) , and visualized with a microscope to ensure quality and density of DNA fibers . Coverslips were baked for 30 minutes at 60°C to crosslink DNA fibers to coverslip . DNA fibers were denatured for 20 minutes in 0 . 2 M NaOH . Slides were neutralized 5 times with 1x PBS ( pH 7 . 4 ) for 1 minute . Slides were dehydrated for 3 minutes each in successive baths of 70% , 90% , and 100% EtOH and air dried for 8 minutes . 500 ng of HPV16 biotinylated probe was mixed with 5 μg of human Cot-1 DNA and hybridization buffer ( 50% formamide , 2x SSC , 0 . 5% SDS , 0 . 5% N-lauroylsarcosine sodium salt , 1% Blocking reagent [Roche , 11096176001] , 10 mM NaCl ) up to a total volume of 20 μL . Probe mixture was added to coverslip , a clean coverslip was placed on top and sealed with rubber cement . Slides were denatured for 5 minutes at 75°C and then incubated for 16 hours at 37°C . The rubber cement and top coverslips were removed from the slides and the slides rinsed with 2x SSC . The slides were washed three times with 2x SSC/50% formamide for 5 minutes , and then washed three times with 2x SSC for 5 minutes . Slides were blocked for 1 hour in a humidified chamber with 5% BSA in 1x PBS and washed 3 times for 5 minutes with PBS-T ( 0 . 1% Triton X-100 in 1x PBS ) . To visualize biotinylated probe , slides were incubated in a humidified chamber at 37°C in the following layers . Layer 1: 1:100 streptavidin , Alexa Fluor-488 conjugated ( ThermoFisher , S11223 ) in 5% BSA/1x PBS for 30 minutes . Layer 2: 1:200 biotinylated anti-streptavidin ( Vector , BA-0500 ) in 5% BSA/1x PBS for 1 hour . Layer 3: 1:200 streptavidin , Alexa Fluor-488 conjugated in 5% BSA/1x PBS for 30 minutes . Layer 4: 1:200 biotinylated anti-streptavidin and 1:200 anti-ssDNA IgG2a ( EMD Millipore , MAB3034 ) in 5% BSA/1x PBS for 1 hour . Layer 5: 1:200 streptavidin , Alexa Fluor-488 conjugated , and 1:100 goat anti-mouse IgG2a , Alexa Fluor-647 conjugated ( Invitrogen , A-21241 ) , in 5% BSA/1x PBS . Three 5 minute washes with PBS-T were performed in between each layer . Slides were rinsed with 1x PBS , mounted with ProLong Gold ( ThermoFisher , P36930 ) . Digoxigenin labeled probes were synthesized with BioPrime DNA Labeling System ( Invitrogen , 18094011 ) using 500 ng BsaBI and XcmI digested HPV16 DNA . The biotinylated nucleotide mix from the kit was replaced with PCR DIG Probe Synthesis Mix ( 10x , PCR DIG Probe Synthesis Kit , Roche , 11636090910 ) and the labeling reaction incubated at 37°C for 4 hours . The DIG probe was purified using QIAquick PCR Purification Kit ( Qiagen , 28104 ) . Biotinylated probes were prepared as described previously . Following combing , DNA fibers were denatured for 20 minutes in 0 . 2 M NaOH , and neutralized five times with 1x PBS ( pH 7 . 4 ) for 1 minute . Slides were dehydrated for 3 minutes each in successive baths of 70% , 90% , and 100% EtOH and air dried for 8 minutes . Biotinylated/DIG probes in an equivalent ng/kb ratio , 200 ng of an 8 kb probe and 620 ng of a 25 kb probe , were mixed with 5 μg human Cot-1 DNA in hybridization buffer ( 50% formamide , 2x SSC , 0 . 5% SDS , 0 . 5% N-lauroylsarcosine sodium salt , 1% Blocking reagent [Roche , 11096176001] , 10 mM NaCl ) to a total volume of 20 μL . Probe mixture was added to the coverslip , a clean coverslip was placed on top and sealed with rubber cement . Slides were denatured for 5 minutes at 75°C and incubated for 16 hours at 37°C . Slides were washed three times with 2x SSC/50% formamide for 5 minutes and three times with 2x SSC for 5 minutes . Slides were blocked for 1 hour in a humidified chamber with 5% BSA in 1x PBS and washed 3 times for 5 minutes with PBS-T ( 0 . 1% Triton X-100 in 1x PBS ) . To visualize the biotin and DIG-labeled probes , slides were incubated in a humidified chamber at 37°C with the following layers: Layer 1: 1:100 streptavidin , Alexa Fluor-555 conjugated ( ThermoFisher , S21381 ) , and 1:100 anti-digoxigenin , FITC conjugated ( Roch , 11333089001 ) , in 5% BSA/1x PBS for 1 hour . Layer 2: 1:200 biotinylated anti-streptavidin ( Vector , BA-0500 ) and 1:200 anti-FITC ( Invitrogen , 711900 ) in 5% BSA/1x PBS for 45 minutes . Layer 3: 1:200 streptavidin , Alexa Fluor-555 conjugated in 5% BSA/1x PBS for 30 minutes . Layer 4: 1:200 biotinylated anti-streptavidin , 1:200 anti-rabbit , FITC conjugated ( Rockland , 611-102-122 ) , and 1:200 anti-ssDNA IgG2a ( EMD Millipore , MAB3034 ) in 5% BSA/1x PBS for 45 minutes . Layer 5: 1:200 streptavidin , Alexa Fluor-555 conjugated , and 1:100 goat anti-mouse IgG2a , Alexa Fluor-647 conjugated ( Invitrogen , A-21241 ) , in 5% BSA/1x PBS . Three 5 minute washes with PBS-T were performed in between each layer . Slides were rinsed with 1x PBS , mounted with ProLong Gold ( ThermoFisher , P36930 ) . All slides were imaged using a Leica TCS-SP5 laser scanning confocal imaging system . Measurement of signal lengths from raw Leica files was performed using Bitplane Imaris x64 . Images were analyzed in Surpass with an orthogonal camera . Measurement points of equal diameter were placed on the first and last points of signal for each HPV16 genome . Dots of signal were considered contiguous if they were within 0 . 6 μm of a previous dot . Genomes continuous in a fiber were measured with a single line of measurement points to determine the length of any interspersing DNA . Base pair length was determined using 1 μm = 2 kb conversion .
|
Oncogenic human papillomavirus ( HPV ) infection is responsible for ~5% human cancers . A key event in the development of many of these cancers is integration of the viral genome into host chromatin . Integration results in dysregulated expression of the viral oncogenes , which promotes unregulated cellular division and the accumulation of cellular mutations , ultimately leading to cancer . Genetic rearrangement and/or amplification of cellular sequences flanking sites of integration are frequent in HPV positive tumors , and this can influence transcription of the viral oncogenes from integrated loci . We show an example where the HPV16 genome integrated adjacent to a cellular , tissue-specific enhancer and subsequently the viral DNA and flanking cellular sequences were co-amplified into a tandemly interspersed viral-cellular sequence array . The genetic and epigenetic signature of this site promoted the formation of a de novo super-enhancer-like element to drive high viral oncogene expression . This provides insight into the genesis of super-enhancer-like elements and is a novel mechanism by which HPV integration can promote oncogenesis .
|
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"hpv-16",
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2018
|
HPV integration hijacks and multimerizes a cellular enhancer to generate a viral-cellular super-enhancer that drives high viral oncogene expression
|
Genetic variation in the mosquito Anopheles gambiae profoundly influences its ability to transmit malaria . Mosquito gut bacteria are shown to influence the outcome of infections with Plasmodium parasites and are also thought to exert a strong drive on genetic variation through natural selection; however , a link between antibacterial effects and genetic variation is yet to emerge . Here , we combined SNP genotyping and expression profiling with phenotypic analyses of candidate genes by RNAi-mediated silencing and 454 pyrosequencing to investigate this intricate biological system . We identified 138 An . gambiae genes to be genetically associated with the outcome of Serratia marcescens infection , including the peptidoglycan recognition receptor PGRPLC that triggers activation of the antibacterial IMD/REL2 pathway and the epidermal growth factor receptor EGFR . Silencing of three genes encoding type III fibronectin domain proteins ( FN3Ds ) increased the Serratia load and altered the gut microbiota composition in favor of Enterobacteriaceae . These data suggest that natural genetic variation in immune-related genes can shape the bacterial population structure of the mosquito gut with high specificity . Importantly , FN3D2 encodes a homolog of the hypervariable pattern recognition receptor Dscam , suggesting that pathogen-specific recognition may involve a broader family of immune factors . Additionally , we showed that silencing the gene encoding the gustatory receptor Gr9 that is also associated with the Serratia infection phenotype drastically increased Serratia levels . The Gr9 antibacterial activity appears to be related to mosquito feeding behavior and to mostly rely on changes of neuropeptide F expression , together suggesting a behavioral immune response following Serratia infection . Our findings reveal that the mosquito response to oral Serratia infection comprises both an epithelial and a behavioral immune component .
Genetic variation within populations of the An . gambiae mosquito , especially with regard to genes encoding immune factors , is believed to play an important role in the mosquito susceptibility to infection by the malaria parasite Plasmodium falciparum [1]–[3] . Many immune factors exhibit both anti-Plasmodium and antibacterial activities , such as those involved in the IMD/REL2 pathway , which is triggered by bacteria through the peptidoglycan recognition receptor PGRPLC [4] , [5] . Bacterial infections can affect mosquito survival [6] and are thought to constitute a major evolutionary drive [7] as opposed to Plasmodium infections the impact of which on mosquito fitness is unclear [8] . An example is the segregation of TEP1 alleles between the M and S molecular forms of An . gambiae in west Africa , which differentially affect Plasmodium infections , and is thought to be largely driven by bacterial pathogen pressure in larval habitats [2] . Therefore , genetic associations related to the outcome of bacterial infections may , directly or indirectly , influence mosquito vectorial capacity . The adult mosquito gut harbors a wide spectrum of bacterial populations , mainly Gram-negative enterobacteria [9]–[11] . The broad variation in gut microbiota composition observed both at the individual and population levels is probably the result of an interplay between the environmental bacterial diversity and the mosquito genetic makeup [12]–[14] . Moreover , a precipitous bacterial increase after a blood meal , whose peak coincides with midgut invasion by Plasmodium [15] , can affect the Plasmodium infection load both indirectly , by triggering PGRPLC-mediated mosquito immune responses [5] , [16] or through generation of immune memory [17] , and directly , through the generation of reactive oxygen species by specific enterobacteria that compromise malaria parasites [18] . Epithelial responses against Gram-negative bacteria have been extensively studied in Drosophila [19] . They involve recognition of peptidoglycan [20] , [21] that triggers a finely-tuned immune response mainly through the Imd pathway , resulting in the expression of antimicrobial peptides that limit bacterial populations [22] , [23] . Production of reactive oxygen species , which target bacteria , through the Dual Oxidase ( DUOX ) pathway , has also been reported [24] . Gut stem cell proliferation and epithelial cell renewal following tissue damage due to bacterial infection are regulated by the EGFR and JAK/STAT pathways [25]–[27] . However , the mechanisms involved in achieving gut homeostasis remain poorly understood . It has been suggested that regulation of Imd responses can influence the microbiota composition in Drosophila [28] . Further discrimination between commensal and pathogenic bacteria can be provided by recognition of pathogen-derived uracil , most likely by unidentified G protein-coupled receptors ( GPCRs ) , which triggers the DUOX pathway [29] , but the possibility of more specific responses that shape the gut microbiota remains open . One unexplored aspect of antibacterial immunity is the behavioral immune responses that limit or disrupt the intake of pathogens , thus making an infection more controllable by the immune system . Feeding behavior in Drosophila is known to be finely regulated through an interplay between allatostatin A and neuropeptide F ( NPF ) [30] while feeding suppression is shown to occur following an immune challenge [31]–[33] . Gustatory receptors are shown to modulate feeding behavior by acting as nutrient sensors [34] and may also be involved in aversion circuits [35] or antibacterial responses through recognition of bacterial-derived metabolites as in mammalian chemoattractant receptors [36] . Here we set out to examine the genetic basis of bacterial infection in the mosquito gut using An . gambiae infections with the Gram-negative enterobacterium Serratia marcescens that is prevalent in both lab-reared and field collected mosquitoes and is shown to affect the Plasmodium infection load [10] , [12] , [37] . To achieve this , we used an Affymetrix 400 k SNP genotyping array to identify genetic variation associated with the outcome of oral S . marcescens infection in a recently established M form An . gambiae colony . The results identify 138 genes associated with the outcome of infection , including the gene encoding the major IMD/REL2 receptor PGRPLC and the epidermal growth factor receptor EGFR , and further suggest that epithelial immune responses against gut bacteria are more complex than previously thought . We identify a set of three type III fibronectins that modulate homeostasis of the gut microbiota with specificity mainly against Enterobacteriaceae . We also present evidence that behavioral responses following S . marcescens infection can modulate the bacterial load . These data could be further exploited in mosquito microbiota-based interventions aiming to limit malaria transmission .
An . gambiae female adults were treated with antibiotics to reduce their natural gut microbiota load ( Figure S1 ) and subsequently fed with fluorescently labeled S . marcescens ( Db11-GFP ) added to the sugar meal . The bacterial levels in the gut of sugar-fed mosquitoes ( henceforth referred to as infection ) were monitored from day 2 to 6 post infection and showed considerable variation including highly and lowly infected mosquitoes as well as mosquitoes that despite ingesting bacteria-containing sugar showed no sign of fluorescence in their gut ( Figure 1A ) . While the proportion of lowly infected mosquitoes remained rather constant at approximately 50% throughout the course of the experiment , the relative proportions of highly and non-infected mosquitoes changed between days 2 and 3 in favor of highly infected mosquitoes and remained stable thereafter until day 5 ( Figure 1B ) . At day 6 , highly infected mosquitoes decreased by ca . 15% with a parallel increase of non-infected mosquitoes . To investigate whether genetic variation could partly explain the observed S . marcescens infection phenotype , single nucleotide polymorphism ( SNP ) divergence between the highly and non-infected phenotypic pools was interrogated using a 400 k SNP genotyping array . Mosquitoes were orally infected with S . marcescens , and gut infection levels were determined at day 5 post infection . The results were similar to those obtained in the previous replicate experiments: 38 . 4% of mosquitoes could be classified as highly infected , 48 . 6% lowly infected and 13% non-infected ( Figure 1C ) . Pools of equimolar amounts of genomic DNA ( gDNA ) prepared from carcasses of 15 highly infected and 15 non-infected mosquitoes out of 139 and 47 mosquitoes in each phenotypic group , respectively , were hybridized onto two Affymetrix SNP genotyping arrays . These SNP chips interrogate genetic variation at ∼400 , 000 variable positions in the An . gambiae genome ( Table S1 ) [38] , and were previously shown to provide useful quantitative information regarding divergence between pooled mosquito samples [39] . Allele calls for each SNP locus were used to determine the minor allele frequency ( MAF ) differences between highly and non-infected gDNA pools . Two approaches were used to assess genotypic association with the S . marcescens infection phenotype . The first included MAF difference at a SNP locus between highly infected and non-infected pools >0 . 5 , suggesting a preponderance of different genotypes between the two pools for the respective locus . The second involved a permutation analysis in which the average MAF difference of 10 adjacent SNP loci ( SNPs ) was compared with that of 10 random SNPs . Statistical significance was assessed for each of the ∼40 , 000 non-overlapping 10-SNP windows ( Table S2 ) and those showing a p-value<10−5 , following a Bonferroni correction for the number of tests conducted , were considered as being associated with the S . marcescens infection phenotype . The two approaches detected 140 SNPs with MAF difference >0 . 5 and 44 10-SNP windows with significant p-values , respectively . As shown in Figure 2 , these SNPs and 10-SNP windows together formed distinctive clusters along the An . gambiae genome that were designated as peaks so that they are discerned from each other , although assessed association was limited to genes within a 5 kb radius of highlighted SNPs or within genomic areas delineated by significant 10-SNP windows . Overall , 118 genes were found to reside within a 5 kb radius of highlighted SNPs ( Table S3 ) , while 27 genes fell within significant 10-SNP windows ( Table S4 ) . The two approaches combined detected 138 genes ( Table S5 ) , as there was an overlap of 7 genes between the two sets , including the highly relevant CLIPE6 and EGFR as discussed below . In peak 2L-5 ( chromosome 2L , peak 5 ) , the gene encoding PGRPLC is found within a 5 kb radius of a highlighted SNP . PGRPLC recognizes peptidoglycan and activates the IMD/REL2 NF-kappaB signaling pathway , thus eliciting antibacterial responses [21] , [40] , [41] . This pathway is constitutively triggered by mosquito gut bacteria maintaining an elevated level of antimicrobial peptide production [5] , [42] . The association of PGRPLC with the S . marcescens infection phenotype suggests that genetic variation within the mosquito population may influence the ability to mount an antibacterial response via the IMD/REL2 pathway . Adjacent to PGRPLC , in peak 2L-5 , is another peptidoglycan recognition protein encoding gene , PGRPLA . Of the remaining genes , several exhibit homologies suggesting involvement in antibacterial immune responses , especially in recognition of pathogen or host derived signals as well as in signal transduction and regulation of immune responses ( Table 1 ) . The permutation analysis revealed 3 genes , out of a total of 27 , encoding proteins with type III fibronectin domains ( FN3D ) in different peaks: FN3D1 in peak 2L-4 , which was also in the proximity of a highlighted SNP , FN3D2 in 2L-14 and FN3D3 in 2R-4 . A total of 65 An . gambiae genes contain FN3 domains , including the hypervariable pattern recognition receptor AgDscam , the insulin receptor INR and the JAK/STAT receptor DOME . FN3D2 and FN3D3 additionally possess immunoglobulin and putative transmembrane domains , while FN3D2 is an ortholog of Drosophila Dscam4 . Drosophila Dscam is shown to bind bacteria and influence the efficiency of phagocytosis [43] , while its An . gambiae ortholog , AgDscam , is also shown to bind bacteria and mediate antibacterial and anti-Plasmodium responses [44] . Importantly , Dscam genes in various organisms generate a diverse repertoire of isoforms , suggestive of challenge-specific pattern recognition through alternative splicing [43] , [45] , [46] , with particular AgDscam isoforms specifically targeting P . berghei , P . falciparum or commensal bacteria [47] , [48] . Several putative transcription factors with homeobox-like or DNA-binding domains were found in the identified peaks . AGAP005096 in 2L-4 and AGAP005244 in 2L-5 ( together with PGRPLC and PGRPLA ) encode homeodomains . The homeobox gene , Caudal , has been previously implicated in the regulation of epithelial immune responses and shown to influence the gut bacterial population structure in Drosophila [28] , while its mosquito homolog has been shown to regulate the IMD/REL2 pathway [49] . Thus , these putative transcription factors could play similar regulatory roles . AGAP002492 in peak 2R-7 encodes a DNA-binding domain , while its Drosophila ortholog ewg is involved in the Wnt/Wingless pathway [50] . AGAP005156 , in peak 2L-4 encodes an ARID/BRIGHT DNA-binding domain , with its Drosophila ortholog , retained , is involved in behavioral modulations and repression of male courtship [51] , [52] . AGAP005661 , in peak 2L-7 , a putative ligand-regulated transcription factor , is an ortholog of the Drosophila nuclear receptor FTZ-F1 , involved in juvenile hormone mediated gene expression [53] . Genes encoding alpha-glucosidase and alpha-mannosidase homologs were detected in peaks 2R-1 and 2R-13 , respectively . These genes possess glycoside hydrolase domains that are also present in the conserved chitinase gene family [54] , involved in bacterial clearance and host tolerance [55] . The gene encoding the epidermal growth factor receptor , EGFR , was identified in the prominent peak 3R-6 both by both the permutation and the individual SNP analysis . The Drosophila EGFR pathway has been implicated in gut remodeling following oral bacterial infection [27] , suggesting that the EGFR pathway may influence the outcome of S . marcescens infection in Anopheles , possibly through synergistic functions in gut homeostasis . CLIPE6 and CLIPE7 , found in peak 3L-16 , belong to the non-catalytic E sub-family of CLIP-type serine proteases , a family known to participate in proteolytic cascades in antibacterial and anti-Plasmodium responses [56] , [57] , with SPCLIP1 , another E sub-family member , involved in anti-Plasmodium responses by regulating complement recruitment [58] , [59] . Several leucine-rich repeat containing genes were also detected , including LRIM15 ( peak 2L-13 ) , a transmembrane member of the LRIM family of immune proteins [60] . LRIMs have also been implicated in complement anti-Plasmodium responses [61]–[63] . Two Toll-like receptors , TOLL1A and a previously uncharacterized paralog of TOLL5B , were found in peak X-4 . Little is known about the role of Toll-like receptors in Anopheles immunity , however , cross-talk between the REL1 and REL2 signaling pathways in the yellow fever mosquito Aedes aegypti [64] and synergistic interactions between the Toll and Imd pathway in Drosophila [65] , leave open the possibility for involvement of Toll-like receptors in defenses against Gram-negative bacteria , also in Anopheles [66] . A gene encoding a protein with a ricin B lectin domain was found in peak 2R-15 . Lectins bind oligosaccharides and have been shown to modulate mosquito immune responses [6] , [63] , while mammalian lectins modulate host and gut microbiota interactions [67] . Genes belonging to other families of putative pattern recognition receptors were also found to be associated with the S . marcescens infection phenotype , including a fibrinogen-related protein ( FBN or FREP ) and a galectin in peak 3L-10 and an MD2-like receptor in 2L-16 [59] , [68] , [69] . Five annotated or putative GPCRs were found to be associated with the S . marcescens infection phenotype , including three putative neurotransmitter-triggered receptors: the serotonin receptor GPR5HT7 in peak 2R-14 , the GABA-B family receptor GPRGBB1 in peak 3R-15 and the neuropeptide receptor GPRNPR2 in 3R-5 . GPCRs have been previously implicated in modulation of P . falciparum infection in An . gambiae [70] , but the mechanism by which this is accomplished remains unclear . NPR-1 , a neurotransmitter-triggered GPCR of Caenorhabditis elegans , has been shown to modulate antibacterial defenses in a behavior dependent or independent manner , and NPR-1 genetic polymorphisms are suggested to be major determinants of bacterial susceptibility [71] , [72] . Serotonin is a major modulator of mammalian intestinal inflammation [73] , [74] , in an interplay between the nervous and immune system [75] . The Drosophila ortholog of GPR5HT7 is involved in various behavioral processes [76] , [77] , including aggressive behavior , a process also modulated by NPF [78] . Interestingly , the Drosophila ortholog of GPRGBB1 has been implicated in behavioral responses to alcohol sensitivity [79] , a process in which NPF is also a major modulator [80] , [81] . Two gustatory receptor genes , Gr9 and Gr10 , encoding 7-transmembrane chemoreceptor domains , were associated with the outcome of S . marcescens infection ( Figure 2 , peak 3R-11 ) . Gr9 and Gr10 are paralogs and show co-orthologous relationships with the Drosophila Gr32a , Gr39a and Gr68a [82] . Gr32a and Gr68a act as pheromone receptors in modulating mating behavior [83] , [84] , while Gr39a has been implicated , through 4 splice variants , in sustaining courtship behavior [85] . Gr32a is also involved in regulating aggressive behavior through recognition of small non-volatile hydrocarbons [86] , or feeding suppression triggered by DEET or other antifeedants [87] . Gustatory receptor family members have also been implicated in aversive taste [35] , [88] , CO2 responses [89] , [90] and sugar recognition [91]–[94] . A Drosophila gustatory receptor , Gr43a , has been shown to recognize fructose and act as a nutrient sensor , promoting or suppressing feeding [34] . Since enhanced or suppressed feeding of bacteria-containing sugar can decisively influence the abundance of S . marcescens that the mosquito takes in and its immune system can handle , it is possible that Gr9 or Gr10 variants linked to altered mosquito feeding behavior can affect the outcome of infection . Furthermore , GPR43 , a mammalian chemoattractant receptor , has been shown to recognize short-chain fatty acids of bacterial origin and participate in antibacterial responses [36] , while other mammalian chemoattractant receptors regulate inflammatory responses by recognizing endogenous factors [95] . Recognition of bacterial-derived uracil has recently been shown to modulate Drosophila antibacterial responses through the DUOX pathway [29] . Therefore , another possibility is that Gr9 or Gr10 recognize bacterial-derived metabolites or infection-induced mosquito molecules and mediate antibacterial responses . Several other genes with no known or unrelated to immune responses homologies were also associated with the S . marcescens infection phenotype such as AGAP013684 in peak 2R-8 , encoding a putative miRNA . MiRNAs are known to modulate gene regulation in processes that include epithelial immunity [96] , [97] . AGAP006405 in peak 2L-10 encodes a tyrosine protein kinase , while its Drosophila ortholog , derailed2 , is involved in Wnt5 signaling and establishment of olfactory circuits [98] . In peak 2L-15 the inhibitors of apoptosis IAP4 and IAP5 were found . The Drosophila IAP2 is known to regulate Imd signaling [99] , suggesting that the An . gambiae IAP4 or IAP5 may also play similar roles . AGAP012252 , in peak 3L-19 , encodes the ortholog of Drosophila PKC53E , implicated in NPF-mediated alcohol sensitivity [100] , [101] . AGAP011363 , in peak 3L-11 , encodes the ortholog of Drosophila rab6 , implicated in phagocytosis [102] but also trafficking of Grk , the EGFR ligand [103] , [104] . AGAP010503 , in peak 3L-4 , encodes the ortholog of the Drosophila SK channel , implicated in behavioral courtship memory [105] . AGAP005216 , in peak 2L-5 , encodes the ortholog of Drosophila fab1 , involved in autophagy but also the lysosomal degradation of necrotic , a modulator of the Toll pathway [106]–[109] . Candidate gene prioritization for further phenotypic analysis was based on homologies with genes known to be involved in species-specific antibacterial responses , e . g . FN3D2 and Dscam [43] or demonstrably regulating the response to gut microbiota in other systems , e . g . Gr9 and the mammalian chemoattractant receptor GPR43 [36] , with the aim of the identification of novel functions of genes or gene families in antibacterial responses . The involvement of the three FN3D genes in shaping the outcome of An . gambiae gut infection with S . marcescens was investigated by RNAi-mediated gene silencing ( Figure 3 ) . Antibiotic treated mosquitoes were orally infected with S . marcescens following knockdown ( kd ) of each of the FN3Ds ( Figure S2 ) . The bacterial load in mosquito guts was determined 5 days post infection by quantitative RT-PCR ( qRT-PCR ) , using both broad range bacterial 16S and Serratia-specific primers . Highly significant and robust increase of the S . marcescens load was observed after silencing any of the three genes compared to dsLacZ-treated controls: 21 to 53-fold in FN3D1 ( Figure 3A ) , 41 to 60-fold in FN3D2 ( Figure 3B ) and 13 to 29-fold in FN3D3 kd ( Figure 3C ) . We also assessed the role of FN3Ds in shaping the load of Serratia naturally found in the mosquito gut . Mosquitoes reared in standard conditions , without antibiotic treatment or infection with S . marcescens , were treated with dsRNA against each of FN3D1–3 and the level of commensal Serratia was determined 5 days later ( Figure 3 A–C , last bar in each panel ) . Silencing any of the three genes resulted in a significant 4 to 8-fold increase in the levels of commensal Serratia compared to dsLacZ-treated controls . These data indicate the involvement of FN3D1–3 in constitutive antibacterial effects that shape the load and composition of the mosquito natural gut microbiota . When the effect of FN3D1–3 kd was assessed on the total bacterial load in the gut of mosquitoes that retained their natural gut microbiota , a non-uniform effect was observed between 4 independent replicate assays ( Figure S3 ) . In some cases , FN3D silencing resulted in moderate increases of both Serratia and total bacterial load , while in other cases the total bacterial load showed no or marginal increase while Serratia showed a strong increase . This variability suggested that the FN3D effect on total bacteria may depend on the initial Serratia load and that FN3Ds may function in shaping the population structure of the gut microbiota by affecting a subset of bacteria inhabiting the mosquito gut , including Serratia . To further investigate these hypotheses , we carried out a microbiome analysis using 454 pyrosequencing of samples from two of the replicate assays in which FN3D1–3 kd increased Serratia but not total bacteria abundance ( Figure 4A–B ) and from a replicate assay in which FN3D3 kd increased both Serratia and total bacterial load ( Figure 4C ) . The resulting sequence reads were assigned to their respective bacterial family . Reads aligning to Serratia reference sequences were categorized separately from other Enterobacteriaceae ( Table S6 ) . Considerable variation in bacterial composition was observed in control gut pools between the three assays ( Figure 4 ) . This variation is consistent with previously reported metagenomic analyses in lab-reared and field-collected mosquitoes , which revealed extensive gut microbiota diversity at both the individual and population levels [12]–[14] . Total Enterobacteriaceae ( Serratia and other Enterobacteriaceae ) were highly prevalent in all pools corresponding to 83 . 2% , 44 . 2% and 47 . 5% of total reads , respectively , while significant variation was observed in the specific representation of Serratia that corresponded to 1 . 9% , 24 . 5% and 9 . 5% of total sequence reads , respectively . This natural Serratia variation is consistent with the variation observed following oral infection with Db11-GFP S . marcescens ( see Figure 1 ) and may be related to the underlying genetic variation . Acetobacteriaceae was a prominent family in all assays , while Flavobacteriaceae was the prevailing family in the second assay . In the first assay , FN3D1 or FN3D2 kd increased the representation of total Enterobacteriaceae to 87 . 6% and 89 . 6% , respectively ( Figure 4A ) . Remarkably , silencing FN3D1 or FN3D2 resulted in a dramatic increase in Serratia representation from 1 . 9% in the dsLacZ-treated control to 30% and 39 . 3% of total sequence reads , respectively , in agreement with the qRT-PCR analysis of the same samples ( Figure S3 ) . Similar results were obtained in the second assay whereby silencing FN3D2 or FN3D3 resulted in an increase in total Enterobacteriaceae representation , from 44 . 2% to 83 . 1% and 69 . 7% , respectively ( Figure 4B ) . In both cases , Serratia representation showed a precipitous increase from an initial intermediate level of 24 . 5% , to almost all Enterobacteriaceae sequence reads aligning to Serratia reference sequences , again in consistence with the qRT-PCR analysis ( Figure S3 ) . Although non-Enterobacteriaceae representation decreased in both FN3D2 and FN3D3 kd , Flavobacteriaceae persisted following FN3D2 kd but were completely eliminated following FN3D3 kd , indicating a difference in the effect between the two FN3Ds related to non-Enterobacteriaceae strains . Taken together , these data indicate that FN3Ds indeed play a major role in shaping the population structure of the mosquito gut microbiota , as silencing any of FN3D1–3 led to increased Serratia abundance but also shifted the composition of the mosquito gut microbiota in favor of Enterobacteriaceae , mainly Serratia or strains that show similarity to Serratia reference sequences . This shift may be a result of a specific FN3D function against Serratia or a subset of gut bacteria . Alternatively , bacterial interactions or differential growth potential of different bacterial strains may account for the observed shift following a uniform FN3D antibacterial effect . We tested this hypothesis by examining whether FN3D1–3 silencing could affect the levels of gut infection with non-Enterobacteriaceae . Antibiotic treated dsLacZ treated controls and FN3D1–3 kd An . gambiae mosquitoes were orally infected with bacteria of the genus Asaia , a member of the Acetobacteriaceae family , common in both field and laboratory-reared An . gambiae [9]–[11] and present in all of our sequenced samples . FN3D1–3 silencing resulted in moderate , non-significant increases in bacterial load , compared to controls ( Figure S4 ) , distinguishably lower than following oral S . marcescens infection ( Figure 3 ) . These data suggest that the observed FN3D antibacterial effect is not uniform across all Gram-negative bacteria and may be specific to a subset of the gut bacterial population including Enterobacteriaceae . The observed shift in favor of Enterobacteriaceae representation when both Serratia and total bacterial abundance increased following FN3D3 kd was also confirmed by microbiome sequencing that showed an increase of Serratia from 9 . 5% to 33 . 7% of total sequence reads and of total Enterobacteriaceae from 47 . 5% to 66% ( Figure 4C ) . Remarkably , FN3D3 kd also increased the representation of bacteria of the genus Burkholderia , from an initial 0 . 71% to 15 . 1% of total reads ( Figure 4C ) . Burkholderia were not traced in the dsLacZ treated control pool in which the effect of FN3D3 kd was also assayed ( Figure 4B ) . These data suggest that FN3D3 limits a subset of the mosquito gut bacterial community including Enterobacteriaceae but also bacteria of the genus Burkholderia . The genomic area encompassing genes encoding the gustatory receptors Gr9 and Gr10 was associated with the outcome of S . marcescens infection . As alternative splicing of Gr9 has been previously suggested [82] , with Gr9 possessing 13 splice variants compared to one for the adjacent Gr10 , we considered Gr9 genetic variation more likely to influence the outcome of S . marcescens infection , leading to the observed SNP divergence . Gr9 has shown significant upregulation compared to other tissues in the midgut of blood-fed adult mosquitoes [110] and also in the midgut of adult mosquito tissues [111] . The Gr9 midgut expression was also confirmed here ( Figure S2 ) . Furthermore , comparison of transcription profiles between antennae or maxillary palps and whole body transcriptomes in female mosquitoes has previously shown a non-significant upregulation of Gr9 in those two tissues ( 1 . 42 for antennae and 1 . 15 for maxillary palps ) [112] . We carried out RNAi-mediated silencing of Gr9 in adult mosquitoes and examined the outcome of oral S . marcescens Db11-GFP infection . Gr9 knockdown resulted in a precipitous 36 to 48-fold increase in S . marcescens levels compared to dsLacZ treated controls , as determined using both broad range 16S and Serratia-specific primers ( Figure 5A ) . These data suggest that Gr9 exerts an antibacterial effect that influences the outcome of S . marcescens infection . We next examined the possibility that the Gr9 antibacterial effect is related to changes in mosquito feeding behavior . Based on the Gr9 many-to-many orthologous relationship with the Drosophila Gr32a , Gr39a and Gr68a , it would be more likely that any Gr9 effects on mosquito behavior would be exerted through the recognition of mosquito-induced or bacterial-derived molecules rather than nutrient sensing [83]–[85] , [87] . Therefore we first examined the possibility that Gr9 mediates aversion to bacteria-containing sugar thus limiting sugar meal uptake upon oral S . marcescens infection . A two-choice preference assay , in which mosquitoes were offered to feed from a capillary that contained S . marcescens and another that contained only sugar , indicated that there was no significant difference due to Gr9 silencing in consumption between the two capillaries ( Figure S5 ) . Another possibility that could explain the Gr9 antibacterial effect is that Gr9 modulates meal size irrespective of the presence of bacteria . Antibiotic treated mosquitoes were starved and then offered a sugar meal . Consumption was determined 16 hours later in LacZ or Gr9 dsRNA treated mosquitoes ( Figure 5B ) . Indeed , Gr9 silencing resulted in a significant 1 . 45-fold increase in meal size compared to dsLacZ treated mosquitoes . An increased meal size could result in higher S . marcescens uptake following oral infection , thus contributing to the precipitous increase in S . marcescens load following Gr9 silencing . Our data suggest that Gr9 influences feeding behavior by triggers that do not rely on the presence of bacteria . As the presence of S . marcescens does not seem to affect sugar uptake following Gr9 silencing , there is no reason to assume that the presence of S . marcescens influences the Gr9 effect on meal size , although Gr9-independent aversion circuits could conceivably taper overall consumption . To examine the relationship between genes identified in the population genetics analysis to be associated with the S . marcescens infection outcome and infection-induced transcriptional responses , we used DNA microarrays to monitor the transcriptional profile of mosquito guts 3 days post infection with S . marcescens added to the sugar meal . Uninfected mosquitoes , which were also treated with antibiotics , were used as controls . Three independent replicate infections were performed . Overall , 55 and 44 transcripts were found to be up and down regulated by at least 1 . 75-fold , respectively , with 38 and 28 respective up or down regulated transcripts yielding a significant p-value in a t-test against zero , where zero corresponds to no transcriptional regulation ( Figure 6A and Table S7 ) . Functional classification of all 97 differentially regulated genes , accounting for multiple transcripts of the same gene , identified serine-type endopeptidases and protein/receptor binding as the most represented classes ( Figure 6B ) . The protein/receptor binding functional class comprised 12 members , including several up or downregulated FREPs , zinc finger containing proteins , PGRPLC and the complement factor regulator LRIM1 , which has been previously shown to be regulated by the IMD/REL2 pathway [113] . The oxidoreductase class comprised 7 members , including two P450 cytochromes , possibly involved in detoxification [114] , the hydrolase class included a glycoside hydrolase and the nucleotide metabolic process class included 5 heat shock proteins , likely to be involved in stress responses [115] . The antimicrobial peptide LYSC2 , showing the highest 3 . 44-fold upregulation of all genes , has been previously shown to be upregulated following a bacterial challenge [116] . A hypergeometric test followed by Benjamini-Hochberg correction was used to determine enriched GO terms in the set of 97 genes . The results identified 16 GO terms that were significantly overrepresented , most of which were related to just two functional classes: serine-type endopeptidases and chitin-binding genes ( Figure S6 and Table S8 ) . In total , 16 serine-type endopeptidase genes were differentially regulated , including CLIPE6 , which was also associated with the outcome of infection , CLIPB14 that has been implicated in defense against Gram-negative bacteria [57] , [117] , CLIPB17 and CLIPB20 . The group of chitin-binding genes comprised 5 members , including the gene encoding the scavenger receptor SCRASP1 , previously shown to be upregulated following bacterial infection and bind chitin [117] , [118] and two downregulated peritrophic matrix components identified by a previous proteomic analysis [119] . Chitin-binding genes are upregulated following oral bacterial infection in Drosophila [26] , with one member participating in barrier formation that protects against oral S . marcescens infection [120] , while their suggested role in mosquitoes is recognition of danger signals following tissue remodeling due to a bacterial infection [118] . Several transcriptionally regulated genes suggested a mosquito behavioral response following S . marcescens infection ( Table 2 ) . Among these genes , NPF was downregulated after infection . NPF is expressed in the midgut of Drosophila [121] and Aedes aegypti [122] , and has been implicated in modulation of feeding behavior in Drosophila [30] , aversion to noxious food [123] as well as in alcohol sensitivity [80] and regulation of reward systems [81] . It has been also linked to food signaling by integrating sugar gustatory stimuli [124] and behavioral immune responses against endoparasitoid wasps , by mediating oviposition behavior [125] . Additional behavior-related genes that were transcriptionally regulated following S . marcescens infection included the gustatory receptor Gr13 with two downregulated transcripts , three upregulated juvenile hormone-inducible kinases , two downregulated genes encoding a pheromone and a juvenile hormone binding protein and the downregulated odorant binding protein genes OBP13 and OBP54 . Juvenile hormone circuits are known to affect gustatory perception and feeding behavior in various organisms including Ae . aegypti [126]–[129] , while pheromone and olfaction circuits are also known to affect mosquito behavior [130] , [131] . The gene encoding the juvenile hormone binding protein TO2 ( takeout2 ) , was also upregulated following S . marcescens infection and may also participate in behavioral responses , as its Drosophila homolog , takeout , is known to regulate feeding behavior [132] , [133] . We examined a possible link between the observed downregulation of the known modulator of feeding behavior , NPF , following S . marcescens infection and the role of Gr9 in modulating the S . marcescens infection outcome . The Gr9 ortholog Gr39a has been previously shown to be expressed in the Drosophila midgut and co-localize with NPF in enteroendocrine cells [134] , raising the possibility of a functional link between these two genes . In mosquitoes orally infected with S . marcescens , NPF expression showed a significant 1 . 8-fold increase following Gr9 silencing compared to dsLacZ treated controls ( Figure 7A ) . This modulation of NPF expression following Gr9 silencing suggested that NPF expression may mediate the observed Gr9 antibacterial effect . Therefore , we further examined whether silencing NPF can affect the increase of S . marcescens observed in Gr9 kd mosquitoes . Indeed , concomitant silencing of Gr9 and NPF resulted in a significant 10-fold decrease in infection load , compared to Gr9 silencing alone ( Figure 7B ) . Taken together , our data suggest a behavioral immune response involving Gr9 , mostly relying on changes of NPF expression . One hypothesis is that Gr9 activation in the midgut , most likely through a mosquito-induced cue , tapers the expression of NPF , resulting in feeding suppression that limits the mosquito meal size and thus the abundance of ingested S . marcescens . Gr9 variants that influence the efficiency of this suppression may lead to enhanced feeding which , depending on the efficiency of the epithelial response to handle the infection , can influence the outcome of S . marcescens infection , thus explaining the observed Gr9 association with the S . marcescens infection phenotype .
The rapidly evolving and adapting mosquito species have become tractable systems for genetic association studies that could yield important information about vector/parasite interactions leading to malaria transmission [135] . Previous studies have focused on the outcome of Plasmodium infections , using laboratory or field mosquitoes and genetic tools such as microsatellite markers and targeted SNP loci genotyping [1] , [3] , [136] . These studies have not considered the effect of gut bacteria on the outcome of Plasmodium infections , which has been revealed recently [5] , [16]–[18] . Furthermore , the influence of associated complement factors on natural P . falciparum infections remains questionable [137] . Indeed , the presence of Enterobacteriaceae , such as S . marcescens , a common member of the mosquito gut flora , has been correlated with P . falciparum susceptibility in field mosquito populations [12] , while intraspecific variation within S . marcescens populations also is shown to affect the Plasmodium infection load [37] . Therefore , genome-wide studies to determine factors that modulate the levels of mosquito gut bacteria can provide novel insights into how midgut bacteria affect the outcome of Plasmodium infection and hence malaria transmission . The unprecedented level of detail achieved in the population genetics analysis presented here in identifying SNPs associated with the outcome of S . marcescens infection is a result of the strong evolutionary drive exerted by gut bacteria on mosquito genetic variation , the use of a high-resolution SNP genotyping array and the use of a recently established laboratory colony of An . gambiae which retains genetic variation found in field populations but also shows elevated linkage disequilibrium due to colonization bottlenecks . This population homogeneity can facilitate gene discovery as shown in human genome-wide association studies in isolated populations [138] , [139] . A dual implication can be inferred for genes associated with the S . marcescens infection phenotype; they are putatively involved in shaping the infection outcome , while their level of involvement may also be affected by genetic variation within the mosquito population . It is possible that identified associations are the result of causal polymorphisms such as gain or loss of function mutations in coding or regulatory sequences or the result of allele combination in several genetic loci which shapes the outcome of infection through synergism , epistatic interactions or redundant function . In any of the latter cases , a reverse genetics approach may not be capable of capturing such interactions . The involvement of the three FN3Ds in the outcome of Serratia infection reveals a novel function of this family in modulating the load and composition of the mosquito gut microbiota and opens new avenues in investigating the complexity of such responses and possible synergisms with known antibacterial pathways such as the IMD/REL2 . The three FN3D genes identified here emerge as major modulators of the bacterial population structure in the mosquito gut , limiting the representation of Enterobacteriaceae , mainly Serratia or strains with similarity to Serratia reference sequences , but also , for FN3D3 , bacteria of the genus Burkholderia . As shifts in gut microbiota population structure can elicit gut pathology [28] , [140] , while Serratia can influence the outcome of Plasmodium infection [37] , FN3Ds can play critical roles in gut homeostatic interactions and malaria transmission dynamics . Further insights into the FN3D mode of action remain to be determined . Our data showing that the knockdown effects of FN3Ds may be limited to Serratia or to a fraction of the microbiota raise intriguing questions about the specificity of bacterial recognition in the mosquito gut . The homology of FN3D2 with the hypervariable pattern recognition receptor Dscam opens the possibility that the specific pathogen recognition shown for AgDscam [47] concerns a broader family of FN3Ds , equipping mosquitoes with the capacity for specific recognition resembling that of animals possessing adaptive immune systems . The phylogenetically unrelated FN3D2 and FN3D3 share a similar domain architecture comprising immunoglobulin and FN3 domains , as is the case with Dscam . The identification of FN3D2 and FN3D3 as being both associated with the outcome of S . marcescens infection and exhibiting discrete but similar phenotypic characteristics in modulating the bacterial population structure in the mosquito gut , parallels the discrete but similar functions of the phylogenetically unrelated Dscam , Frazzled and Roundabout in Drosophila axon guidance , with all three receptors sharing immunoglobulin and FN3 domains [141]–[144] . FN3D1 has a distinct domain architecture with an FN3 domain , while its orthologous relationship with Drosophila windei [145] and sequence similarity with the activating transcription factor 7- interacting protein [146] , suggest a role in regulating gene expression . The identification of An . gambiae genes involved in immune responses against bacteria and/or Plasmodium has been largely based to date on studies that combine bioinformatic identification of known immunity gene homologs and transcriptional profiling of genes following a pathogen challenge . This approach , however , has the limitation of the a priori assumption that genes of interest show significant change in transcriptional regulation , mostly induction , which is true for most effectors , but not all genes , for example pattern recognition receptors or transcription factors . In addition , it is possible that even strong changes in transcriptional regulation are the consequence of the infection rather than part of the response . Especially for quantitative traits within mosquito populations , such as Plasmodium infection intensity , different infection intensities can correlate with variable transcriptional responses [70] , while the underlying genetic variation further complicates the observed transcriptional regulation . The microarray approach adopted here has identified a limited set of 99 differentially regulated transcripts following oral S . marcescens infection . The number of regulated transcripts is consistent with that of a previous microarray-based comparison of antibiotic treated and untreated mosquitoes , which showed differential expression for 185 transcripts [16] , attributing this limited transcriptional regulation to symbiotic relationships that have led to adaptation of commensal bacteria . A much broader set of differentially expressed genes has been identified following oral bacterial infections in Drosophila [26] , [32] . This is most likely due to differences in gene pool diversity between the genetically homogeneous fly lines and the recently established mosquito laboratory colony used here , which retains considerable genetic variation thus enabling the SNP genotyping analysis . The different levels of infection seen between mosquitoes ( high , low and no infection ) , which are largely attributed to genetic variation within the colony population , are most likely linked to differences in the mosquito transcription profiles that are averaged out in our study design . Therefore , our analysis identifies transcripts with the most pronounced and consistent differential expression , comprising the core response to S . marcescens infection . Future studies investigating the transcription profile of highly , lowly or non-infected mosquitoes are most likely to reveal components of transcriptional regulation that lead to the respective outcome of infection . Indeed , genes identified to show prominent differential expression after bacterial challenge in previous studies also showed transcriptional regulation following oral S . marcescens infection , including CLIPB14 [117] , LRIM1 [63] , [113] , LYSC2 [116] and SCRASP1 [117] , [118] . The identification of diverse transcriptional responses to different bacteria in Drosophila [32] along with the specificity of mosquito responses to a subset of bacteria , as suggested by the SNP genotyping analysis presented here , may explain the surprisingly little overlap between differentially expressed genes following S . marcescens infection and antibiotic treated vs . untreated mosquitoes [16] . Remarkably , however , consistency is seen in gene families present in both datasets , including CLIPs , chitin-binding genes , homeobox genes , PGRPs and FREPs , suggesting that similar defense strategies are employed , which are customized for each type of infection through utilization of different gene family members . The approach we adopted here to identify genes involved in mosquito gut infection with S . marcescens combines transcriptional profiling of infected guts with the identification of SNPs segregating between phenotypic pools , whereby an association implies contribution to the outcome of infection , while the study design incorporates variation that leads to different observed phenotypes . This approach addresses some of the aforementioned shortcomings but introduces others , as it cannot capture genes with redundant functions , genes with additional housekeeping functions or a role during development , of which variants are eliminated from the population , or genes with rare variants that are not in the variation pool of our colony . Furthermore , an association may be the result of a selective sweep in the proximity of the gene that creates linkage disequilibrium and leads to SNP divergence between the phenotypic pools . Therefore , although each of the approaches cannot provide by itself a complete picture , the combination of the two can provide novel insights into the mosquito gut responses to S . marcescens . The comparison between the datasets of transcriptionally regulated genes and genes associated with the outcome of S . marcescens infection shows limited overlap , with only PGRPLC and CLIPE6 found in both datasets . Again , considerable overlap is detected in identified gene families , which are represented by different members in each dataset . These include acyl-transferase , glycoside hydrolase , kinase , GPCR , LRIM , homeobox , zinc-finger , PGRP , peptidase , FREP , MD2-like and chitin-binding genes . Interestingly , a previous study investigating differential expression following a bacterial challenge in mosquito immunoglobulin-containing genes failed to identify significant regulation for FN3D2 or FN3D3 [147] , strengthening the case for the complementarity of the SNP genotyping and expression analysis approaches . The specific role of gene family members , especially those showing considerable expansion in Anopheles , e . g . FREPs [68] , [148] , remains unclear . Therefore , SNP genotyping reveals a different set of candidate genes involved in antibacterial immunity while at the same time it is intriguing to postulate whether this divergence between associated and differentially expressed genes within each gene family constitutes a functional divergence between them . A novel finding stemming from this combinatorial approach is a mosquito behavioral response to S . marcescens infection that involves Gr9 signaling and is mediated by changes of NPF expression . Although Gr9 orthologs in Drosophila recognize chemosensory cues and mediate aversive behaviors [83] , [84] , [87] , surprisingly , Gr9 appears to suppress feeding irrespective of the presence of bacteria . One explanation is that the Gr9 antibacterial effect relies on its expression in the midgut rather than external sensory organs , where the role of its Drosophila counterparts has been studied . The role of gustatory receptor midgut expression [134] remains poorly understood and could involve detection of nutrients or host-derived molecules that triggers downstream responses . The role of NPF midgut expression [121] , [122] also remains poorly understood . NPF downregulation following S . marcescens infection implies its involvement in an aversion circuit triggered by the presence of S . marcescens , with a possible NPF role in integrating aversion and satiation signals that lead to feeding suppression . Such NPF involvement remains to be further investigated , in conjunction with the involvement of other genes related to mosquito behavior which were either associated with the outcome of S . marcescens infection or were transcriptionally regulated following infection . These include Gr13 , downregulated following S . marcescens infection but also three neurotransmitter-triggered GPCRs , associated with the outcome of infection , pointing to complex behavioral circuits involved in antibacterial responses , which are yet to be revealed . The identification of FN3Ds as well as Gr9 and NPF in responses affecting the outcome of S . marcescens infection , in addition to known responses including the IMD/REL2 and DUOX pathways , suggests that the response to gut infection is the result of a complex molecular interplay . Both the SNP genotyping and expression analysis suggest that the mosquito response to oral S . marcescens infection involves two discrete but inextricably linked modes of defense , a behavioral and an epithelial immune response . A behavioral immune response involving Gr9 and NPF can limit or disrupt pathogen intake , a defense conceptually similar to barrier responses that inhibit pathogen contact with triggers of epithelial or systemic immune responses . An impaired behavioral response , e . g . due to Gr9 variants that affect feeding behavior , can decisively influence the efficacy of the epithelial response and thus the infection outcome . This implies a threshold after which epithelial immunity cannot efficiently handle the pathogen load , an aspect of immunity that remains poorly understood . Nevertheless , in mosquito infections with Plasmodium parasites , the intensity of infection has been correlated with the efficacy of different components of the IMD/REL2 pathway , suggesting that different effectors may be deployed in low , mid or high intensity parasite infections [4] . As pathogen abundance most likely relies on feeding behavior , the interplay between behavioral and epithelial immunity can shape both responses . Our implementation of a model of natural bacterial infections through the oral route integrated both behavioral and epithelial responses and not only revealed the previously unknown behavioral component but also allowed the study of aspects of epithelial immunity that , by being infection intensity dependent , possibly rely on the behavioral component . This integrative approach to behavioral and epithelial immunity can be further employed to reveal aspects of this interplay that may involve regulation of behavioral responses by host-derived factors induced by the epithelial component . This implies that the study of behavioral immunity alone may be insufficient to uncover some aspects of its biological consequences . In Drosophila , a balance between immune response and tolerance , achieved by various Imd regulators , largely shapes the gut microbiota population structure , although the only known elicitor of such responses is DAP-type peptidoglycan , common to all Gram-negative bacteria [28] , [149] , [150] . A similar mechanism has been suggested for mosquitoes through alternative splicing of the modular IMD/REL2 pathway receptor PGRPLC that leads to production of positive and negative pathway modulators [5] , [42] . Indeed , utilization of alternative splicing as a mechanism to derive new immune functions and increase the specificity of pathogen recognition by the mosquito innate immune system has been described for the FN3D2 homolog , Dscam [43] , [44] , [47] . Whether the Enterobacteriaceae-specific effect of FN3D2 knockdown is due to specific recognition and activation of highly specialized or targeted effector reactions remains to be investigated . Furthermore , the significance of alternative splicing suggested for Gr9 [82] remains to be determined along with the cue that triggers its antibacterial effect , and could also involve recognition of , most likely , host-derived signals . In addition , recognition of differentially produced metabolites after infection as shown for the DUOX pathway [29] could further increase the specificity in antibacterial responses . Whether PAMPs ( bacterial-derived ) or DAMPs ( host-derived ) , such metabolites can be recognized by gustatory receptors triggering specific antibacterial responses , which together with FN3Ds and the rather generalist response of the IMD/REL2 pathway can shape the load and composition of the mosquito gut microbiota . In conclusion , our findings suggest that mosquitoes can mount a much more complex and specific antibacterial response than previously thought , which not only contributes to fending off intestinal bacterial infections but also to achieving homeostasis of the complex gut ecosystem .
This study was carried out in strict accordance with the United Kingdom Animals ( Scientific Procedures ) Act 1986 . The protocols for maintenance of mosquitoes by blood feeding were approved and carried out under the UK Home Office License PPL70/7170 . The procedures are of mild to moderate severity and the numbers of animals used are minimized by incorporation of the most economical protocols . Opportunities for reduction , refinement and replacement of animal experiments are constantly monitored and new protocols are implemented following approval by the Imperial College Ethical Review Committee . The N'gousso strain of An . gambiae was used . This is an M form strain colonized in 2006 [1] and kept in large numbers to retain genetic variation . Rearing and maintenance of the strain was performed as described previously [151] . Mosquitoes were collected after emergence and kept on a cocktail of 25 µg/ml gentamicin , 10 µg/ml penicillin and 10 units/ml streptomycin , diluted in 10% D- ( - ) -Fructose ( Sigma ) . This antibiotic treatment regime was used for 5 days , with the antibiotic solution refreshed every 24 hours . At day 5 post emergence , the antibiotic solution was replaced by dH2O and mosquitoes were starved overnight prior to oral bacterial infection . We used the S . marcescens Db11-GFP strain , modified to be GFP-fluorescent and resistant to tetracycline and carbenicillin [152] . S . marcescens glycerol stock was grown in 5 ml LB cultures containing 50 µg/ml tetracycline and carbenicillin ( Sigma ) at 37°C . Following overnight incubation , the cultures were expanded to 100 ml and further incubated overnight at 37°C . OD600 and GFP fluorescence ( excitation/emission at 485/520 nm ) were then measured to ensure cultures maintained GFP fluorescence , using the Fluostar Omega spectrophotometer ( BMG Labtech ) . Bacterial pellets following centrifugation at 2500 rpm for 5 minutes were washed twice with PBS and resuspended in such volume of 10% D- ( - ) -Fructose , so that 1 ml of the bacteria-containing sugar solution corresponded to OD600 = 0 . 1 of the initial 100 ml culture . The sugar solution was further diluted 1∶12 in a 10% D- ( - ) -Fructose solution that contained tetracycline and carbenicillin at 50 µg/ml and 5% v/v of scarlet dye ( Langdale ) . Mosquitoes were fed with this solution for 2 days . Subsequently , mosquitoes fed with bacteria-containing sugar were separated based on the presence of the dye in their gut and kept on 10% D- ( - ) -Fructose containing tetracycline and carbenicillin at 50 µg/ml . Oral infections with Asaia were conducted in a similar manner . The Asaia SF2 . 1 ( GFP ) strain was used , grown as previously described [153] and maintained in 50 µg/ml kanamycin ( Sigma ) . The levels of S . marcescens infection were determined by microscopic observation of dissected midguts immersed in Vectashield mounting medium ( Vecta ) , immediately after dissection . The Zeiss Axiophot fluorescence microscope was used , equipped with light and GFP filters while photos of observed midguts were taken with the Axiocam HRc and Axiovision software ( Zeiss ) . All carcasses corresponding to midguts of S . marcescens infected mosquitoes were kept numbered in 96-well plates immersed in 75% ethanol at −80°C . Carcasses from selected midguts were used for gDNA extraction using the QIAquick Blood and Tissue kit ( QIAGEN ) . Subsequently , gDNA concentrations were determined using the Picogreen dsDNA kit ( Invitrogen ) and equimolar gDNA quantities from each mosquito were pooled . The design and validation of the SNP genotyping array used along with the treatment of gDNA pools , hybridization , calling of SNP genotypes and measurement of differentiation in each pooled hybridization between allele A and B have been described previously [39] , [154] . The frequency of designated allele A was considered as the minor allele frequency and was used to measure the difference between pooled hybridizations . The permutation analysis used has been described previously [39] , with a modified length of non-overlapping 10-SNP windows . Determination of genes residing in identified genomic areas and homology analysis was performed using Biomart 0 . 7 and the AgamP3 . 7 An . gambiae gene annotation [155] . The SNP genotyping array datasets have been deposited to ArrayExpress under the experiment name Serratia_SNP1 and accession number E-MEXP-3951 . Total RNA was extracted from midguts using the Trizol reagent ( Invitrogen ) , and treated with Turbo DNAse I ( Ambion ) . Samples were further purified using the RNeasy kit ( QIAGEN ) . Quantification was performed using the Nanodrop 1000 spectrophotometer ( Thermo Scientific ) and RNA integrity was assessed using the RNA 6000 Pico Chip kit ( Agilent ) . Labeling and hybridization were performed using the Low Input Quick Amp Labeling kit for two-color microarray based expression analysis ( Agilent ) . We used Agilent custom 4×44 k gene expression microarrays . The microarray design Pfalcip_Agamb2009 ( A-MEXP-2324 ) comprises oligonucleotide probes encompassing all An . gambiae annotated transcripts of the AgamP3 . 6 release along with P . falciparum probes , with each probe represented in duplicate . Slides were scanned using the Genepix 4000B scanner equipped with the Genepix Pro 6 . 1 software ( Axon instruments ) . All dataset files were normalized using the Genespring 11 . 0 GX software ( Agilent ) . The Lowess normalization method was used while the threshold of raw signals was set to 5 , which was sufficient to eliminate background regulation of P . falciparum probes . Further analysis of transcriptionally regulated genes and GO analysis was performed using the Genespring 11 . 0 GX software . For GO analysis , GO accession numbers for all An . gambiae transcripts were obtained using Biomart 0 . 7 and a hypergeometric test with Benjamini-Hochberg correction was performed on the set of more than 1 . 75-fold regulated genes . The corrected p-value for testing multiple GO accession numbers for their significance was set to 0 . 1 . The log2-transformed transcriptional regulation for each transcript was extracted from the normalized datasets for each of the two probes corresponding to each transcript and the obtained values from all three independent infections were used in a t-test against zero , with a p-value cut-off of 0 . 05 . The DNA microarray datasets have been deposited to ArrayExpress under the experiment name Serratia_infections and accession number E-MEXP-3952 . Mosquitoes were treated with the respective dsRNA at the day of emergence , as described previously [156] . For each targeted gene or the dsLacZ control , dsRNA was synthesized using the T7 Megascript kit ( Invitrogen ) and further purified using the RNeasy kit ( QIAGEN ) to a concentration of 3 µg/µl . For each T7 Megascript reaction , 1 µg of purified PCR product was added , derived using the T7 primer sets shown in Table S9 , using An . gambiae cDNA as template . Mosquitoes were surface sterilized by immersing them in 70% ethanol for 30 seconds and washing them twice in PBS and midguts were dissected in RNA later ( Invitrogen ) . Total RNA from mosquito midguts was extracted after homogenization with a pestle motor in RNA later using the RNeasy kit ( QIAGEN ) . cDNA was synthesized from total RNA using the QuantiTect Reverse Transcription kit ( QIAGEN ) . Quantification of bacterial load or the efficiency of RNAi-mediated silencing was performed using qRT-PCR with the respective primers shown in Table S9 . In a 20 µl reaction of Fast SYBR Green Master Mix ( Applied Biosystems ) , 1 µl of cDNA template and 2 µl of each respective primer at a 0 . 5 to 9 µM concentration , optimized for each primer set , were added . The 7500 Real-Time PCR System ( Applied Biosystems ) was used with its respective software to perform the reaction and any further analysis . The relative abundance of each sample was determined using the standard curve method as described in User Bulletin #2 for the ABI Prism 7700 Sequence Detection system ( Applied Biosystems ) in which the housekeeping AgS7 gene was used as an endogenous control . cDNA pools were amplified with the GO Taq DNA polymerase ( Promega ) using the 16S V4–V6 primers shown in Table S9 and suitable barcode sequences and purified using PCR purification and Gel Extraction kits ( QIAGEN ) . PCR products were sequenced by Beckman Genomics ( Grenoble , France ) using the Roche 454 GS FLX+ and standard procedures . The resulting FASTA files were filtered to a minimum read length of 250 bp using Galaxy [157] and blasted against the NCBI 16SMicrobial database using BLAST+ and prfectBLAST [158] with standard blastn algorithm settings and 10 maximum target sequences . Further analysis was performed using MEGAN4 [159] . Sugar meal size was determined through a modified capillary feeder assay [160] . Mosquitoes treated with LacZ or Gr9 dsRNA were antibiotic treated for 5 days , starved overnight and , subsequently , individual mosquitoes were fed on a 5 µl glass capillary ( VWR ) containing 10% D- ( - ) -Fructose and 5% v/v scarlet dye . For alive mosquitoes , sugar consumption was determined 16 hours later through the reduction of sugar solution in each capillary . The two-choice preference assay was also conducted based on a previously described capillary feeder assay [160] . Mosquitoes treated with LacZ or Gr9 dsRNA were antibiotic treated for 5 days , starved overnight and placed in pools of 8–11 mosquitoes . Mosquitoes were offered to feed from two capillaries , one containing a sugar solution as above and one also containing S . marcescens , prepared as described above for oral infection . Water-containing cotton pads were also used and pools with mosquito mortality were disregarded . 16 hours later , consumption for each capillary was determined based on the reduction of the sugar solution .
|
In malaria vector mosquitoes , the presence of bacteria and malaria parasites is tightly linked . Bacteria that are part of the mosquito gut ecosystem are critical modulators of the immune response elicited during infection with malaria parasites . Furthermore , responses against oral bacterial infections can affect malaria parasites . Here , we combined mosquito gut infections with the enterobacterium Serratia marcescens with genome-wide discovery and phenotypic analysis of genes involved in antibacterial responses to characterize molecular processes that control gut bacterial infections thus possibly affecting the mosquito susceptibility to infection by malaria parasites . Our data reveal complex genetic networks controlling the gut bacterial infection load and ecosystem homeostasis . These networks appear to exhibit much higher specificity toward specific classes of bacteria than previously thought and include behavioral response circuits involved in antibacterial immunity .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"of",
"the",
"immune",
"system",
"genetic",
"polymorphism",
"zoology",
"immunity",
"entomology",
"innate",
"immunity",
"immunity",
"to",
"infections",
"immunology",
"host-pathogen",
"interaction",
"biology",
"microbiology",
"population",
"biology",
"population",
"genetics",
"immune",
"response"
] |
2014
|
Genetic Dissection of Anopheles gambiae Gut Epithelial Responses to Serratia marcescens
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We surveyed the genetic diversity among avian influenza virus ( AIV ) in wild birds , comprising 167 complete viral genomes from 14 bird species sampled in four locations across the United States . These isolates represented 29 type A influenza virus hemagglutinin ( HA ) and neuraminidase ( NA ) subtype combinations , with up to 26% of isolates showing evidence of mixed subtype infection . Through a phylogenetic analysis of the largest data set of AIV genomes compiled to date , we were able to document a remarkably high rate of genome reassortment , with no clear pattern of gene segment association and occasional inter-hemisphere gene segment migration and reassortment . From this , we propose that AIV in wild birds forms transient “genome constellations , ” continually reshuffled by reassortment , in contrast to the spread of a limited number of stable genome constellations that characterizes the evolution of mammalian-adapted influenza A viruses .
Low pathogenic ( LP ) , antigenically diverse influenza A viruses are widely distributed in wild avian species around the world . They are maintained by asymptomatic infections , most frequently documented in aquatic birds of the orders Anseriformes and Charadriformes . As such , wild birds represent major natural reservoirs for influenza A viruses [1]–[11] and at least 105 species of the more than 9000 species of wild birds have been identified as harboring influenza A viruses [8] , [12] , [13] . These influenza A viruses , commonly referred to as avian influenza viruses ( AIV ) , possess antigenically and genetically diverse hemagglutinin ( HA ) [14] and neuraminidase ( NA ) subtypes , which includes all known influenza A virus HA ( H1–H16 ) and NA ( N1–N9 ) subtypes . At least 103 of the possible 144 type A influenza A virus HA-NA combinations have been found in wild birds [8] , [15] . AIV maintained in wild birds have been associated with stable host switch events to novel hosts including domestic gallinaceous poultry , horses , swine , and humans leading to the emergence of influenza A lineages transmissible in the new host . Adaptation to domestic poultry species is the most frequent [16]–[26] . Sporadically , strains of poultry-adapted H5 or H7 AIV evolve into highly pathogenic ( HP ) AIV usually through acquisition of an insertional mutation resulting in a polybasic amino acid cleavage site within the HA [15] , [25] . The current panzootic of Asian-lineage HP H5N1 AIV appears to be unique in the era of modern influenza virology , resulting in the deaths of millions of poultry in 64 countries on three continents either from infection or culling . There are also significant zoonotic implications of this panzootic , with 379 documented cases in humans , resulting in 239 deaths in 14 countries since 2003 ( as of April 2008 [27] ) . The Asian lineages of HP H5N1 AIV have also caused symptomatic , even lethal , infections of wild birds in Asia and Europe , suggesting that migratory wild birds could be involved in the spread of this avian panzootic [28]–[31] . Concern is heightened since wild birds are also likely to be the reservoir of influenza A viruses that switch hosts and stably adapt to mammals including horses , swine , and humans [3] . The last three human influenza pandemic viruses all contained two or more novel genes that were very similar to those found in wild birds [16] , [20] , [32] , [33] . Despite the recent expansion of AIV surveillance [7] , [8] , [10] , [34] , [35] and genomic data [5] , [36]–[38] , fundamental questions remain concerning the ecology and evolution of these viruses . Prominent among these are: ( i ) the structure of genetic diversity of AIV in wild birds , including the role played by inter-hemispheric migration , ( ii ) the frequency and pattern of segment reassortment , and ( iii ) the evolutionary processes that determine the antigenic structure of AIV , maintained as discrete HA and NA subtypes . Herein , we address these questions using the largest data set of complete AIV genomes compiled to date .
The complete genomes of 167 influenza A viruses isolated from 14 species of wild Anseriformes in 4 locations in the U . S . ( Alaska , Maryland , Missouri , and Ohio ) were sequenced; viral isolates consisted of 29 HA and NA combinations , including 11 HA subtypes ( H1–H8 , H10–H12 ) and all 9 neuraminidase subtypes ( N1–N9 ) . These sequences were collected as part of an ongoing AIV surveillance project at The Ohio State University and collaborators in other states ( 1986–2005 ) using previously described protocols [39] , and more than double the number of complete U . S . -origin avian influenza virus genomes available in GenBank . In total , 1340 viral gene segment sequences ( 2 , 226 , 085 nucleotides ) were determined ( Table S1 ) and are listed on the Influenza Virus Resource website ( http://www . ncbi . nlm . nih . gov/genomes/FLU/Database/shipment . cgi ) . Cloacal samples from wild birds frequently show evidence of mixed infections with influenza viruses of different subtypes by serologic analysis [39]–[41] . Therefore , the isolates chosen for sequence analysis were subjected to sequential limiting dilutions ( SLD ) [39] . The amplification and sequencing pipeline employed a ‘universal’ molecular subtyping strategy in which every sample was amplified with sets of overlapping primers representing all HA and NA subtypes . In this manner , samples without clear prior subtype information , and/or mixed samples , could be accurately analyzed . Despite performing SLD , 4 samples were shown by sequence analysis to represent a mixed infection ( yielding sequence with more than one HA and/or NA subtype . In addition 40 samples had mismatches between the initial antigenic subtyping results ( determined on first- or second-egg-passage isolates prior to SLD ) and the subtype determined by sequence analysis of cDNA ( following one SLD of low-egg-passage isolates ) which suggests the possibility of minor populations of antigenically distinct viruses in the low-passage isolate that outgrew the dominant antigenic population in a foreign host system during the SLD or that mixed infections in first egg passage stock caused difficulty in initial subtyping and a dominant strain emerged during SLD ( see table of viral isolates at http://www . ncbi . nlm . nih . gov/genomes/FLU/Database/shipment . cgi to examine the discordant results observed ) . Thus , up to 44 of 167 ( 26% ) of isolates potentially represent mixed infections in the initial cloacal sample . Given the SLD procedure , the true rate of mixed infection , as defined by the presence of >1 HA and/or NA subtype , was likely to be even higher , although mis-serotyping cannot also be ruled out . Sequencing viral genomes directly from primary cloacal material would be the only way to assess the mixed infection frequency , in a manner unbiased by culture , but no such studies have yet been attempted to our knowledge . For a more comprehensive analysis of AIV diversity , the AIV genomes from this study were compared to other AIV genomes available on GenBank [38] . In total , 452 HA sequences and 473 NA sequences , representative of the global diversity of AIV , were used in phylogenetic analyses . For the internal protein genes ( PB2 , PB1 , PA , NP , M , NS ) , a subset of 407 complete globally-sampled AIV genomes was used to assess the degree of linkage among gene segments . Phylogenetic trees for the HA alignment ( Figures 1a and S1 ) and NA alignment ( Figure 1b and S2 ) are shown here . Phylogenetic trees for the six other gene segments are presented in Figures S3 , S4 , S5 , S6 , S7 and S8 . The topology of the HA phylogeny reflects the antigenically defined subtypes , with some higher-order clustering among them ( e . g . , H1 , H2 , H5 and H6; H7 , H10 and H15; Figures 1a and S1 ) , as seen previously in smaller studies [14] , [42] . Although most subtypes are found in numerous avian species and occupy wide global distributions , this phylogenetic structure indicates that HA subtypes did not originate in a single radiation . More striking was the high level of genetic diversity between different subtypes; the average amino acid identity of 120 inter-subtype comparisons of full-length HA was 45 . 5% . As expected , inter-subtype comparisons of the HA1 domain exhibited more diversity , with an average inter-subtype identity of 38 . 5% . In contrast , intra-subtype identity is high ( averaging >92% ) , even when comparing sequences from different hemispheres . Hence , the genetic structure of the AIV HA is characterized by highly divergent subtypes that harbor relatively little internal genetic diversity . However , 4 subtype comparisons show considerably less divergence ( 76–79% identity ) ; H4 vs . H14 , H7 vs . H15 , H13 vs . H16 , and H2 vs . H5 , indicating that they separated more recently ( Figure 1; see below ) . A similar phylogenetic structure was seen in the NA ( Figure 1b and S2 ) , again with evidence for higher-order clustering ( e . g . , N6 and N9; N1 and N4 ) . In contrast to the HA , however , levels of genetic divergence among the NA types are more uniform , with the 9 subtypes exhibiting an average inter-subtype identity of 43 . 6% ( with an average intra-subtype identity of >89% ) and no clear outliers . Hence , no new ( detected ) NA types have been created in the recent evolutionary past . This correlates with the more uniform distribution of NA than HA subtypes in wild bird AIV isolates [43] . The topology of the NS segment phylogeny was also of note , being characterized by the deep divergence among the A and B alleles as described [44] ( Figure S8 ) . Almost every HA and NA subtype of AIV contain both the A and B NS alleles , without evidence of ‘intermediate’ lineages expected under random genetic drift , strongly suggesting that the two alleles are subject to some form of balancing selection . The NS1 protein has pleiotropic functions during infection in mammalian cells , and plays an important role in down-regulating the type I interferon response [45] . Supporting these results are the elevated rates of nonsynonymous to synonymous substitution per site ( ratio dN/dS ) observed for the NS1 gene in both avian and human influenza viruses [46] suggesting that the NS1 protein has experienced adaptive evolution in both host types . Whether this selection relates to the role the NS1 protein plays in its interaction in the type I interferon pathway is currently unclear . Far less genetic diversity is observed in the 5 remaining AIV gene segments ( PB2 , PB1 , PA , NP , and M - Figures S3 , S4 , S5 , S6 and S7 ) . Indeed , the extent of diversity in these genes is less than that within a single HA or NA subtype , with average pairwise identities ranging from 95–99% . Our phylogenetic analysis also revealed a clear separation of AIV sequences sampled from the Eastern and Western Hemispheres , as previously noted ( 3 , 19 ) , indicating that there is relatively little gene flow between overlapping Eastern and Western Hemisphere flyways . However , despite this strong biogeographic split , mixing of hemispheric AIV gene pools clearly occurs at a low level ( see below ) . To assess the frequency and pattern of reassortment in AIV , we compared the extent of topological similarity ( congruence ) among phylogenetic trees of each internal segment . This analysis revealed a remarkably frequent occurrence of reassortment , supporting previous studies on smaller data sets [37] , [47] . For example , 5 H4N6 AIV isolates were recovered from mallards sampled at the same location in Ohio on the same morning and in the same trap ( Figure 2 ) . For the internal genes , these viruses contained 4 different genome ‘constellations’ , with only 1 pair of viruses sharing the same constellation . In the data set as a whole , the large number of different subtype combinations recovered highlights the frequency of reassortment ( Figures 1b and S2 ) , and provides little evidence for the elevated fitness of specific HA/NA combinations in AIV isolates from wild birds . That the majority of HA/NA combinations have been recovered [8] , [15] also strongly supports the high frequency of reassortment involving these surface protein genes . Thus , while there is strong evidence of frequent reassortment between HA and NA , we also sought to assess the extent of reassortment among the less commonly studied internal gene segments . A maximum likelihood test of phylogenetic congruence [48] revealed that although the topologies of the internal segment trees are more similar to each other than expected by chance , so that the segments are not in complete linkage equilibrium ( in which case they would be no more similar in topology than two random trees ) , the difference among them is extensive , indicative of extremely frequent reassortment and with little clear linkage among specific segments ( Figure 3 ) . Of the 6 internal segments , NS exhibited the least linkage to other genes , falling closest to the random distribution ( i . e . possessed the greatest phylogenetic incongruence ) . This is compatible with the deep A and B allelic polymorphism in this segment . In contrast , the M segment showed the greatest phylogenetic similarly , albeit slight , to the other segments . Overall , however , the relationships between segments are better described by their dissimilarity than their congruence . Occasional AIV isolates demonstrated hemispheric mixing with reassortment . As reported previously , the majority of such mixing occurs in shorebirds and gulls [36] ( with the exception of Eurasian lineage H6 HA genes distributed widely in North American Anseriformes [5] as also revealed in this study ) . Interestingly , no completely Eurasian-lineage AIV genome has been reported in North America , or vice versa [9] , [49] . This suggests that birds initially carrying AIV between the hemispheric flyways have not been identified in surveillance efforts . Most mixed isolates possess only one gene segment derived from the other hemisphere , indicating that there is little or no survival advantage for such hemispheric crossovers in the new gene pool . Since Asian lineage HP H5N1 AIV have been isolated from wild birds in Eurasia [50] , concern has been raised over the importation of the virus into North America via migratory birds . Our analyses suggest that enhanced surveillance in gulls and other shorebirds may be warranted , and that with frequent reassortment ( see below ) , entire Asian HP H5N1 AIV isolate genome constellations may not be detected in these surveys . Overall , 25 of 407 ( 6% ) AIV genomes show evidence of hemispheric mixing , with the phylogenies suggesting a general pattern of viral gene flow from Eurasia to North America: 5 North American isolates possessed two Eurasian-lineage internal gene segments , and 20 carried a single segment . North American isolates possessing a Eurasian-lineage M segment were the most common , seen in 18 isolates ( Figure S7 ) , followed by 8 with a Eurasian PB2 segment ( Figure S3 ) , four with a Eurasian PB1 segment ( Figure S4 ) , and 1 with a Eurasian PA segment ( Figure S5 ) . The 18 Eurasian M segments and the 8 Eurasian PB2 segments each form monophyletic groups , suggesting single introductions to North America . In each case , sequences from domestic ducks in China and turkeys in Europe were the closest relatives . It is therefore theoretically possible that some of these introductions may have been derived from imported poultry rather than migratory birds . In contrast , 3 of the 4 Eurasian PB1 and the single Eurasian PA segment in North American AIV contained genes whose closest relatives were in viruses found in red-necked stints from Australia . These small waders are widely migratory , with a range from Siberia to Australasia , and occasionally in Europe and North America . Interestingly , 23 of 25 such mixed genomes were observed in shorebirds along the U . S . Atlantic coast . Unfortunately , no complete AIV genomes are available from shorebirds on the U . S . Pacific coast for comparison . In theory , two evolutionary models can explain the global pattern of AIV diversity , analogous to the allopatric and sympatric models of speciation . Under the allopatric model , the HA and NA subtypes correspond to viral lineages that became geographically isolated , resulting in a gradual accumulation of amino acid changes among them . Because of physical separation through geographical divergence , there is no requirement for natural selection to reinforce the partition of HA and NA diversity into discrete subtypes by preferentially favoring mutations at antigenic sites . In contrast , under the sympatric model , the discrete HA and NA subtypes originate within the same spatial population , such that natural selection must have reinforced speciation; subtypes that were too antigenically similar would be selected against because of cross-protective immune responses . Therefore , mutations would accumulate first at key antigenic sites , allowing subtypes to quickly diversify in the absence of herd immunity . The AIV genomic data available here suggest a complex interplay of evolutionary processes . That discrete HA and NA subtypes , as well as the 2 divergent NS alleles , are maintained in the face of frequent reassortment strongly suggests that each represents a peak on a fitness landscape shaped by cross-immunity ( Figure 4a ) . Under this hypothesis , ‘intermediate’ HA/NA/NS alleles would be selected against because they generate more widespread herd immunity , corresponding to fitness valleys . Indeed , it is the likely lack of immunological cross-protection at the subtype level that allows the frequent mixed infections described here ( although mixed infections may also occur in young , immunologically naïve birds ) . Further , in most cases these divergent HA , NA and NS alleles circulate in the same bird species in the same geographical regions , compatible with their divergence under sympatry . In addition , 3 of the most closely related pairs of HA subtypes contain an HA that is rarely isolated or limited geographically or by host species restriction , implying that their dispersion is inhibited by existing immunity; H14 has only been isolated rarely in Southern Russia , H15 only in Australia , and H16 has only been described in gulls . The possible exception is H2–H5 , where both subtypes have been isolated from a variety of bird species in a global distribution . Although these may represent more recent occurrences of allopatric speciation , antigenic cross-reactivity between the H2–H5 , H7–H15 , H4–H14 pairs was recently demonstrated [51] , again compatible with the sympatric model . Further support for possible cross-immunity between these subtypes would require experimental challenge studies . In contrast to the extensive genetic diversity seen in HA , NA and NS , the 5 remaining internal gene segments encode proteins that are highly conserved at the amino acid level , indicating that they are subject to widespread purifying selection . The fitness landscape for these genes is therefore not determined by cross-immunity , but by functional viability , with less selective pressure to fix advantageous mutations ( Figure 4b ) . Further , given such strong conservation of amino acid sequence , large-scale reassortment is permitted as it will normally involve the exchange of functionally equivalent segments , with little impact on overall fitness . These data also suggest that the cross-immunity provided by these proteins is minimal . Together , these global genomic data provide new insight into the different evolutionary dynamics exhibited by influenza A viruses in their natural wild bird hosts and in those viruses stably adapted to novel species ( e . g . , domestic gallinaceous poultry , horses , swine , and humans ) . Based on these analyses , we hypothesize that AIV in wild birds exists as a large pool of functionally equivalent , and so often inter-changeable , gene segments that form transient genome constellations , without the strong selective pressure to be maintained as linked genomes . Rather than favoring successive changes in single subtypes , geographic and ecologic partitioning within birds , particularly within the different flyways , coupled with complex patterns of herd immunity , has resulted in an intricate fitness landscape comprising multiple fitness peaks of HA , NA and NS alleles , interspersed by valleys of low fitness which prevent the generation of intermediate forms ( Figure 4a ) . In contrast , stable host switching involves the acquisition of a number of ( as yet ) poorly characterized mutations [24] , [33] , [52] , [53] that serve to separate an individual , clonally derived influenza virus strain from the large wild bird AIV gene pool . Because adaptation to a new host likely limits the ability of these viruses to return to the wild bird AIV gene pool [24] , [54] , these emergent viruses must evolve as distinct eight-segment genome configurations within the new host . The ability of recent HP H5N1 AIV to cause spillover infections in wild birds is an unprecedented exception . Further , because humans represent a large and spatially mixed population , natural selection is able to act efficiently on individual subtypes [55] . Hence , a limited number of subtypes circulate within humans and evolve by antigenic drift to escape population immunity . Notably , the recent Asian lineage HP H5N1 AIV strains are intermediate between these two contrasting influenza ecobiologies; a combination of large poultry populations allows natural selection to effectively drive rapid antigenic and genetic change within a single subtype [46] , [56] , while reassortment with the wild bird AIV gene pool facilitates the generation of new genome constellations [57]–[59] . Similar patterns have also been observed with the widely circulating H9N2 and H6N1 viruses in gallinaceous poultry in Eurasia [60] , [61] . Previous analyses have also shown that recent HP H5N1 viruses had the highest evolutionary rates and selection pressures ( dN/dS ratios ) as compared to other AIV lineages [46] . Consequently , these results underscore the importance of determining the mechanistic basis of how H5N1 has spread so successfully among a diverse range of both domestic and wild bird species .
The genomes of 167 influenza A virus isolates recovered from 14 species of wild Anseriformes located in four U . S . states ( Alaska , Maryland , Missouri , Ohio ) were sequenced for this study; viral isolates consisted of 29 hemagglutinin ( HA ) and neuraminidase ( NA ) combinations , including H1N1 , H1N6 , H1N9 , H2N1 , H3N1 , H3N2 , H3N6 , H3N8 , H4N2 , H4N6 , H4N8 , H5N2 , H6N1 , H6N2 , H6N5 , H6N6 , H6N8 , H7N3 , H7N8 , H8N4 , H10N7 , H10N8 , H11N1 , H11N2 , H11N3 , H11N6 , H11N8 , H11N9 , H12N5 . Cloacal swabs were collected as previously described [39] from 1986–2005 as part of The Ohio State University's ongoing influenza A virus surveillance activities and in collaboration with many researchers in other states since 2001 . A table listing the details of each isolate are available from the Influenza Virus Resource page ( http://www . ncbi . nlm . nih . gov/genomes/FLU/Database/shipment . cgi ) . Avian influenza viruses were originally isolated using standard viral isolation procedures after 1–2 passages in 10-day-old embryonated chicken eggs ( ECEs ) [62] . Type A influenza virus was confirmed using commercially available diagnostic assays ( Directigen Flu A Assay , Becton Dickinson Microbiology Systems , Cockeysville , MD ) and isolates were subtyped at the National Veterinary Services Laboratories ( NVSL ) , Animal and Plant Health Inspection Service , United States Department of Agriculture , Ames , Iowa , using standard hemagglutinin inhibition and neuraminidase inhibition testing procedures [51] . Isolates for this investigation were generally selected from the viral archives based on antigenic diversity , clustering of recoveries , no evidence of antigenically mixed subtypes , and distribution over time . First- or second-egg-passage isolates in chorioallantoic fluid ( CAF ) were rapidly thawed from −80°C to room temperature , vortexed for 30 seconds and centrifuged at 1500 rpm for 10 minutes . Approximately 0 . 5 ml of CAF was drawn from the vial using a 26-gauge needle and subsequently passed through a 25 mm , 0 . 2 µm filter . Following filtration , a 10−1 CAF stock dilution was obtained by adding 0 . 2 ml filtered CAF to 1 . 8 ml Brain Heart Infusion Broth containing penicillin and streptomycin and vortexed for 30 seconds . Serial dilutions ( 10−6 maximum ) were performed and 0 . 1 ml of each dilution was inoculated into each of four 10-day-old ECEs . After approximately 48 hours of incubation at 35°C/60% humidity , the inoculated eggs were chilled overnight and CAF was harvested from each egg and tested for hemagglutinating activity . The CAF from the last dilution positive for hemagglutinating activity was tested for the presence of type A influenza virus using the Directigen Flu A or Synbiotics Flu Detect Antigen Capture Test Strips™ ( Synbiotics Corp . , San Diego , CA ) . Hemagglutination titer assays were performed and CAF aliquots from the most dilute influenza A positive samples were stored at −80°C . If no endpoint titer was determined , the 10−6 CAF dilution was stored at −80°C and the procedure repeated utilizing 10−4 to 10−9 sequential dilutions . Viral RNA was isolated from allantoic fluid using Trizol® Reagent ( Invitrogen Corp . , Carlsbad , CA ) and transcribed into 20 µl of cDNA for a subset of samples [63] . Segment-specific universal primers designed to amplify partial and/or full-segments were initially used in RT-PCR assays to assess vRNA quality and RT-PCR primer specificity and sensitivity . Additionally , M13 sequencing tags ( F primer: GTAAAACGACGGCCAG; R primer: CAGGAAACAGCTATGAC ) were added to each primer set for ease of sequencing RT-PCR products in both forward and reverse directions . For initiation of a high-throughput sequencing pipeline , a universal strategy for primer design was employed to ensure detection of multiple viral infections within a single sample . Primers were designed to semi-conserved areas of the six internal segments . For the segments encoding the external proteins , primers were designed from alignments of subsets of the 16 HA and 9 NA avian subtypes . Alignments were generated with MUSCLE [64] and visualized with BioEdit [65] . An M13 sequence tag was added to the 5′ end of each primer to be used for sequencing . Four sequencing reactions per run were analyzed on an agarose gel for quality control purposes . The sequence success rate of each primer pair was analyzed relative to the HA and NA subtype . Primers that did not perform well were altered or replaced . All primers and RT-PCR assay cycling conditions are available upon request . Influenza A virus isolates were amplified with the OneStep RT-PCR kit ( Qiagen , Inc . , Valencia , CA ) . Amplicons were sequenced in both the forward and reverse directions . Each amplicon was sequenced from each end using M13 primers ( F primer: TGTAAAACGACGGCCAGT; R primer: CAGGAAACAGCTATGACC ) . Sequencing reactions were performed using Big Dye Terminator chemistry ( Applied Biosystems , Foster City , CA ) with 2 µl of template cDNA . Additional RT-PCR and sequencing was performed to close gaps and to increase coverage in low coverage or ambiguous regions . Sequencing reactions were analyzed on a 3730 ABI sequencer and sequences were assembled in a software pipeline developed specifically for this project . Once genomic sequence was obtained for an individual sample , reads for each segment were downloaded , trimmed to remove amplicon primer-linker sequence and low quality sequence , and assembled . A small genome assembly suite called Elvira ( http://elvira . sourceforge . net/ ) , based on the open-source Minimus assembler , was developed to automate these tasks . The Elvira software delivers exceptions including failed reads , failed amplicons , and insufficient coverage to a reference sequence ( as obtained from GenBank ) , ambiguous consensus sequence calls , and low coverage areas . The avian influenza A sequences ( with GenBank Accession numbers ) produced from this ongoing study are available at http://www . ncbi . nlm . nih . gov/genomes/FLU/Database/shipment . cgi . The first 167 avian influenza genomes from this collection were submitted to GenBank and included in this study . The genomes of avian influenza virus newly determined here were combined with those already available on GenBank , particularly from recent large-scale surveys of viral biodiversity [38] . Sequences from viruses isolated before 1970 , which may have been subjected to extensive laboratory passage , were excluded as were the large numbers of H5N1 sequences collected in recent years ( a sample of H5N1 genomes , 1997–2005 , were included for analysis ) . In total , 452 HA sequences and 473 NA sequences were used in analyses . For the internal protein-encoding segments ( PB2 , PB1 , PA , NP , M , NS ) , a total of 407 genomes were analyzed ( by considering a common data set we were able to investigate patterns of segment linkage , see below ) . For each data set , sequence alignments of the coding regions were created using MUSCLE [64] and adjusted manually using Se-Al [66] according to their amino acid sequence . In the case of HA and NA , some regions of the inter-subtype sequence alignment were extremely divergent such that they could not be aligned with certainty ( HA signal peptide and cleavage site insertions in HPAI H5 or H7 , and variable small stalk deletions in NA ) . Because of their potential to generate phylogenetic error , these small regions of ambiguity were deleted . This resulted in the following sequence alignments used for evolutionary analysis: PB2 = 2277 nt; PB1 = 2271 nt; PA = 2148 nt; HA = 1683 nt; NP = 1494 nt; NA = 1257 nt; M = 979 nt; NS = 835 nt . All sequence alignments are available from the authors on request . Nucleotide and amino acid identity was calculated using Megalign ( Lasergene 7 . 2 , DNAStar , Madison , WI ) . Using these alignments , maximum likelihood ( ML ) trees were inferred using PAUP* [67] , based on the best-fit models of nucleotide substitution models determined by MODELTEST [68] . In most cases , the preferred model of nucleotide substitution was GTR+I+Γ4 , or a close relative . For each of these trees , the reliability of all phylogenetic groupings was determined through a bootstrap resampling analysis ( 1000 pseudo-replicates of neighbor-joining trees estimated under the ML substitution model ) . We employed a maximum likelihood method to assess the extent of phylogenetic congruence , indicative of reassortment [48] . To reduce any bias in phylogenetic structure caused by geographic segregation , only isolates from North American flyways were used in analyses of the internal gene segments . Briefly , ML trees for each internal gene segment were estimated as described above . Next , the log likelihood ( -LnL ) of each of the ML trees was estimated on each gene segment data set in turn , optimizing branch lengths under the ML substitution model in every case . The topological similarity between each gene segment tree on each data set was then determined by compared the difference in likelihood among them ( Δ-LnL ) . Clearly , the greater the similarity in topology ( congruence ) among the trees for each segment , the closer their likelihood scores and so the more likely they are to be linked . To put the distribution of Δ-LnL values in context , we constructed 500 random trees for each data set and optimized their branch lengths in the same manner . If any of the Δ-LnL values among the ML trees falls within the random distribution then we can conclude that the gene segments in question are in complete linkage equilibrium . All these analyses were conducted using PAUP* package [67] .
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Influenza A viruses are an extremely divergent group of RNA viruses that infect in a variety of warm-blooded animals , including birds , horses , pigs , and humans . Since they contain a segmented RNA genome , mixed infection can lead to genetic reassortment . It is thought that the natural reservoir of influenza A viruses is the wild bird population . Influenza A viruses can switch hosts and cause novel outbreaks in new species . Influenza viruses containing genes derived from bird influenza viruses caused the last three influenza pandemics in humans . In this study , we surveyed the genetic diversity among influenza A viruses in wild birds . Through a phylogenetic analysis of the largest data set of wild bird influenza genomes compiled to date , we were able to document a remarkably high rate of genome reassortment , with no clear pattern of gene segment association and occasional inter-hemisphere gene segment migration and reassortment . From this , we propose that influenza viruses in wild birds forms transient “genome constellations , ” continually reshuffled by reassortment , in contrast to the spread of a limited number of stable genome constellations that characterizes the evolution of mammalian-adapted influenza A viruses .
|
[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"evolutionary",
"biology/microbial",
"evolution",
"and",
"genomics",
"virology/virus",
"evolution",
"and",
"symbiosis",
"evolutionary",
"biology/bioinformatics",
"evolutionary",
"biology/evolutionary",
"ecology"
] |
2008
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The Evolutionary Genetics and Emergence of Avian Influenza Viruses in Wild Birds
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The Bacillus subtilis GlmR ( formerly YvcK ) protein is essential for growth on gluconeogenic carbon sources . Mutants lacking GlmR display a variety of phenotypes suggestive of impaired cell wall synthesis including antibiotic sensitivity , aberrant cell morphology and lysis . To define the role of GlmR , we selected suppressor mutations that ameliorate the sensitivity of a glmR null mutant to the beta-lactam antibiotic cefuroxime or restore growth on gluconeogenic carbon sources . Several of the resulting suppressors increase the expression of the GlmS and GlmM proteins that catalyze the first two committed steps in the diversion of carbon from central carbon metabolism into peptidoglycan biosynthesis . Chemical complementation studies indicate that the absence of GlmR can be overcome by provision of cells with N-acetylglucosamine ( GlcNAc ) , even under conditions where GlcNAc cannot re-enter central metabolism and serve as a carbon source for growth . Our results indicate that GlmR facilitates the diversion of carbon from the central metabolite fructose-6-phosphate , which is limiting in cells growing on gluconeogenic carbon sources , into peptidoglycan biosynthesis . Our data suggest that GlmR stimulates GlmS activity , and we propose that this activation is antagonized by the known GlmR ligand and peptidoglycan intermediate UDP-GlcNAc . Thus , GlmR presides over a new mechanism for the regulation of carbon partitioning between central metabolism and peptidoglycan biosynthesis .
Bacillus subtilis provides a powerful model system for understanding cell wall homeostasis in Gram positive bacteria . Disruption of pathways for the synthesis of peptidoglycan ( PG ) and other cell envelope components elicits complex adaptive responses often controlled by alternative σ factors or two-component systems [1 , 2] . The ECF σ factor σM regulates numerous operons involved in PG synthesis and mutants are sensitive to PG synthesis inhibitors [3] . Previously , we found that mutation of gdpP , which encodes a cyclic-di-adenosine monophosphate ( c-di-AMP ) hydrolase , can suppress the sensitivity of B . subtilis sigM null mutants towards beta-lactam antibiotics [4] . This suggests that c-di-AMP plays some role in PG homeostasis . Mutations in the yvcK gene ( herein renamed glmR ) also exhibit cell envelope defects , as evidenced by cell bulging and lysis when inoculated into non-glycolytic carbon sources [5] . Moreover , a yqfF::Tn insertion suppressed the inability of a glmR mutant to grow on gluconeogenic media [5] . Although unknown at the time , yqfF is now known to encode a second c-di-AMP hydrolase renamed PgpH [6 , 7] . These observations encouraged us to investigate possible connections between GlmR , c-di-AMP , and cell envelope homeostasis . In B . subtilis , GlmR ( formerly YvcK ) is essential for growth on amino acids and intermediates of the tricarboxylic acid cycle and pentose phosphate pathway , but dispensable for growth on glucose and other glycolytic carbon sources [5] . Previous genetic studies revealed that mutations in genes affecting central carbon metabolism ( CCM ) , including zwf and cggR , allow a glmR null mutant to grow on gluconeogenic carbon sources [5] . These observations suggest that GlmR has a yet undefined role in regulating metabolism . In the absence of GlmR , cells display cell envelope defects and lyse under gluconeogenic growth conditions . The function of GlmR in CCM , and how this relates to cell envelope integrity , is not yet clear . One model suggests that GlmR may function as a cytoskeletal filament protein analogous to MreB to help coordinate cell wall synthesis [8] . MreB , an actin-like cytoskeletal protein , is important for maintaining a rod shape in B . subtilis and deletion of mreB leads to severe morphological defects and eventual cell lysis , effects attributed to mislocalization of penicillin binding protein 1 ( PBP1 ) [9] . B . subtilis GlmR localizes to the membrane in a helical fashion , and overexpression of GlmR rescues the cell defects seen in an mreB deletion mutant and restores proper localization of PBP1 . Conversely , overexpression of MreB rescues the morphological defects of a glmR null mutant when grown on gluconeogenic carbon sources [8] . Recently , GlmR was found to possess a ligand binding site for UDP sugars such as UDP-glucose and UDP-N-acetylglucosamine ( UDP-GlcNAc ) [10] . Since UDP-GlcNAc is a precursor of PG synthesis , this suggests that GlmR may sense this intermediate to somehow modulate CCM or cell envelope homeostasis . Mutations altering the UDP-sugar binding site did not affect growth on gluconeogenic media in B . subtilis , but did lead to increased sensitivity to bacitracin [10] . Although the biochemical details are unclear , the role of GlmR in metabolism and cell wall homeostasis seems to be widely conserved . Homologs of GlmR are present diverse bacteria and a glmR mutant can be complemented by expression of the Escherichia coli homolog , YbhK [5] . Mutation of glmR homologs in the intracellular pathogens Mycobacterium tuberculosis ( cuvA ) and Listeria monocytogenes ( yvcK ) leads to alterations in cell morphology and sensitivity to cell wall acting antibiotics , as well as defects in carbon source utilization and establishment of infection in the host cell [11 , 12] . Although these diverse phenotypes , biochemical properties and cell localization studies are all intriguing , a unifying model to account for the role of GlmR in the cell has been elusive . Here , we show that a B . subtilis strain lacking glmR is susceptible to peptidoglycan ( PG ) biosynthesis inhibitors such as beta-lactams , vancomycin and moenomycin . Characterization of glmR suppressor mutations indicates that increased expression of genes involved in UDP-GlcNAc biosynthesis is sufficient to increase beta-lactam resistance and restore growth on gluconeogenic carbon sources . Moreover , supplementation with GlcNAc can bypass the requirement for GlmR even in strains where GlcNAc cannot enter into CCM . Our results support a model in which GlmR functions to help divert carbon to PG biosynthesis , likely through direct interaction with GlmS . We propose that this effect is particularly important during gluconeogenesis since the GlmS substrate fructose 6-phosphate is present at a reduced level under these conditions [13] .
To test the role of GlmR in the connection between CCM and PG biosynthesis ( Fig 1 ) , we generated a B . subtilis strain with an in-frame , unmarked deletion of glmR ( ΔglmR ) and characterized its growth properties and sensitivity to cell wall antibiotics . Mueller-Hinton ( MH ) is a gluconeogenic medium containing amino acids as primary carbon source and is commonly used for antibiotic sensitivity experiments . However , ΔglmR is unable to grow on MH . This phenotype can be complemented by an ectopic , inducible copy of glmR ( Fig 2A ) or addition of glucose ( S1A and S1B Fig ) , consistent with prior results [5] . To monitor the impact of the ΔglmR mutation on antibiotic sensitivity we performed zone-of-inhibition assays using LB ( lysogeny broth ) medium , a complex medium containing a variety of mono- and disaccharides ( a total carbohydrate concentration of ~0 . 16%; [14] ) and abundant amino acids . The ΔglmR mutant is much more sensitive to the beta-lactam antibiotic cefuroxime ( CEF ) ( Fig 2B ) as well as to other beta-lactam antibiotics ( oxacillin and cefixime ) , moenomycin , and vancomycin ( S2A–S2D Fig ) , all of which act by affecting the assembly and cross-linking of the peptidoglycan sacculus . However , we did not observe any significant difference in susceptibility between wild-type ( WT ) and ΔglmR to fosfomycin , bacitracin or nisin ( S2E–S2G Fig ) . The lack of significant effect with these compounds may be due to the presence of inducible resistance mechanisms that might mask the effects of the ΔglmR mutation [15–18] . We selected CEF for further study due to the significantly higher sensitivity of the ΔglmR strain . Induction of an ectopic , IPTG-inducible glmR gene partially complements ΔglmR cefuroxime sensitivity ( Fig 2B ) . Incomplete complementation may indicate that GlmR levels from this construct , while sufficient to restore growth ( Fig 2A ) , are insufficient for robust cell wall synthesis . Consistent with this idea , induction of an N-terminally 3X-FLAG-tagged glmR allele with an optimized ribosome-binding site ( AGGAGG-seven base pairs upstream from start codon ) , complemented CEF resistance to WT levels ( S3A Fig ) . Mutations affecting PG synthesis can often be suppressed by high concentrations of Mg2+ [19 , 20] . Indeed , Mg2+ suppresses the growth defect of a glmR deletion mutant on non-glycolytic carbon sources ( S1A Fig ) , as shown previously [5] , and also partially suppresses CEF sensitivity ( S3B Fig ) . These results suggest that a ΔglmR strain is impaired in PG synthesis , and therefore more susceptible to antibiotics that interfere directly with PG assembly such as beta-lactams . Both the ΔglmR and ΔsigM mutants are CEF sensitive , and in both cases mutations known to increase c-di-AMP levels suppress this sensitivity ( see below ) . This suggests that GlmR and σM may function in the same pathway . However , a ΔglmR ΔsigM double mutant is much more sensitive than either single mutant ( Fig 2C ) , suggesting that these are two independent ( and additive ) pathways for intrinsic CEF resistance . The CEF sensitivity of the ΔglmR strain is suggestive of a defect in PG synthesis . GlmR is also known to be modified on Thr304 by the penicillin binding protein and serine/threonine associated ( PASTA ) kinase PrkC and phosphatase PrpC [21] . PrkC is activated by muropeptides during spore germination [22] and is regulated by interaction with the cell division protein GpsB during growth [23] . PrkC-dependent phosphorylation of GlmR has been linked to its role in morphogenesis and to resistance to bacitracin , but appears not to be required for growth on gluconeogenic carbon sources [21] . Similarly , this post-translational modification is not required for suppression of CEF sensitivity: both the phosphomimetic GlmRT304E and phosphoablative GlmRT304A mutant proteins complement the null mutant as well as wild-type ( Fig 2B ) . To gain insight into the role of GlmR in B . subtilis , we characterized suppressors ( both spontaneous and transposon-generated ) that either increased CEF resistance or restored the ability of ΔglmR to grow on MH medium . We isolated CEF resistant ΔglmR suppressors from CEF zone-of-inhibition assays or as colonies on MH medium ( S1B Fig ) . We identified the causative mutations using whole-genome resequencing ( spontaneous mutations ) or by sequencing of junction fragments ( transposon insertions ) followed by linkage analysis and/or genetic reconstruction and complementation ( Table 1 ) . In general , the selected mutations suppressed both phenotypes associated with ΔglmR . Those suppressors selected for increased CEF resistance also recovered an ability to grow on MH medium . Conversely , for those selected for growth on MH medium , nearly all displayed at least a partial increase in CEF resistance relative to the ΔglmR starting strain ( Table 1 ) . In general , in this and previous studies , we find that CEF sensitivity is an excellent reporter for defects in cell wall synthesis . Often , suppressor mutations that fully restore growth may only partially rescue intrinsic CEF resistance . Here , we will focus on those suppressor mutations in the cdaA-cdaR-glmM-glmS region of the chromosome , which encodes the two initial enzymes in the peptidoglycan biosynthesis pathway , a major cyclic-di-AMP synthase ( CdaA ) and a regulator of CdaA ( CdaR ) . We also recovered mutations in other genes in carbon metabolism , including pgcA and zwf , consistent with prior genetic studies of glmR function [5] . The possible mechanisms of suppression for these and other mutations are considered in the Discussion . Many of the ΔglmR suppressors ( Table 1 ) contained changes in a chromosomal region around two neighboring operons: sigW-rsiW and cdaA-cdaR-glmM-glmS ( Fig 3A ) . These included a transposon insertion immediately after the rsiW stop codon ( rsiW3 ) and point mutations in the glmS ribozyme ( glmS1; 200068A>T ) , in the penultimate codon of rsiW ( rsiW1; 196049G>A ) , and downstream of rsiW ( rsiW2; 196071C>T ) . Note that the identical glmS mutation ( glmS1 ) was recovered independently in both selection conditions . Since most of the suppressor mutations did not fully restore CEF resistance to WT levels ( Table 1 ) , we selected several with intermediate levels of resistance as a starting point for selection of further increased CEF resistance . The most frequent secondary mutations were in rho ( S1 Table ) . A rho deletion mutant has been associated with beta-lactam resistance in B . subtilis previously [24] . Interestingly , a ΔglmR Δrho double mutant is actually more sensitive to CEF than ΔglmR ( S4 Fig ) , and it is only when a primary suppressor mutation ( such as glmS1 ) is present in ΔglmR that rho mutations confers significant CEF resistance ( S4 Fig and S1 Table ) . GlmS is an amidotransferase that catalyzes the first step in PG synthesis ( Fig 1 ) by conversion of the glycolysis intermediate fructose-6-phosphate ( F6P ) into glucosamine-6-phosphate ( GlcN6P ) using glutamine as an amino group donor [25] . Expression of GlmS is under negative feedback control mediated by a ribozyme structure encoded in the 5'-untranslated region ( 5’-UTR ) of the glmS mRNA . Upon binding to the GlmS product , GlcN6P , the ribozyme promotes site specific self-cleavage of glmS mRNA and consequently reduces glmS expression [26] . The glmS1 suppressor mutation is a base change in the catalytic domain of the glmS ribozyme ( Fig 3B ) [27] . After moving the glms1 mutation into a ΔglmR strain , the reconstructed ΔglmR glmS1 strain regains the ability to grow on gluconeogenic carbon sources ( Fig 4A ) and has increased resistance to CEF ( Fig 4B ) . We hypothesized that glmS1 might interfere with the catalytic activity of the glmS ribozyme . Consistent with this idea , the glmS1 mutation caused a >50-fold increase in glmS mRNA compared to WT ( Fig 4C ) and a corresponding increase in GlmS protein levels ( Fig 4D ) . We did not see any significant difference in glmS mRNA level between WT and ΔglmR . Reconstruction of ΔglmR strains with mutations rsiW1 or rsiW2 confirmed that these changes allow growth of ΔglmR on gluconeogenic growth medium ( Fig 5A ) as well as increased resistance to CEF ( Fig 5B ) . The rsiW1 mutation is silent with respect to the sequence of RsiW and rsiW2 is downstream of the rsiW coding region ( Fig 3A ) . We hypothesized that these point mutations might affect the intrinsic transcription terminator of the sigW-rsiW operon . In silico analysis indicated that each mutation generates a mismatch in the stem of the transcription terminator that is predicted to decrease stability and therefore increase readthrough from the sigW-rsiW operon into the downstream cdaA-cdaR-glmS-glmM operon ( S5 Fig ) . Indeed , the rsiW1 or rsiW2 suppressor mutations led to a >10-fold increase in the mRNA level for the first gene of this operon , cdaA ( Fig 5C ) . Expression of the sigW-rsiW operon is dependent on an autoregulatory σW-dependent promoter . An in-frame deletion mutation of sigW abolished the ability of the rsiW1 and rsiW2 mutations to suppress the ΔglmR phenotype ( Fig 5B ) . However , in a strain with a sigW::erm disruption mutation the rsiW1 and rsiW2 mutations still conferred increased CEF resistance since the erm σA promoter now reads into the cdaA operon ( S5B Fig ) . These observations support our hypothesis that rsiW1 and rsiW2 increase expression of cdaA-cdaR-glmM-glmS . A similar increase in transcription may explain the phenotype of the rsiW3 Tn insertion ( Table 1 ) . We reasoned that the rsiW1 , rsiW2 and rsiW3 mutations likely lead to elevated expression of the cdaA-cdaR-glmM-glmS operon . The first two genes encode the major synthase ( CdaA ) for c-di-AMP and an activator protein ( CdaR ) [6 , 7] . The final two genes encode enzymes for the initial steps of PG biosynthesis that ( together with GlmU; also known as GcaD; [28] ) convert F6P to UDP-GlcNAc ( Fig 1 ) . To determine which gene ( s ) in this operon are involved in suppression of the ΔglmR phenotypes we integrated IPTG-inducible copies of various portions of this operon ( including cdaA , cdaA-cdaR , cdaA-cdaR-glmM , cdaA-cdaR-glmM-glmS , glmM-glmS ) at the amyE locus in the ΔglmR strain . These strains were tested for CEF sensitivity and growth on MH medium . Overexpression of cdaA or cdaA-cdaR was not sufficient to increase CEF resistance of ΔglmR ( Fig 6A ) , although we did note an increased frequency of spontaneous suppressors . Overexpression of cdaA-cdaR-glmM or glmM-glmS partially restored CEF resistance ( Fig 6A ) . However , when the whole operon ( cdaA-cdaR-glmM-glmS ) was induced CEF resistance was restored to essentially WT levels ( Fig 6A ) . Increased expression of cdaA-cdaR-glmM or cdaA-cdaR-glmM-glmS also suppressed the essentiality of ΔglmR on gluconeogenic MH medium ( Fig 6B ) . In contrast , induction of cdaA-cdaR alone has a comparatively weak and variable effect on growth , which may reflect the rapid emergence of suppressors in this strain ( Fig 6B ) . From these results we conclude that the key factor in increased fitness of the ΔglmR strain is elevated expression of GlmS and/or GlmM , but that c-di-AMP may also play a role . An increase of c-di-AMP has been previously associated with CEF resistance since mutations in gdpP , encoding the major c-di-AMP hydrolase , suppress the CEF sensitivity of a sigM mutant [4] . Moreover , a yqfF::Tn insertion , affecting a second c-di-AMP hydrolase renamed PgpH [6 , 7] , suppresses the inability of a glmR ( yvcK ) mutant to grow on gluconeogenic media [5] . We have confirmed these findings and here demonstrate that inactivation of gdpP increases CEF resistance of ΔglmR , although pgpH does not have a significant effect under our conditions ( S6A and S6B Fig ) . It is interesting to note that a gdpP pgpH double mutant , which has greatly elevated c-di-AMP levels and is growth impaired [7] , is also highly sensitive to CEF . This effect is not additive with ΔglmR , suggesting that excess c-di-AMP may affect the same pathway as GlmR ( S6A and S6B Fig ) . Consistently , the ability of CdaA and CdaR to increase CEF resistance in a ΔglmR mutant seems to be contingent on the additional expression of GlmM and GlmS , as noted above ( Fig 6A ) . CdaA forms a complex with both CdaR and GlmM [7 , 29] , suggesting that c-di-AMP may modulate GlmM activity . We next considered whether a ΔglmR strain might be phenotypically suppressed by over-expression of other individual enzymes upstream and downstream of UDP-GlcNAc . Induction of glmS , glmM or glmU ( Fig 1 ) , partially restored CEF resistance ( Fig 7A ) and restored the ability of ΔglmR to grow on gluconeogenic medium ( Fig 7B ) . We suggest that these enzymes increase the forward reaction catalyzed by GlmS by consumption of the product , GlcN6P . GlcN6P is potent inhibitor of GlmS ( product inhibition ) [30] , a property shared with the human ortholog [31] . A portion of cellular UDP-GlcNAc is converted to UDP-MurNAc , the second building block of PG , by MurA and MurB ( Fig 1 ) . B . subtilis has two MurA paralogs , MurAA and MurAB , but only MurA is essential . UDP-MurNAc is then modified by addition of a pentapeptide side-chain and transferred to the undecaprenylphosphate carrier lipid to ultimately generate lipid II ( Fig 1 ) , a lipid-linked GlcNAc-MurNAc-pentapeptide that is the substrate for extracellular PG synthesis [32] . Overexpression of murAA or murB increased the sensitivity of the ΔglmR strain to CEF ( Fig 7C ) , and neither rescued the growth defect of ΔglmR on MH medium ( Fig 7D ) . We reasoned that the effects of MurAA and MurB overproduction might be relieved in cells that have increased capacity to synthesize UDP-GlcNAc . To test this hypothesis , we introduced the glmS1 mutation ( which abolishes negative feedback regulation of glmS ) into the ΔglmR amyE::Pspac ( hy ) murAA and ΔglmR amyE::Pspac ( hy ) murB strains . In these glmS1 strains , induction of murAA or murB no longer increases sensitivity to CEF ( Fig 7C ) . Based on these observations we hypothesize that B . subtilis lacking GlmR is impaired specifically in UDP-GlcNAc biosynthesis . The resulting inability to efficiently synthesize PG is a likely reason for the essentiality of glmR on gluconeogenic media . GlmR was recently found to bind UDP-sugars such as UDP-glucose and UDP-GlcNAc [10] . UDP-GlcNAc bound with five times higher affinity that UDP-Glc , suggesting that the former may be a regulatory ligand for GlmR . We used CRISPR-gene editing to introduce single amino acid substitutions in the UDP-GlcNAc binding site of GlmR that were previously shown to abolish ligand binding ( Y265A , R301A and R301E ) . Consistent with prior results [10] , none of these three mutations affected the ability of GlmR to support growth on gluconeogenic MH medium ( Fig 8A ) , nor did they have a significant impact on CEF resistance ( Fig 8B ) . We therefore suggest that ligand binding serves as a feedback mechanism to down-regulate GlmR activity when UDP-GlcNAc levels are high . Under gluconeogenic conditions , when GlmR is required for redirecting carbon from CCM into PG synthesis , this binding site would be vacant , and therefore these mutations would not affect the stimulatory function of GlmR ( Fig 1 ) . Since ΔglmR suppressor mutations lead to increased glmS expression ( Fig 4C and 4D ) , we reasoned that the ΔglmR strain may be specifically defective in GlmS activity . If this is the case , we hypothesized that provision of cells with GlcNAc would chemically complement the ΔglmR growth defect . Indeed , when a disc containing GlcNAc was placed on a MH medium plate strong growth of the ΔglmR strain was observed ( Fig 9A ) . GlcNAc is taken up by the GlcNAc-specific phosphoenolpyruvate phosphotransferase system ( PTS ) protein NagP and enters the cell as GlcNAc-6-phosphate [33] . Deacetylation by NagA then generates GlcN6P ( Fig 1 ) , which is also the product generated by GlmS [34] . GlcN6P can either feed into peptidoglycan biosynthesis ( GlmM and GlmU ) or feed CCM by conversion to F6P by either of two inducible deaminases ( NagB and GamA ) [33 , 35] ( Fig 1 ) . The ability of GlcNAc to support growth of the ΔglmR strain requires NagA , but is independent of the GamA and NagB deaminases ( Fig 9B ) . This indicates that the limiting step in metabolism during growth of the ΔglmR strain on largely gluconeogenic carbon sources is the GlmS-catalyzed conversion of F6P to GlcN6P . This limitation can be by-passed by up-regulation of GlmS ( e . g . by overexpression , Fig 7B , or in the glmS1 mutant strain , Fig 4 ) or by provision of cells with GlcNAc . The ability of overproduced GlmM or GlmU to support growth ( Fig 7B ) may therefore seem surprising , but may be explained by more rapid consumption of GlcN6P , which would prevent product inhibition of GlmS and also increase translation of GlmS by inhibiting glmS ribozyme cleavage . To test if GlcNAc addition also suppresses the increased CEF sensitivity , we tested WT and ΔglmR strains on LB agar supplemented with 0 . 5% and 1% GlcNAc . Addition of GlcNAc partially suppressed the CEF sensitivity of ΔglmR , but had no significant effect on a strain in which GlmS was up-regulated by the glmS1 suppressor mutation ( Fig 9C ) . In a ΔglmR ΔnagB ΔgamA strain in which added GlcNAc cannot re-enter CCM , CEF resistance is restored to near WT levels ( Fig 9D ) . The greater suppression seen in this strain may result from the inability of this strain to catabolize incoming GlcNAc , which thereby further increases the flux into PG synthesis . This supports the notion that a major contributor to CEF sensitivity is a metabolic defect that limits the ability of the cell to synthesize PG , apparently due to a limitation in the ability of GlmS to redirect carbon from CCM to cell wall synthesis . We hypothesize that GlmR may directly stimulate GlmS enzyme activity . This is supported by evidence of a GlmR-GlmS protein interaction in bacterial two-hybrid assays ( Fig 10 ) . The observed interaction is robust , as compared to the positive control , and GlmR did not interact with other proteins tested including CdaA , GlmM or CdaR ( Fig 10 ) .
Our genetic analysis supports a model in which GlmR activates GlmS , and we suggest that this activity is inhibited when GlmR is bound to the downstream metabolite , UDP-GlcNAc ( Fig 1 ) . This model is supported by several key observations . First , overproduction of GlmS , in either the glmS1 mutant or by induction from an ectopic glmS gene , is sufficient to restore growth of the glmR null mutant on MH medium ( Figs 4 and 7 ) . Second , a glmR mutant can be chemically complemented by GlcNAc , even under conditions where GlcNAc cannot be routed into CCM ( Fig 9 ) . Since metabolism of GlcNAc generates GlcN6P , this addition specifically bypasses the GlmS reaction ( Fig 1 ) . Therefore , we suggest that GlmS ( rather than GlmM or GlmU ) is limiting the flux of carbon into PG in the ΔglmR strain . Third , GlmR and GlmS interact in vivo as judged by a bacterial two-hybrid assay ( Fig 10 ) . Fourth , previous metabolomics measurements indicate that F6P levels are ~16-fold lower during growth on gluconeogenic carbon sources when compared to glucose [13] , consistent with the requirement for GlmR under these conditions ( Fig 1 ) . Fifth , GlmR was recently found to bind UDP-GlcNAc [10] . However , mutations that abolish binding do not affect the ability of GlmR to stimulate growth under gluconeogenic conditions [10] or to provide intrinsic CEF resistance ( Fig 8 ) , as predicted by the hypothesis that UDP-GlcNAc antagonizes GlmR function ( Fig 1 ) . GlmS is recognized as the key branch-point enzyme in bacteria for diverting carbon from CCM into PG synthesis , and in eukaryotes the GlmS ortholog diverts carbon into hexosamine synthesis . Both classes of enzyme are in some cases feedback regulated by UDP-GlcNAc [43–47] . Here , UDP-GlcNAc binding is proposed to antagonize GlmR function , and therefore reduce stimulation of GlmS . In addition to GlmS , we also demonstrate that overproduction of either GlmM or GlmU , but not by enzymes downstream of the key intermediate UDP-GlcNAc , can suppress the glmR growth defect under gluconeogenic conditions . GlmS catalyzes a reversible reaction , and its product ( GlcN6P ) is a potent inhibitor of the forward reaction [30] . Moreover , GlcN6P binds to the glmS ribozyme to cleave the mRNA and suppress translation [26] . Therefore , we suggest that increasing the level of GlmM and/or GlmU likely helps pull the reaction in the forward direction and may also stimulate GlmS translation . With a defined model in hand , we can revisit the other suppressor mutations recovered both in our selection conditions ( Table 1 ) and the studies of Görke et al . [5] . As noted previously , many of the mutations that suppress glmR affect CCM . We recovered a frameshift mutation in zwf , a gene also recovered in the previous transposon-based selection for glmR suppressors [5] . Normally , Zwf diverts a substantial fraction of glucose-6-phosphate from glycolysis into the pentose phosphate pathway [48] . We speculate that in the absence of Zwf there is increased flux leading to F6P , the GlmS substrate . We also recovered a mutation in pgcA , which encodes another branch point enzyme that uses glucose-6-phosphate . Previously , it was reported that a mutation in cggR , encoding the central glycolytic genes regulator , also suppresses glmR [5] . Since a cggR null mutant will have increased levels of several key enzymes that function in both glycolysis and gluconeogenesis [49] , we speculate that this mutation alleviates the metabolic restriction in the glmR strain by increasing gluconeogenesis and therefore F6P levels . A second class of mutations that increase the fitness of the ΔglmR strain are those that lead to elevated c-di-AMP levels . This was foreshadowed by the finding that a pgpH ( formerly yqfF ) mutation suppresses glmR [5] . In our studies , we find that gdpP suppresses glmR both for growth on MH medium and for CEF resistance , whereas pgpH has a lesser effect ( S6 Fig ) . CdaA is regulated by interaction with the CdaR protein and also forms a complex with GlmM [7 , 29] . Indeed , the cdaA-cdaR-glmM genes are co-transcribed in a wide variety of species , suggesting a functional connection . This has led to the suggestion that GlmM may regulate c-di-AMP synthesis [7 , 29] . Conversely , CdaA may regulate GlmM . In this scenario , conditions that lead to elevated c-di-AMP may alter the CdaA-CdaR complex to favor a stimulatory interaction of CdaA with GlmM . Indeed , it is striking that induction of the entire cdaARglmMS operon fully restores CEF resistance to a glmR mutant ( Fig 6 ) , whereas this is not the case for the glmR glmS1 strain ( Fig 4 ) or for induction of glmS alone ( Fig 7 ) . Alternatively , c-di-AMP is also known to regulate potassium homeostasis by interaction with both protein and RNA ( riboswitch ) targets [50–53] . This c-di-AMP dependent osmolyte transport is important for maintaining turgor pressure in the cell and it has been proposed that perturbations of c-di-AMP metabolism can affect cell envelope integrity by increasing resistance against osmotic stresses [54] . A third class of suppressor mutations is in genes important for energy generation by the electron transport chain . These include mutations in qoxB , encoding cytochrome aa3 quinol oxidase , and yqiD ( ispA ) , encoding a geranyltransferase that is involved in synthesis of isoprenoid compounds including menaquinone , an electron carrier important for respiration ( Table 1 ) . Mutations in both of these loci have been previously associated with an increased ability of cells to survive the transition to L-forms that lack a peptidoglycan cell wall [55] . This observation led to a model in which a lethal consequence of cell wall defects is oxidative damage triggered by increased flux through the electron transport chain when carbon flux into peptidoglycan is eliminated [55] . Regardless of the precise mechanism , it is intriguing that mutations in these same genes were recovered as suppressors of ΔglmR . Finally , we recovered one strain containing a missense mutation in yvcJ ( Table 1 ) , the gene immediately upstream of glmR . The role of YvcJ is unknown , but it has GTPase activity , affects phosphorylation of an uncharacterized cell component , and has an apparent role in natural competence [56 , 57] . Since this strain contained an additional mutation in sigA ( Table 1 ) , further work is needed to determine the effect of the yvcJ mutation on CEF resistance . Curiously , mutants of the E . coli YvcJ homolog ( RapZ; formerly YhbJ ) lead to overproduction of GlmS [58] . RapZ appears to sense GlcN6P and regulates the processing and stability of a small RNA , GlmZ , that activates GlmS synthesis [46 , 58 , 59] . It is presently unknown whether YvcJ plays a related role in B . subtilis , perhaps by interacting either with GlmR or the glmS ribozyme . In conclusion , the results presented here highlight the importance of the GlmS branch point in regulating the flow of carbon from CCM into PG synthesis . In eukaryotes , GlmS orthologs serve as the initiating enzyme for hexosamine biosynthesis , and are sensitive to both GlcN6P product inhibition [31] and feedback regulation by UDP-GlcNAc , which binds to the isomerase domain [43 , 44] . In bacteria , GlmS is also subject to complex regulation at the level of both synthesis and activity [45–47] . In B . subtilis , GlmS is feedback inhibited by its immediate product , GlcN6P [30] , which also activates the glmS ribozyme [26] . GlmR provides another layer of regulation . Our results support a model in which GlmR stimulates GlmS activity , and we propose that binding of UDP-GlcNAc may attenuate this stimulation .
B . subtilis strains used are derived from strain 168 ( trpC2 ) ( S2 Table ) . E . coli strain DH5α was used for cloning and strain BTH101 [60] for bacterial two hybrid experiments . Bacteria were cultured in LB broth . Strains with a glmR deletion mutation were cultured on LB with 20 mM MgSO4 unless specified otherwise . Antibiotics were added to growth media when required at the following concentrations: 100 μg/ml ampicillin , 30 μg/ml chloramphenicol for E . coli , 10 μg/ml kanamycin , 10 μg/ml chloramphenicol , 5 μg/ml tetracycline , 100 μg/ml spectinomycin and 1 μg/ml erythromycin with 25 μg/ml lincomycin ( erm; macrolide-lincomycin-streptogramin B resistance ) . For cloning procedures , restriction digestion and ligation with T4 ligase was done as per manufacturer's instructions ( NEB , USA ) . Plasmids were then transformed into competent DH5α cells [61] . Cloning was confirmed by polymerase chain reaction ( PCR ) followed by Sanger sequencing . B . subtilis transformation was carried out in minimal competence media with 12 mM MgSO4 . DNA was added when cells reached OD600 of ~0 . 7–0 . 8 . Generation of B . subtilis strains overexpressing gene ( s ) at amyE was achieved using pPL82 [62] carrying gene ( s ) of interest followed by transformation into the indicated B . subtilis recipient strain . Bacillus knockout erythromycin ( BKE ) strains with various gene deletion mutations of B . subtilis were obtained from the Bacillus Genetic Stock Center ( BGSC ) [63] . Chromosomal DNA from each BKE strain was transformed into our lab strain B . subtilis 168 . The erythromycin resistance cassette was removed using pDR244 [63] , which produces Cre recombinase at the permissive temperature of 30°C , to generate in-frame deletions . pDR244 was transformed into B . subtilis strain at 30°C and plated on LB plates with spectinomycin . Colonies were picked after two overnight incubations and patched three successive times on LB plates incubated at the non-permissive temperature 42°C overnight . Strains were then patched on spectinomycin- and erythromycin-containing plates to confirm the absence of both markers . All the deletion mutants used in study are markerless deletions except Δrho ( rho::erm ) . Single nucleotide mutations glmS1 , rsiW1 and rsiW2 were reconstructed using the integration vector pMutin4 that has an erm resistance marker and lacZ [64] . A fragment of DNA with the mutation of interest was cloned into pMutin4 and confirmed with PCR and Sanger sequencing . The vector was transformed into B . subtilis where it integrated at locus by single crossover homologous recombination . Transformants were selected on plates with Erm and 40 μg/ml X-gal . After overnight incubation , a few blue color colonies were picked . Since pMutin4 integration is unstable , cells were grown without antibiotic selection three consecutive times with each time adding 1:100 dilution of cells from previous culture . Cells were then plated on LB plates with X-gal and white colonies were picked and sequenced to find those strains that retained the single nucleotide mutation of interest . Mariner transposon mutagenesis procedure was carried out in ΔglmR as described previously [65] . In brief , ΔglmR was transformed with the pMarA vector . The strain with pMarA was grown in 5 ml LB broth until mid-exponential phase and various dilutions of cells were plated on selection medium . In independent experiments CEF resistance and ability to grow on MH media were used as a selection . Spontaneous suppressors of ΔglmR were picked from the clear zone of CEF disc diffusion plates and independently from MH plates after overnight incubation at 37°C . Chromosomal DNA extracted from these suppressors was sequenced using an Illumina machine . The sequencing data were analyzed using CLC genomics workbench . Antibiotic sensitivity was tested using disc diffusion assays , which were carried out on LB medium . Strains to be tested were grown in 5 ml LB broth at 37°C with vigorous shaking to an OD600 of ~0 . 4 . 100 μl of cells were added to 4 ml top LB agar ( 0 . 7% agar ) kept at 50°C . 1 mM IPTG was added to top agar when indicated . Top agar with cells was poured over 15 ml LB bottom agar ( 1 . 5% ) plate . A Whatman paper disc ( 7mm dia ) with 6 μg CEF was put on the plate unless specified otherwise . Plates were incubated at 37°C overnight and the clear zone of inhibition was measured the next day . Values for CEF resistance ( Table 1 ) report the diameter of the zone of growth inhibition . For all histograms , the values shown have the diameter of the filter disk ( 7 mm ) subtracted from the average diameter . To test the ability of B . subtilis mutants to grow under gluconeogenic conditions we used MH medium ( Sigma-Aldrich , USA ) prepared per the manufacturer's instruction . Growth was monitored using a Bioscreen growth analyzer with 200 μl of MH broth in 100 well Bioscreen plates inoculated with 2 μl of B . subtilis strains pre-grown in LB broth at 37°C to an OD600 of ~0 . 4 . When required , glucose , MgSO4 and IPTG were added to the final concentrations of 1% , 20 mM and 1 mM respectively . Strains of interest were grown to an OD600 of ~0 . 5 . 1 . 5 ml of culture was used for RNA extraction . RNA isolation ( Qiagen , USA ) and cDNA preparation ( Thermofisher , USA ) was carried out as suggested by the manufacturer . qRT-PCR was carried out using a Bio-Rad iTaq universal SYBR green super mix . 23S rRNA was used to normalize the cycle threshold ( Ct ) value . For GlmS measurements , ΔglmR and ΔglmR glmS1 strains were grown in LB medium to an OD600 of ~0 . 3 at 37°C with shaking . 30 ml of culture was withdrawn and centrifuged at 5000 rpm for 10 minutes . Cell pellets were frozen at -20°C . Pellets were washed once with 1X phosphate buffer saline ( pH 7 . 4 ) . 150 μl of lysis buffer ( 20 mM tris-HCl , 100 mM NaCl , 1 mM EDTA , 1 mM DTT , 10% glycerol and protease inhibitor cocktail ) was used to re-suspend the cell pellets . One tablet of protease inhibitor cocktail from Roche diagnostics was added to 10 ml of lysis buffer . Cells were lysed by sonication . After centrifugation cell lysates were transferred to fresh tubes . Protein concentration was measured by Bradford assay ( Bio-Rad ) . 5 μg of protein was run on a 4–15% gradient gel from Bio-Rad . Protein was transferred onto a PVDF membrane using a Bio-Rad transblot turbo transfer system . The membrane was blocked with 5% milk powder for one hour followed by overnight incubation with primary anti-GlmS polyclonal antibodies [66] added to 1:3000 dilution in 1X tris buffer saline with 0 . 1% tween 20 and 0 . 5% milk powder . After three washes , the membrane was incubated with a 1:3000 dilution of HRP conjugated anti-Rabbit antibodies ( Sigma ) . Bands were visualized on a Bio-Rad Chemidoc MP imaging system . Strains of interest were grown in 5 ml LB medium to an OD600 of ~0 . 4 . 100 μl of cells were added to 4 ml top MH agar ( 0 . 7% agar ) preheated at 50°C and was laid on a 15 ml MH agar ( 1 . 5% ) plate . A disc with 0 . 5 mg GlcNAc ( Sigma , USA ) was put on the plate . After overnight incubation at 37°C , the zone of growth surrounding the disc was measured . DNA changed encoding single amino acid substitutions ( GlmRY255A , GlmRR301A and GlmRR301E ) were generated at the native glmR locus using CRISPR editing as described [67] . In brief , oligonucleotides encoding a 20 nucleotide gRNA with flanking BsaI sites and a repair fragment carrying mutations of interest with flanking SfiI restrictions sites were cloned sequentially into vector pJOE8999 followed by transformation into E . coli DH5α cells . The resultant plasmid was transformed into recipient B . subtilis strain and cells were plated on 15 μg/ml kanamycin plates with 0 . 2% mannose . Transformation was carried out at 30°C as pJOE8999 cannot replicate at higher temperatures . The transformants were patched on LB agar plates and incubated at the non-permissive temperature of 42°C . The loss of vector was confirmed by the inability of selected isolates on kanamycin plates . The presence of the desired mutations was confirmed by Sanger sequencing . Vectors pT18 and pT25 and strains for bacterial two hybrid were prepared as described [60] . E . coli BTH101 strains carrying pT18 and pT25 with genes of interest were grown in LB broth overnight at 30°C with 100 μg/ml ampicillin , 50 μg/ml chloramphenicol and 0 . 5 mM IPTG . 10 μl of cells were spotted on LB plate with 100 μg/ml ampicillin , 50 μg/ml chloramphenicol , 0 . 5 mM IPTG and 40 μg/ml X-gal . Plates were incubated overnight at 30°C . In silico analysis was carried out using NUPACK web application [68] .
|
Bacterial cells are surrounded by a peptidoglycan cell wall that is , under most conditions , required for viability . Synthesis of the cell wall requires a considerable diversion of resources from central carbon metabolism into a lipid-linked precursor ( lipid II ) that is exported from the cell for wall assembly . Here , we propose that GlmR presides over a new mechanism for the regulation of carbon partitioning between central metabolism and peptidoglycan biosynthesis: GlmR activates the GlmS-dependent diversion of carbon from the glycolytic pathway into peptidoglycan synthesis . This effect is particularly important during gluconeogenesis since the GlmS substrate fructose 6-phosphate is present at a reduced level under these conditions .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"antimicrobials",
"pharmacologic",
"analysis",
"deletion",
"mutation",
"medicine",
"and",
"health",
"sciences",
"pathology",
"and",
"laboratory",
"medicine",
"antibiotic",
"susceptibility",
"testing",
"pathogens",
"drugs",
"bacillus",
"microbiology",
"operons",
"mutation",
"prokaryotic",
"models",
"antibiotics",
"materials",
"science",
"experimental",
"organism",
"systems",
"pharmacology",
"dna",
"macromolecules",
"bacteria",
"bacterial",
"pathogens",
"research",
"and",
"analysis",
"methods",
"polymers",
"polymer",
"chemistry",
"animal",
"studies",
"medical",
"microbiology",
"bacterial",
"disk",
"diffusion",
"microbial",
"pathogens",
"chemistry",
"biochemistry",
"point",
"mutation",
"nucleic",
"acids",
"peptidoglycans",
"bacillus",
"subtilis",
"genetics",
"microbial",
"control",
"biology",
"and",
"life",
"sciences",
"biosynthesis",
"physical",
"sciences",
"materials",
"organisms"
] |
2018
|
A metabolic checkpoint protein GlmR is important for diverting carbon into peptidoglycan biosynthesis in Bacillus subtilis
|
The emergence of dengue throughout the tropical world is affecting an increasing proportion of adult cases . The clinical features of dengue in different age groups have not been well examined , especially in the context of early clinical diagnosis . We structured a prospective study of adults ( ≥18 years of age ) presenting with acute febrile illness within 72 hours from illness onset upon informed consent . Patients were followed up over a 3–4 week period to determine the clinical outcome . A total of 2 , 129 adults were enrolled in the study , of which 250 ( 11 . 7% ) had dengue . Differences in the rates of dengue-associated symptoms resulted in high sensitivities when the WHO 1997 or 2009 classification schemes for probable dengue fever were applied to the cohort . However , when the cases were stratified into age groups , fewer older adults reported symptoms such as myalgia , arthralgia , retro-orbital pain and mucosal bleeding , resulting in reduced sensitivity of the WHO classification schemes . On the other hand , the risks of severe dengue and hospitalization were not diminshed in older adults , indicating that this group of patients can benefit from early diagnosis , especially when an antiviral drug becomes available . Our data also suggests that older adults who present with fever and leukopenia should be tested for dengue , even in the absence of other symptoms . Early clinical diagnosis based on previously defined symptoms that are associated with dengue , even when used in the schematics of both the WHO 1997 and 2009 classifications , is difficult in older adults .
The mosquito-borne dengue virus ( DENV ) has emerged in the latter half of the 20th century to become an important cause of morbidity and mortality . Over half of the world's population live at risk of infection annually [1] . Infection with any of the four antigenically distinct virus serotypes results in a wide range of clinical manifestation , from mild undifferentiated febrile illness to classical dengue fever ( DF ) to the life-threatening dengue hemorrhagic fever ( DHF ) and dengue shock syndrome ( DSS ) [2]; the latter two syndromes are characterized by plasma leakage resulting from alteration in microvascular permeability [3] , [4] , [5] . Supportive fluid therapy is effective in preventing the onset of shock from excessive plasma leakage but relies on early diagnosis of dengue and monitoring for the clinical signs of plasma leakage [5] . Furthermore , early diagnosis would have an increasingly important role with the development of antiviral therapies , because the effectiveness of antivirals is likely to be high only if initiated early after illness onset [6] , during the short viremic phase [7] , [8] . Differentiating dengue from other causes of febrile illness clinically is difficult to achieve reliably during the early phase of illness [9] . In most dengue endemic countries , access to diagnostic laboratories is limited and dengue diagnosis may rely solely on clinical recognition . Moreover , even where diagnostic laboratory services are available , virological tests are requested only upon a clinical suspicion of dengue , based on the presenting symptoms and signs . The World Health Organization ( WHO ) developed a set of guidelines ( WHO 1997 ) [10] , which was recently revised ( WHO 2009 ) [11] , to aid diagnosis and disease classification for case management , but how these schemes perform in the context of early clinical diagnosis needs further evaluation . Furthermore , dengue infection in adults is showing an increasing trend globally , both among travellers [12] , [13] as well as those residing in endemic regions [14] , [15] , [16] , [17] , [18] , [19] , [20] . The collective clinical experience of dengue in adults is limited compared to that in children [5] , [21] , [22] , [23] , [24] , upon which the criteria for dengue diagnosis in the WHO classification schemes have been developed . Adults appear to be at lower risk of DHF compared to children [25] , but complications such as bleeding and severe organ impairment are more common [26] , [27] , [28] , [29] . How increasing age affects the clinical presentation of dengue infections and hence early clinical dengue diagnosis , is unknown . We undertook a multi-centre longitudinal study of adult dengue infection to characterize the early phase of dengue illness . We report here the observations obtained from 2 , 129 patients enrolled over a five-year period . Our findings indicate that the symptoms associated with dengue are less frequently reported in older adults , making early clinical diagnosis more difficult with increasing age of the cases .
The study protocol was approved by the National Healthcare Group Domain Specific Review Board ( DSRB B/05/013 ) , as well as the Institutional Review Boards of the National University of Singapore and DSO National Laboratories . Enrolment of study participants was conditional on appropriate written informed consent administered by a study research nurse . All biological material collected were de-identified after completion of demographic and clinical data collection . The protocol for patient recruitment was described previously [30] . Adult patients ( age ≥18 years ) presenting with acute onset fever ( a presenting temperature of ≥37 . 5°C or a history of fever ≥37 . 5°C for less than 72 hours ) at selected public primary healthcare clinics were eligible for study inclusion . Upon consent , demographic , clinical and epidemiological information were collected on a standardized data entry form on three occasions: 1–3 days post fever onset ( Visit 1 ) , 4–7 days post fever onset ( Visit 2 ) and 3–4 weeks post fever onset ( Visit 3 ) during convalescence [30] . Venous blood was collected at every visit . Serum was stored at −80°C until use . From January 2008 , a nasal swab was also collected , which was immediately placed in viral transport medium , and tested for respiratory pathogens in the laboratory . Recruited patients who were hospitalized for further management within the defined study period were followed up in the hospital or post-discharge . These patients received medical treatment at the discretion of their attending physicians . Hospitalization information and investigation data were extracted from electronic hospital records or discharge summaries using a pre-defined protocol . The decision to hospitalize a patient was left to the discretion of the treating physician . However , national guidelines on dengue management are available and are adopted by the healthcare institutions in Singapore [31] . Hospitalization criteria in these guidelines include: significant bleeding , fall in blood pressure , dehydration and postural hypotension , rise in hematocrit of 20% or greater compared to the baseline , platelet count <80 , 000 cells/mm3 , severe vomiting or diarrhea , severe abdominal pain , and elderly patients with co-morbidities who are unwell . The WHO 1997 [10] and WHO 2009 [11] classification schemes were applied to the clinical data obtained at Visit 1 . WHO 1997 [10] classifies acute febrile illness as probable DF based on headache , retro-orbital pain , myalgia , arthralgia , rash , hemorrhagic manifestations and leukopenia . In contrast , WHO 2009 [11] utilizes two or more clinical manifestations for a probable DF classification , which are , nausea/vomiting , rash , aches and pains , tourniquet test positive , leukopenia and any warning signs . For this analysis , leukopenia was defined as a white blood cell count ( WBC ) of below 4 , 500 cells/µL and joint pain/muscle pain was included under aches and pains . The tourniquet test was not carried out in the study and hence was not included in the analysis . Three of the seven warning signs [11] , namely clinical fluid accumulation , liver enlargement >2cm and increase in haematocrit concurrent with rapid decrease in platelet count were also not included for analysis as these parameters are not routinely monitored by primary healthcare clinicians in patients presenting with acute febrile illness . Likewise , persistent vomiting could not be assessed solely on a single visit . Drowsiness was counted under lethargy/restlessness . Hence , the warning signs used in this analysis were abdominal pain , mucosal bleed , and drowsiness . The WHO classification schemes and the factors included in this study are summarized in Table 1 . A full blood count was performed on anticoagulated blood collected at all time points using a bench-top , FDA-approved hematocytometer ( iPoch-100 , Sysmex , Japan ) . IgM and IgG antibodies against DENV were detected using commercially available ELISAs ( Panbio , Brisbane , Australia ) according to manufacturer's instructions . RT-PCR was performed to detect DENV RNA and determine the serotype of the DENV as previously described [32] . Results were analyzed with the LightCycler software version 3 . 5 . Reactions with high crossover point ( Cp ) or ambiguous melting curve results were further analyzed by 2% agarose gel electrophoresis , to confirm the presence of the correctly sized amplicon . RT-PCR was also carried out to test for influenza A and B viruses in the nasal swabs . Briefly , viral RNA was extracted from the viral transport medium using QIAamp Viral RNA mini kit ( Qiagen , Hilden , Germany ) according to the manufacturer's protocol . Viral RNA was reversed transcribed using random hexamer primers with Superscript III ( Invitrogen ) according to the manufacturer's protocol . Influenza A and B viruses were detected using previously described methods [33] , [34] . Statistical analysis was performed using GraphPad Prism v5 . 0 d . For continuous variables , the Mann Whitney U test was applied to determine statistical significance . If more than two groups of continuous variables were analysed , the Kruskal-Wallis test was used . Fisher's exact test and the chi-square test with Yates' continuity correction were used in comparisons of sensitivity and specificity as well as rates of presenting symptoms , pre-existing co-morbidities , hospitalization and severe dengue . All analyses were two-tailed . A P value of less than 0 . 05 was considered statistically significant .
The clinical features of dengue , OFI and influenza at Visit 1 are shown in Table 3 . Arthralgia , loss of appetite , nausea , vomiting , altered taste sensation , rashes and skin sensitivity were more frequently reported in patients with dengue compared to OFI and influenza ( Table 3 ) . Presenting aural temperature was higher in patients with dengue compared to OFI but not influenza . The mean platelet , WBC , lymphocyte and neutrophil counts were significantly lower in dengue compared to OFI or influenza ( Table 4 ) . Dengue patients experienced a longer duration of illness compared to OFI and influenza and a higher proportion of them ( 46 . 4% ) were hospitalized ( Table 3 ) . The factors identified at Visit 1 that were associated with hospitalization are shown in Table 5 . Hospitalized dengue cases were significantly older than those that received only ambulatory care . The platelet , WBC , lymphocyte and neutrophil counts were significantly lower in hospitalized compared to ambulatory patients . In addition , a higher rate of secondary infection , as defined by a positive DENV IgG finding on the blood sample taken at Visit 1 , and a lower crossover point of the real-time RT-PCR , which is indicative of higher viremia levels , were also observed in the hospitalized patients ( Table 5 ) . Among the 116 hospitalized cases , records were available for the 110 patients that were admitted in public hospitals . The remaining 6 patients were treated in private hospitals and their records were not available for review . Of the 110 , 20 ( 18 . 2% ) developed an illness consistent with the classification of severe dengue under the WHO 2009 guidelines [11] . The demographics of these 110 cases are shown in Table 6 . Of these 20 , 11 ( 55% ) had severe plasma leakage in the form of either a pulse pressure difference of less than 20 mmHg , a systolic pressure of less than 90 mmHg , pleural effusion or ascites , five ( 25% ) bled internally requiring transfusion , three ( 15% ) had liver transaminases that were elevated above 1000 IU and one ( 5% ) had a temporally associated seizure without a previous history of epilepsy . To examine the impact of increasing age on the clinical presentation and outcome of dengue infection , patients were separated into 5 age groups ( 18–25 , 26–35 , 36–45 , 46–55 and those 56 years of age and above ) . The frequency of patients with symptoms associated with dengue fever , namely myalgia , arthralgia , retro-orbital pain and mucosal bleeding reduced significantly with increasing age ( Table 7 ) . We have previously shown that the WHO 1997 and 2009 classification schemes are highly sensitive although they lacked specificity [35] . Here , our data indicates that with reducing rates of the above symptoms , the sensitivity of the WHO classification schemes in differentiating dengue from OFI decreased with age ( Table 8 ) . Collectively , the results indicate that clinical recognition of dengue becomes harder as the age of the patients increase . We also observed that leukopenia was more marked with increasing age , although this difference was not statistically significant among the dengue patients in different age groups ( Figure 1 ) . However , when compared to patients with OFI and influenza in the same age groups , the difference in WBC appeared greater with increasing age ( Figure 1 ) . We thus tested if the use of leukopenia alone can differentiate dengue from OFI . Using a receiver operating characteristic ( ROC ) analysis , the area under the curve ( AUC ) values increased with age ( Table 9 ) . Likewise , the sensitivity of this test increased from 53 . 1% in the 18–25 year old group to 81 . 6% in those 56 years old and above . Specificity was over 85% in all age groups ( Table 9 ) . Significant differences in the rates of those with leukopenia were also observed across the age groups when comparing dengue and influenza patients ( Figure 1 ) . ROC analysis of platelet , neutrophil and lymphocyte counts also showed statistically significant AUC but these were all lower than WBC alone ( data not shown ) .
In Singapore , DHF was first reported in the 1960s and quickly became a major cause of childhood mortality . With the implementation of vector control leading to reduced Aedes aegypti population , the incidence of DHF declined and Singapore experienced a 15-year period of low DF/DHF incidence [15] . However , since the 1990s , dengue has re-emerged as a consequence of a number of different factors [15] , [36] , [37] and the highest incidence has been observed in adults with relatively few pediatric cases . This setting provides us with an opportunity to examine the clinical features of dengue in adults . We structured a prospective study enrolling adults presenting with an acute febrile illness of less than 72 hours duration , with follow up over a 3–4 week period to determine the clinical outcome . This enabled us to capture the early features of dengue illness and systematically compare them to other febrile illnesses including influenza , which is another viral infection commonly encountered in the primary healthcare setting . Though Singapore experienced an outbreak of chikungunya from mid-2008 to early 2009 [38] , none of our cases tested positive to chikungunya virus . A limited subset of these patients have been previously analysed and reported elsewhere . These reports have either described the study design along with the preliminary clinical and epidemiological description of the adult dengue cases [30] , the development of a algorithms for early dengue diagnosis and triaging for case management [39] or a cross-sectional comparison on the usefulness of NS1 rapid test relative to clinical diagnosis [35] , [40] . However , a full analysis of the patients enrolled over a 5-year period has not been previously described . Our data indicates that clinical features associated with dengue are relatively common during the first 72 hours of illness , which represents the first time patients with acute febrile illness seek medical attention . We have recently shown that the high sensitivity of the WHO dengue classification schemes can be useful in ruling out cases of acute febrile illness from further laboratory investigation for a confirmatory dengue diagnosis [35] . Our findings here indicate that while this is true for most age groups , caution needs to be exercised in older adults as the frequency of symptoms and signs in the WHO classification schemes reduced significantly with increasing age of infection . This would thus render the process of early diagnosis more difficult , as was suggested in a case report [41] , thereby reducing the effectiveness of any antiviral intervention [6] when these become available . The longitudinal study also enabled us to assess the burden dengue imposes on the adult population . The median age of the dengue cases was 39 years old , indicating that the majority of dengue cases are in the productive working age . Overall , dengue patients were ill for a longer period , had greater rates of hospital admission and , if admitted , were hospitalized for a longer period than OFI or influenza . The risk of hospitalization also appeared to increase with age . Given the trend of increasing age of dengue cases in Singapore [15] , the burden dengue poses on society is thus likely to worsen . These considerations , however , may be confounded by the availability of a national guideline for admitting dengue patients for hospitalized treatment . In contrast , the decision to hospitalize patients with OFI or influenza is based solely on the clinical judgement of the emergency physician . Indeed , it is entirely possible that the present guidelines have resulted in over-hospitalization of dengue cases as out of the 110 hospitalized cases reviewed , only 20 ( 18% ) had severe dengue . Furthermore , pre-existing chronic illness are more common in older adults and this criterion in the guideline could have resulted in the increased hospitalization rates with age . However , our study did also observe an increasing trend of severe dengue with increasing age , suggesting that age alone may have an impact on disease outcome . In a retrospective study of the 1981 DHF outbreak in Cuba , peak mortality rates were observed in children and in adults above 60 years old [42] . The underlying mechanism on how age influences clinical outcome , with or without pre-existing chronic illness , however , cannot be gleaned from this study as there were relatively few severe dengue cases . To improve clinical suspicion of dengue in older adults who present with acute febrile illness , we suggest the use of a simple WBC . Leukopenia has been previously reported to be associated with dengue infections and its use as a diagnostic tool was proposed before the availability of RT-PCR or NS1 antigen assays [43] . The ROC analyses in the different age groups in this study indicate that the usefulness of leukopenia in aiding an early clinical diagnosis of dengue is not consistent throughout all age groups but instead increases sharply with age . Platelet count is not below the normal limits at this stage of the illness in most patients , although it was significantly lower in patients with dengue compared to OFI or influenza , and the usefulness of thrombocytopenia in triggering a differential diagnosis improves in the later stages of illness . While the underlying explanation for the increasing trend of leukopenia with age is not known , it could be an inexpensive tool to enable clinicians to decide whether to pursue additional laboratory tests for dengue or not . It would also be interesting to explore if fever and leukopenia , plus one or more of the list of symptoms in the WHO 2009 classification , could be used for a clinical diagnosis of probable dengue in this group of adults . To our knowledge , this is the largest prospective study that examined early clinical diagnosis of dengue in adults in a primary healthcare setting [44] . A limitation of this study , however , is the small number of cases that met the WHO 2009 classification's criteria for severe dengue . We have thus not analysed here how predictive the early clinical or haematological parameters are in determining the development of severe illness , which has been addressed elsewhere [39] , [45] , [46] . In conclusion , early clinical diagnosis based on previously defined symptoms that are associated with dengue , even in the schematics of both the WHO 1997 and 2009 classifications , is difficult in older adults . The presence of leukopenia in older adults that present with an acute febrile illness should trigger a differential diagnosis of dengue for further laboratory confirmation .
|
Dengue infection in adults has become increasingly common throughout the world . As most of the clinical features of dengue have been described in children , we undertook a prospective study to determine the early symptoms and signs of dengue in adults . We show here that , overall , dengue cases presented with high rates of symptoms listed in the WHO 1997 or 2009 classification schemes for probable dengue fever thus resulting in high sensitivities of these schemes when applied for early diagnosis . However , symptoms such as myalgia , arthralgia , retro-orbital pain and mucosal bleeding were less frequently reported in older adults . This trend resulted in reduced sensitivity of the WHO classification schemes in older adults even though they showed increased risks of hospitalization and severe dengue . Instead , we suggest that older adults who present with fever and leukopenia should be tested for dengue , even in the absence of other symptoms . This could be useful for early clinical diagnosis in older adults so that they can be monitored and treated for severe dengue , which is especially important when an antiviral drug becomes available .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"infectious",
"diseases",
"dengue",
"viral",
"diseases"
] |
2011
|
The Early Clinical Features of Dengue in Adults: Challenges for Early Clinical Diagnosis
|
From bacteria to multicellular animals , most organisms exhibit declines in survivorship or reproductive performance with increasing age ( “senescence” ) [1] , [2] . Evidence for senescence in clonal plants , however , is scant [3] , [4] . During asexual growth , we expect that somatic mutations , which negatively impact sexual fitness , should accumulate and contribute to senescence , especially among long-lived clonal plants [5] , [6] . We tested whether older clones of Populus tremuloides ( trembling aspen ) from natural stands in British Columbia exhibited significantly reduced reproductive performance . Coupling molecular-based estimates of clone age with male fertility data , we observed a significant decline in the average number of viable pollen grains per catkin per ramet with increasing clone age in trembling aspen . We found that mutations reduced relative male fertility in clonal aspen populations by about 5 . 8×10−5 to 1 . 6×10−3 per year , leading to an 8% reduction in the number of viable pollen grains , on average , among the clones studied . The probability that an aspen lineage ultimately goes extinct rises as its male sexual fitness declines , suggesting that even long-lived clonal organisms are vulnerable to senescence .
Many species of animals , and even bacteria , demonstrate a decline in survivorship or reproductive performance with increasing age ( “senescence” ) [1] , [2] . Evidence for senescence in perennial plants , however , is scant [3] , [4] , [7] . One feature that distinguishes plants from many animals is indeterminate growth . Indeterminate growth is particularly extreme in clonal plants , where an individual ( genet/clone ) can continually produce new physiological and demographic units ( ramets ) without undergoing sex . Senescence is thought to be a by-product of natural selection , acting most effectively early in life , when many species have the greatest reproductive value [8] . Mutations that are deleterious to late-life survival and reproduction can spread because of early-life benefits and/or because selection against them is too weak when few individuals survive to old age [9] . Because many perennial plants and especially clonal plants continue to grow throughout life , their reproductive potential can rise over time [10] . This rising reproductive potential counters the decline in natural selection that accompanies aging , allowing selection to remain effective even in late life . It is this characteristic of indeterminate growth in perennial plants that has led some to speculate that these organisms defy aging [1] , [3] , [10]–[12] . Indeterminate growth is , however , a double-edged sword . Although it facilitates genet growth and renewal , it also results in more mitotic cell divisions , increasing the accumulation of somatic mutations [13] . Because somatic mutations arise in the cells of the plant body ( roots and/or above-ground mass ) , they can be passed on to subsequent ramet generations . Furthermore , because plants do not sequester their germline , these mutations can be transmitted to reproductive organs and subsequently to sexual offspring [14] . During clonal growth , somatic mutations that negatively impact sexual fitness are free to accumulate as long as they have little or no deleterious effect on clonal growth [5] , [6] . This led us to hypothesize that long-lived clonal organisms might suffer senescence . Typically , senescence results from an age-related decline in the intensity of natural selection , which allows late-acting mutations to accumulate within a population of individuals . Long-lived clones , however , might senesce because somatic mutations that reduce sexual fitness accumulate within a population of ramets ( i . e . , within a clone ) . Importantly , we expect such clonal senescence to occur even when the intensity of natural selection does not decline with age , because selection on sexual fitness is absent during clonal growth . This is not true of traits like root growth , ramet production , average photosynthetic rates , hormone sensitivity , or even a clone's susceptibility to stress from the abiotic environment , all of which are likely to remain under selection within the clone . To test if senescence of sexual fitness occurs at the level of the clone , we asked whether older clones of Populus tremuloides ( trembling aspen ) exhibit lower reproductive performance . Specifically , we examined pollen production and viability among male clones from natural stands in British Columbia , Canada . Coupling molecular-based estimates of clone age with pollen data , we observed a significant decline in male fertility with increasing clone age . Populus tremuloides is a dioecious tree that forms clones consisting entirely of male or of female ramets . Each ramet within a clone is capable of both sexual and asexual reproduction . Sexual reproductive maturity is reached between 10–20 y of age while asexual maturity is reached at 1 y [15] . Individual reproductive shoots produce inflorescences ( catkins ) that often have between 80 and 100 flowers [16] . Projections based on the size of intermountain aspen clones , formed from lateral root suckers , suggest that clones vary in size from 1 . 5 to 43 . 6 hectares and that some of the oldest clones might even be as old as one million years [17] , [18] . Because size-based age estimates might be biased if local ecological conditions constrain growth , we instead estimated clone age by measuring the amount of neutral genetic diversity that had accumulated within each clone at 14 nuclear microsatellite loci ( Materials and Methods ) [19] . Because aspen is dioecious , we assayed the fertility of male clones by sampling whole catkins and quantifying pollen viability and pollen count ( Materials and Methods ) . Thus , our measure of male fertility for each genet/clone was one component of male sexual fitness , the average number of viable pollen grains per ramet per catkin . Although we recognize that male sexual fitness includes other components such as pollen germination , tube growth , number of anthers , and number of flowers , as shorthand we use the terms male sexual fitness and male fertility interchangeably .
In a previous study , we measured the accumulation of neutral somatic mutations within 20 clones from Riske Creek , British Columbia , by genotyping 719 ramets at 14 microsatellite loci [19] . Because variation within a clone is expected to accumulate over time since initiation from a seed , we used genetic diversity within the kth clone ( πk ) to estimate the age of the clone [19] . We found substantial variation among clones for male fertility , with older clones exhibiting significantly lower numbers of viable pollen than younger clones ( Figure 1a , b ) . While our estimates of clone age are subject to error , we infer the same pattern in the raw data ( Figure S6 and Figure S7 ) : clones exhibiting more variation at microsatellite loci produce a significantly lower number of viable pollen grains per catkin per ramet . The observed variation in male sexual fitness among clones could not be explained by factors such as date of flower collection ( F2 , 94 = 2 . 243 , p = 0 . 11 , n = 96 ) and inbreeding level ( F1 , 18 = 1 . 142 , p = 0 . 30 , R2 = 0 . 06 , n = 19 ) ( Text S1; Figures S1 , S2 , and S4 ) . Furthermore , there was no relationship between ramet age and male sexual fitness , suggesting that ramet age plays a minor role relative to genet age with respect to senescence via the accumulation of mutations deleterious to male fertility ( F1 , 94 = 3 . 801 , p = 0 . 054 , R2 = 0 . 04 , n = 95 ) ( Figure S3 ) . Empirical studies suggest that the presence of fungal pathogens and insect herbivory can exert a strong influence on reproductive success; thus we investigated the relationship between male fertility and these environmental factors . To quantify the effect of the variable of interest , clone age , on male sexual fitness , we performed a multiple linear regression accounting for the environmental factors that were substantially correlated with male sexual fitness . The best model consisted of three predictors: a composite measure reflecting the mechanical damage sustained by the average ramet in the clone ( fourth principal component , PCD4 ) , a composite measure reflecting the levels of moisture ( second principal component , PCE2 ) , and clone age ( F3 , 16 = 7 . 312 , p = 0 . 0026 , adjusted R2 = 0 . 50 , Akaike Weight = 0 . 55 ) ( Table 1 , Figure 1c ) . Holding these environmental effects constant , average number of viable pollen grains per catkin per ramet again declined significantly with clone age ( Figure 1 , Figure S6 , Text S1 ) . If there were a trade-off between sexual and asexual function , selection could have facilitated the observed loss in male fertility with clone age because mutations deleterious to male sexual fitness would have increased vegetative success and risen in frequency during clonal growth . To investigate this possibility , we asked whether ramets with lower fertility exhibited higher asexual fitness , measured as the rate of increase in ramet volume per year . Specifically , we divided the volume of a ramet , V ( in m3 , obtained from the formula for a cylinder , V = πh ( d/2 ) 2 , where d is the diameter at breast height and h is ramet height ) by the age of the ramet from tree-ring data . We found no correlation between the male sexual fitness of male ramets and their growth rate ( Figure 2a ) . The accumulation of alleles that were beneficial to asexual growth might not have affected ramet growth but could have affected the rate of clonal expansion . Alternatively , the observed correlation between male sexual fitness and clone age could have been caused by genetic variation among clones ( reflecting genetic variation among the seeds that established the clones ) , where some clones had higher asexual fitness and were more likely to survive for long periods of time but at a cost to male fertility . In either case , we would expect a negative relationship at the clone level between male fertility and asexual fitness . There was , however , no substantial correlation between male fertility and three potential measures of clone fitness: clone area ( r = −0 . 33 , t = −1 . 465 , df = 18 , p = 0 . 16 ) , clone perimeter ( r = −0 . 36 , t = −1 . 662 , df = 18 , p = 0 . 11 ) , and the maximum distance between any two ramets in the clone , Dmax ( Figure 2c ) . Furthermore , an analysis of variance indicated that much of the variation in clone fertility can be explained by clone age ( or , equivalently , genetic diversity , πk ) with very little attributed to the size of a clone ( ANOVA: clone age: F = 11 . 55 , p = 0 . 0034; clonal spread ( Dmax ) : F = 2 . 972 , p = 0 . 10 ) . We also considered whether the accumulation of mutations reducing male fertility might be associated with an increased density of ramets within a clone . Our previous work [20] determined that a patch is largely comprised of a single clone with smaller clones near the edge , so we used estimates of the density of ramets within a 10 m×10 m square at the centre of each patch as a proxy for the density of ramets within a clone . There was , however , no significant relationship between density of ramets in a patch and male sexual fertility ( r = −0 . 124 , t = −0 . 5031 , df = 16 , p = 0 . 62 ) . We caution that all of the above measures provide only rough estimates of clone fitness . To measure clone fitness accurately and to determine any trade-offs with sexual fitness would require a long-term common garden study examining the rates of clonal spread from seed . Thus , while we find no evidence that trade-offs ( negative pleiotropy ) explain the reduction in male fertility with clone age , we do not exclude this possibility .
Evidence that perennial plants exhibit demographic senescence is scarce because obtaining data on survivorship or fecundity from late-life perennials typically requires long-term demographic data . This proves difficult even in short-lived perennials . For example , to demonstrate aging in Plantago lanceolata , one study followed 30 , 000 individuals over 7 y , finding that , during times of stress , older-aged cohorts had significantly higher mortality rates relative to younger-aged cohorts [7] . Unlike many herbaceous perennials , most woody tree species are large in stature and have an extended life cycle , rendering such experiments impractical . Although our data are not without their caveats and limitations , our work offers a novel approach for obtaining late-life demographic data on a variety of clonal species by using a molecular clock to age individual clones . We observed a significant decline in male fertility with clone age ( Figure 1 ) , causing a reduction of 8% in the average number of viable pollen grains per catkin per ramet , on average , among the clones sampled . Given the maximum age of the oldest clone was ∼10 , 000 y based on glacial history in this region of British Columbia , we estimate that the rate of decline in average number of viable pollen grains was 5 . 8×10−5 per year ( 95% CI: 3 . 8×10−6 to 1 . 1×10−4 based on the multiple linear regression , Figure 1c ) . Given a minimum age of the youngest clones of 71 y based on tree ring data , the estimated decline was 1 . 6×10−3 per year ( 95% CI: 1 . 03×10−4 to 3 . 1×10−3 ) . Assuming a constant linear decline , it would thus take between ∼500 and 20 , 000 y for a clone to lose sexual function with respect to pollen quantity and quality . A plausible explanation for the observed decline in male sexual fitness with increasing clone age is that somatic mutations that negatively impact pollen fitness accumulate over time . As is the case with meiotic mutations , somatic changes that arise during mitosis can be neutral , deleterious , or beneficial . While somatic selection among cell lineages would act to eliminate deleterious somatic mutations , those mutations that have little to no effect on clonal growth but that reduce sexual fitness are free to accumulate . As mutations affecting fitness tend to be deleterious and partially recessive , at least some somatic mutations may be largely masked in the diploid clone phase but be deleterious in the haploid pollen stage , reducing pollen fitness among older clones . Although we observed a higher number of somatic mutations at microsatellite marker loci among the clones that exhibit reduced male fertility ( Figure 1 , Figure S6 , Figure S7 ) , we have no reason to expect these marker loci are directly responsible for the observed declines in sexual fitness . These marker loci only confirm that somatic mutations can and do accumulate . Two previous studies on clonal ferns showed a direct link between somatic mutations and reduced fitness , using segregation patterns of deleterious mutations among gametophytes obtained from fern clones [21] , [22] . In long-lived plant taxa where higher per generation mutation rates are often found ( [6] , [23] , [24] but see [25] ) , post-zygotic somatic mutations may contribute substantially to the total mutation rate and genetic load [25] , [26] . An alternative explanation is that somatic mutations reducing sexual function have spread within these clones because they enhance clonal fitness ( negative pleiotropy ) , for example , due to trade-offs in resource allocation . We looked for evidence of such trade-offs at two levels: ramet and clone . We found no evidence of a relationship between male fertility and volumetric growth per year ( m3/y ) of a ramet ( Figure 2a ) . Additionally , a trade-off at the level of the clone might predict that larger-sized clones ( regardless of clone age ) should exhibit a reduced sexual fitness when compared to smaller-sized clones , due either to the accumulation of somatic mutations that enhance clonal spread and/or to genetic variation among the seeds that established the clones . We did not , however , find any significant correlations between sexual fitness and three potential measures of clone size/fitness ( Figure 2c ) . Nor was there any evidence that clone size was related to clone age [19] . Although we did not detect evidence for negative pleiotropy , we cannot rule out the possibility that the loss of sex in aspen was driven by the spread of beneficial mutations that improve cell- or ramet-proliferation . A final alternative explanation for why older clones exhibit lowered reproductive performance is that heritable epigenetic changes accumulate that reduce sexual traits . It has been shown previously that allopolyploidization , a change in reproductive mode , and nutritional stresses can lead to both genetic and epigenetic re-patterning [27] . Furthermore , there is growing evidence that epigenetic mechanisms like DNA methylation and siRNAs are responsible for natural population variation in traits like flower symmetry , self fertility , flower initiation , and number of reproductive organs [27] . Although epigenetic mechanisms like paramutation may be highly stable [28] , it is unknown if such heritable epigenetic changes could persist over hundreds to thousands of years and over multiple ramet generations . With current advances in sequencing technologies , it will become increasingly cost-effective to assess the age of clones using a molecular clock and to ask whether sexual fitness declines with clone age as we have found in trembling aspen . Furthermore , given that previous work has shown an increased transmission of deleterious mutations through the sperm than egg [29] , it would be interesting to assess whether female versus male clones differ in the amount of senescence that they exhibit . In long-lived perennials and clonal plants , substantial numbers of somatic mutations can accumulate over time [6] , [13] , [23] , [24] , [30] . This is because in plants there is no distinction between the soma and germline . Somatic cell lineages are not protected in a quiescent non-replicative state and can actively divide , eventually giving rise to gametes whenever reproductive tissues form . Although somatic mutations need not be immediately life-threatening , they can have a devastating impact on sexual function when they are unmasked in the haploid state . This suggests that , in the face of accumulating somatic mutations , plant clones may have a limited time span within which sexual function remains high . The aspen clones that we examined have lost , on average , 8% of their fertility , with less than a quarter of the pollen fertility remaining in the oldest clone ( Figure 1 ) . Without sex , clones of Populus tremuloides are unable to disperse beyond their immediate local environment . Our data provide evidence that male fertility declines with advancing age , demonstrating that aging is inevitable in aspen clones .
We collected foliage for microsatellite analysis from 871 trees of Populus tremuloides sampled from two populations in Canada: Riske Creek , British Columbia ( Nclones = 20 , Nramets = 719 ) , and Red Rock , Waterton Lakes National Park , Alberta ( Nclones = 29 , Nramets = 152 ) . Trees on the perimeter and along transects were physically mapped using both a measuring tape and a handheld Global Positioning System ( GPS ) unit . Details on the Red Rock population are not included here because this mountainous population was comprised of very small clones . The foliage from trees/ramets were sampled in two ways: on the perimeter of a patch and systematically along two or three 30–50 m transects within the patch . On average 30–50 individuals were sampled per patch . No tree less than 1 . 5 m in height was sampled , and only patches separated by at least 1 km of terrain lacking aspen trees were used . We physically mapped ramets , measured height and diameter at breast height on all ramets , and took an increment core from a sample of ramets belonging to each genotype . Estimates of clone age in years are detailed in Ally et al . ( 2008 ) [19] . In short , if neutral mutations accumulate in a clock-like manner at such loci as microsatellites , then coupling the amount of molecular diversity within a clone ( πk ) with a mutation rate ( μ ) can provide a measure of a clone's age . We examined 14 microsatellite loci for somatic mutations across 719 ramets in 20 clones . We scored an allele as a somatic mutation if an individual ramet in a clone differed by one allele at one locus but shared the same alleles at all other loci as the most frequent genotype . Somatic mutations were counted only if we were able to confirm their presence with two subsequent PCRs on the same ramets . Because we found that mutations accumulated within a clone in a manner consistent with a star-like phylogeny [19] , the probability that a mutation had accumulated at a locus in either of two ramet lineages is expected to equal 4 μTCCA . Here , TCCA represents the clone age or the time to the common cellular ancestor , the seed , and 2 μ is the mutation rate per diploid ramet per locus per year [31] . Clone age can thus be estimated from the pairwise genetic distance , πk , averaged over all pairs of ramets and all loci within the kth clone . This assumes that the ramets accumulate somatic mutations according to a star-like phylogeny , i . e . , independently . In our study , sampled ramets were well spaced from one another , with an average distance between any two ramets of 38 m ( s . e . = 3 . 31 m ) . Although somatic mutations were occasionally shared by neighboring ramets , this affected only a small number of pairwise comparisons within a clone . Furthermore , we have shown theoretically that the relationship between TCCA and πk is robust to small departures from a star-like coalescent history , allowing for the possibility that some ramets are more closely related [19] . We thus estimated the time since initiation of the clone , TCCA , as πk/ ( 4 μ ) . Rather than estimating the mutation rate directly , we obtained upper and lower bounds based on the minimum and maximum possible ages of the clones . To estimate the youngest age any clone could be , we used tree ring data , reasoning that a clone had to be at least as old as the oldest cored ramet . This provided an upper bound on the mutation rate per year , μupper , by setting the average value of πk across clones to 4μ times the average age of the oldest cored ramets ( = 71 y ) , yielding . Here , we used all clones except the most divergent clone ( which was likely much older than the oldest cored ramet ) . Given the neutral genetic diversity within the kth clone ( πk ) , this upper bound on the mutation rate was used to estimate the absolute minimum age of each clone , . Similarly , the oldest that a clone could be is 10 , 000 y old . According to the glacial history of British Columbia , this is when the ice sheets retreated from the study area [32] . We thus obtained a lower bound estimate for the mutation rate , μlower , by setting the age of the clone with the most diversity ( πmax = 0 . 0335 ) to 10 , 000 y and using to solve for μlower . Upper and lower bound estimates of the microsatellite mutation rate were thus μupper = 2 . 3×10−5 and μlower = 8 . 4×10−7 [19] . In Populus tremuloides , clones are either male or female; we chose to focus only on male clones , whose fitness components were more readily measured . In contrast , many plant evolutionary studies use monoecious plants with male and female organs on the same individual [7] , [33]–[36] . In such cases , it is possible to measure all aspects of sexual function each generation , including pollen and ovule fitness . Aspen catkins produce between 50 and 100 flowers per inflorescence , with each male flower containing approximately 7–11 anthers [37] . Recognizing the limits of field-based measures , we thus treat the average number of viable pollen grains per catkin per ramet as a proxy for the potential of each male clone to produce further sexual offspring . In the spring of 2003 , we collected 5 whole catkins from each individual ramet , sampling 4–6 ramets per clone in Riske Creek ( Nramet = 97 , Nclone = 20 ) . Every attempt was made to ensure that the catkins had flowers that were fully open and that functional anthers were in the two-lobed condition , indicative of a staminate flower just prior to the shedding of pollen [38] , [39] . To determine if , at the time of collection , the degree of flower/catkin development affected our estimates of male fertility , we noted the state of the catkin ( see Figure S2 for state descriptions ) . Attempts were made to collect replicate catkins from different parts of the ramet crown . Given time constraints and the small size of individual flowers , we did not separate out anthers and suspend them in a mixture of lactophenol-aniline blue as is typical of pollen viability studies . Instead , whole catkins were put immediately into a tube containing lactophenol-aniline blue [40] , and a pestle was used to mechanically free the pollen grains from the anthers . If during the mechanical mixing of anthers ( from a single catkin ) not all anthers were physically opened , then we are likely to have detected fewer pollen grains per catkin . This is , however , a systematic sampling error that should be present across all samples . All sample tubes were assigned a randomly generated code . These were then brought back to the lab where pollen counts and estimates of the proportion of viable pollen were assayed by two “blind observers . ” A pollen grain that was unstained , collapsed , and abnormally shaped was considered non-viable . Pollen count for each ramet was estimated from a sample using a Neubauer hemocytometer and a microscope ( 4× objective ) . Estimates of the proportion of viable pollen grains were made on a standard microscope slide ( at 40× ) , making three sweeps lengthwise along the slide and counting both viable and non-viable pollen . The average number of pollen grains counted on a slide was 1 , 756 . Thus , mean male fertility was a composite measure that included pollen viability and pollen count . We acknowledge that our composite measure only captures some components of male sexual fitness . In controlled breeding trials where dormant floral branches are collected and then forced to flower in greenhouses , it may be relatively easy to measure additional fitness components like pollen tube growth and pollen germination . This was not possible in our field-based study . Empirical studies suggest that plant sexual and asexual reproductive success is affected by the presence of fungal pathogens and insect herbivory [41]–[45] . Thus , for all sampled trees , we measured 11 morphological variables that have been shown previously to reflect disease status for P . tremuloides trees [46] , [47]: diameter at breast height ( cm ) , height ( m ) , number of conks , number of cavities , percentage of dead branches in crown , presence/absence of sap , number of scars , average length of scar ( cm ) , proportion of leaves scored as eaten , proportion of leaves with a gall , and proportion of leaves exhibiting a leaf minor . A second aspect of the environment affecting plant reproductive success is site quality , which reflects available resources like soil moisture , nutrients , drainage level , light , and soil temperature . Environmental variables like moisture vary through time , and thus accurate and detailed assessments of site quality can be time consuming , expensive , and difficult to obtain . As a proxy for site quality , plant assemblages are often used because indicator plant species reflect differential resource availability and the form of competitive interactions . Previous work on aspen-dominated communities has indeed shown that understory vegetation was significantly correlated to site quality [48] . Thus , to obtain an assay of site quality , we measured the percent cover of trees , shrubs , herbaceous plants , moss , lichens , and bryophytes in the centre of each patch using a sampling plot with a radius of 5 m . Surveys were conducted during the months of May and July 2003 . Where possible , all non-woody herbs and shrubs were identified to species level . In a few cases where habitats were similar and more than one species of a genus was found , we collapsed species into genus-level groups to reduce the number of variables in our analyses . In addition , we dug soil pits in each of the sampled patches , and from a combination of topographic and soil morphological properties , we obtained data on soil moisture regime , soil nutrient regime , and drainage class [48] . These soil characteristics were recoded into binary data for the patch . We performed two separate principal component analyses ( PCA ) because our variables reflect different physical scales as well as different aspects of ecology . Specifically , our morphological variables were measured for every sampled tree and indicate susceptibility to disease and health of the individual ramet , while site variables reflect the environment found within a clone . Eleven different ramet health variables were reduced to four composite measures , while plant understory cover and abiotic site variables were reduced to eight environmental indices using principal component analysis ( Supporting Information ) . To assess the relevance of these abiotic and biotic variables to mean sexual fitness , we first examined the data using Pearson product moment correlations ( r ) and scatterplots . No correction was made for multiple comparisons because we were simply identifying potential predictors . From this we chose only those predictors that showed a sizable correlation with sexual fitness ( r>|0 . 30| ) . The predictors that showed a correlation greater than |0 . 30| were PCD4: mechanical damage ( r = −0 . 43 ) , PCE2: moisture ( r = 0 . 43 ) , and the predictor of interest , clone age ( r = −0 . 56 ) . We calculated the Akaike Information Criterion ( AIC ) and then obtained the Akaike Weight ( W ) to determine the relative probability that a given model best fit the data ( Table 1 ) [49] . These variables were then put into a stepwise multiple regression analysis . Subsequent model selection was based on AIC criterion , p values , and adjusted R2 values .
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Aging has been demonstrated in many animals and even in bacteria , but there is little empirical work showing that clonal plants age . Evidence for aging in long-lived perennials is scarce because it typically requires survivorship or fecundity schedules from long-term demographic data . Given the extreme lifespan of many long-lived perennials , it is difficult to follow cohorts of individual clones to collect late-life survivorship or fertility . Our work offers a novel approach for obtaining late-life demographic data on a clonal species by using genetic data to estimate the age of individual clones . We studied plant clones in a natural population of trembling aspen , which grows clonally via lateral root suckers . By coupling estimates of each clone's age with a measure of its male reproductive performance , we show that long-lived plant clones do senesce . Although clonal plants have the capacity for continued growth and reproduction even late in life , mutations that reduce fertility can accumulate because selection on sexual fitness is absent during clonal growth , potentially explaining senescence in this species .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"ecology/evolutionary",
"ecology",
"evolutionary",
"biology/evolutionary",
"ecology"
] |
2010
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Aging in a Long-Lived Clonal Tree
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Transgenerational epigenetic inheritance results from incomplete erasure of parental epigenetic marks during epigenetic reprogramming at fertilization . The significance of this phenomenon , and the mechanism by which it occurs , remains obscure . Here , we show that genetic mutations in Drosophila may cause epigenetic alterations that , when inherited , influence tumor susceptibility of the offspring . We found that many of the mutations that affected tumorigenesis induced by a hyperactive JAK kinase , HopTum-l , also modified the tumor phenotype epigenetically , such that the modification persisted even in the offspring that did not inherit the modifier mutation . We analyzed mutations of the transcription repressor Krüppel ( Kr ) , which is one of the hopTum-l enhancers known to affect ftz transcription . We demonstrate that the Kr mutation causes increased DNA methylation in the ftz promoter region , and that the aberrant ftz transcription and promoter methylation are both transgenerationally heritable if HopTum-l is present in the oocyte . These results suggest that genetic mutations may alter epigenetic markings in the form of DNA methylation , which are normally erased early in the next generation , and that JAK overactivation disrupts epigenetic reprogramming and allows inheritance of epimutations that influence tumorigenesis in future generations .
Epigenetic regulation of gene expression refers to repression or activation of gene expression via covalent modifications of DNA or histones , such as methylation or acetylation , without changing the DNA sequence of the gene [1–3] . Epigenetic modifications are usually stably heritable through subsequent cell divisions , resulting in permanent changes in gene expression profiles , such as those associated with terminal differentiation . However , at critical stages in normal development or disease situations , cells undergo genome-wide epigenetic reprogramming , erasing preexisting epigenetic marks and establishing a new set of marks . For instance , major epigenetic reprogramming occurs at fertilization prior to zygotic development , at dedifferentiation that leads to cancer development , and during somatic cell nuclear transfer , a procedure used for cloning or obtaining embryonic stem cells [4–7] . However , epigenetic marks are not always completely erased from one generation to the next . For instance , genomic imprinting , where clusters of genes or whole chromosomes are preferentially inactivated depending on their parental origin [8 , 9] , can be considered an exception to epigenetic reprogramming , because in this case parental epigenetic markings are retained in the zygote . Loss of imprinting has been shown to increase the likelihood that cancer will develop [10–12] . Furthermore , human diseases , such as Prader-Willi and Angelman syndromes [13] and hereditary nonpolyposis colorectal cancer [14] , are associated with germline inheritance of epimutations . Though transgenerational epigenetic inheritance has been documented for a variety of eukaryotic organisms ranging from plants to humans [15] , the precise mechanisms that regulate epigenetic marking and erasure , as well as those that protect certain epigenetic marks from being reset , are not clear . We have previously undertaken a genetic approach in order to identify genes that are important for hopTum-l-induced tumorigenesis in Drosophila , and in the process , have found that JAK signaling globally counteracts heterochromatin formation [16] . Further analyses of the identified mutations indicated that a number of those mutations that genetically modify hopTum-l tumorigenicity also do so epigenetically . In fact , hopTum-l itself plays an essential role in the maintenance of parental origin epigenetic alterations that subsequently affect tumorigenesis in a transgenerational manner . These results indicate a novel function for the hopTum-l oncogene: it interferes with the epigenetic reprogramming process .
We previously conducted a genetic screen for modifiers of the hopTum-l hematopoietic tumorigenic phenotype and identified 37 modifier mutations [M ( Tum-l ) ] that dominantly enhanced or suppressed hopTum-l tumorigenesis in hopTum-l/+; M ( Tum-l ) /+ transheterozygotes [16] . Hematopoietic tumors in hopTum-l-containing flies were quantified by tumor index ( TI ) ( see Materials and Methods and also [16] ) . Interestingly , many of the M ( Tum-l ) mutations ( 24/37 ) exhibited paternal-effect modification of hopTum-l tumorigenicity , such that when hopTum-l/+ females were mated to males heterozygous for the modifier mutation ( M[Tum-l]/+ ) , tumorigenesis associated with hopTum-l was modified ( enhanced or suppressed ) in the F1 generation regardless of the inheritance of M ( Tum-l ) ( Table 1 ) . The transgenerational effects were confirmed with rebalanced stocks , indicating that they are unlikely to be due to different genetic backgrounds . Since little or no paternal cytoplasmic proteins are carried in the sperm , the observed paternal effects on the zygote suggest an epigenetic mechanism . Possibly , the M ( Tum-l ) mutations caused epigenetic alterations in the paternal chromosomes and these epigenetic changes were maintained through male meiosis and transmitted to the F1 generation , thereby influencing hopTum-l tumorigenicity . To understand the nature of the transgenerational epigenetic modification of hopTum-l tumorigenicity by the M ( Tum-l ) mutations , we conducted a detailed analysis of Kr , which is one of the first zygotically transcribed “gap” genes whose activity is required for the correct segmentation of the embryo [17] . First , we tested two loss-of-function alleles of Kr ( Kr1 and Kr2 ) and found that they both enhanced hopTum-l genetically and epigenetically ( Figure 1A; unpublished data ) , confirming Kr as an E ( Tum-l ) with epigenetic effects . To rule out any genetic background effects , we extensively outcrossed a Kr1 allele , and isogenized and rebalanced it over a CyO balancer chromosome that in previous testing showed no enhancement of hopTum-l ( see Materials and Methods ) . The new iso-Kr1/CyO stock again enhanced hopTum-l tumorigenicity both genetically and epigenetically , such that when hopTum-l/+ females were crossed to iso-Kr1/CyO males both hopTum-l/+; Kr1/+ and hopTum-l/+; +/CyO progeny exhibited significantly higher TI ( Figure 1B , columns 2 and 3 ) . Interestingly , when F1 males of +/Y; +/CyO , which did not inherit Kr1 , were backcrossed to hopTum-l/+ females , we found that the enhancement persisted in the F2 generation in the absence of Kr1 , but diminished in the F3 ( Figure 1B , columns 4 and 5 ) . Since half and a quarter of the P0 paternal DNA contents ( originally exposed Kr1 ) are inherited in the F2 and F3 generation , respectively , the diluting effect of the enhancement in the absence of the original mutation ( Kr1 ) is consistent with the idea that the modification is epigenetic in nature and is distributed genome wide at multiple loci . To rule out the possibility that Kr1 induced genome-wide genetic mutations , we conducted the reciprocal cross , mating iso-Kr1/CyO females with rare escaper hopTum-l/Y males . We found that Kr1 enhanced hopTum-l only genetically but not epigenetically , such that the TI increased in hopTum-l/+; Kr1/+ but not in hopTum-l/+; +/CyO female progeny flies ( Figure 1C ) . The result of the reciprocal cross confirms that the modification is epigenetic in nature , as genetic mutations ( changes in DNA sequence ) would not be reversible under normal circumstances . However , such a result could also suggest a parent-specific effect of Kr on the hopTum-l mutation . To test whether the epigenetic effects of Kr1 are specific for the male genome , we mated hopTum-l/+; Kr1/CyO recombinant females to wild-type males . In this cross , the tumor phenotype associated with hopTum-l was enhanced in both hopTum-l/+; Kr1/+ and hopTum-l/+; CyO/+ progeny flies ( Figure 1D ) , indicating that the presence of Kr1 in the female parent can also have epigenetic effects on hopTum-l tumorigenicity in the F1 generation . Thus , it appeared that Kr1 was capable of epigenetically altering both male and female genomes , and these alterations could be transmitted through both male and female meioses to the F1 . However , the inheritance and/or ability of these parental origin alterations to modify hopTum-l tumorigenicity epigenetically in the F1 progeny appeared to depend on the presence of hopTum-l as a maternal mutation . To further test the ability of maternal hopTum-l to maintain parental origin epigenetic changes , we examined the effects of histone deacetylase ( HDAC ) inhibitors on hopTum-l tumorigenicity . Since Rpd3 , encoding an HDAC , was identified as one of the genes which , when mutated , exhibited both genetic and epigenetic enhancement of hopTum-l tumorigenicity ( Table 1 ) , we reasoned that the epigenetic effect of an Rpd3 mutation on hopTum-l tumorigenicity might be mimicked by HDAC inhibitors such as tricostatin A ( TSA ) and sodium butyrate . Indeed , TSA treatment caused increased levels of acetylated histone H3 ( Figure 1E ) , and increased the tumor index of hopTum-l/+ flies from 0 . 38 to 0 . 96 ± 0 . 06 ( p < 0 . 01 ) . Consistent with a transgenerational epigenetic effect , when wild-type flies that had been treated with TSA were mated with untreated hopTum-l/+ females and the progeny were raised in the absence of the drug , the TI of hopTum-l/+ F1 progeny was also significantly increased ( Figure 1F ) . As with Kr1 , no epigenetic effect was found in the reciprocal cross ( Figure 1F ) , suggesting that the presence of hopTum-l in the early embryo is important for TSA treatment to have a transgenerational epigenetic effect on hopTum-l tumorigenicity . A similar transgenerational epigenetic effect on hopTum-l tumorigenicity was also found with another HDAC inhibitor , sodium butyrate ( unpublished data ) . To investigate the maternal hopTum-l-dependent transgenerational inheritance of epigenetic changes at the level of gene expression , we examined the effects of hopTum-l on Kr-dependent expression of the pair-rule gene fushi-tarazu ( ftz ) , which encodes a homeodomain protein required for embryonic patterning [18] . It has been shown that Kr heterozygous embryos exhibit defects in ftz expression [19] . In wild-type embryos , ftz is expressed in seven stripes at the onset of gastrulation ( Figure 2A ) . In Kr1/+ embryos , however , ftz stripe 3 is narrow or weak ( Figure 2B; also see [19] ) . The same ftz stripe 3 phenotype was found in Kr2/+ embryos ( unpublished data ) . We wondered whether the defects in ftz expression might involve epigenetic alterations , and whether these defects could be passed to the next generation in the presence of maternal hopTum-l mutation . Indeed , we found that the ftz promoter region is differentially methylated in Kr heterozygotes ( see below ) . We reasoned that if hopTum-l promotes transmission of parental origin epigenetic alterations to the next generation , then the ftz stripe 3 defect caused by Kr1 could be retained in embryos from hopTum-l/+ mothers and Kr1/+ fathers that did not inherit Kr1 . To test this , we examined ftz expression from a ftz-lacZ transgene carried on the CyO balancer chromosome , which contains the Kr+ allele and segregates from Kr1 in the F1 when Kr1/CyO ftz-lacZ flies are used as a parent . In embryos from male and female Kr1/CyO ftz-lacZ flies , 70% ( n = 61/87 ) of the β-gal+ embryos exhibited the typical Kr1 heterozygous defects , characterized by weakened or narrowed stripe 3 expression ( Figure 2B ) , suggesting that all embryos that are genotypically Kr1/CyO ftz-lacZ exhibit the stripe 3 defect . When Kr1/CyO ftz-lacZ flies were crossed to wild-type flies , in the F1 embryos , ftz-lacZ was expressed in seven stripes identical to those in the wild-type background , such that these stripes were more or less evenly spaced and similar in intensity ( Figure 2A; n = 54 ) . When hopTum-l females were mated to +/CyO ftz-lacZ males , we found wild-type ftz-lacZ pattern and no stripe 3 defects similar to those in Kr1 heterozygotes in the F1 embryos ( Figure 2C; n = 78 ) . Notably , although the JAK/STAT pathway is involved in regulating even-skipped stripe 3 expression [20 , 21] , ftz expression seemed not affected in hopTum-l mutants . This is consistent with a lack of STAT-binding sites in the ftz promoter region ( unpublished data ) . However , when Kr1/CyO ftz-lacZ males were mated to hopTum-l females , 94% of the F1 β-gal+ embryos retained the stripe 3 defect characteristic of Kr1 heterozygotes ( Figure 2D; n = 48/51 ) . Since in this mating scheme ftz-lacZ segregated from Kr1 , embryos that expressed the ftz-lacZ trangene would not inherit Kr1 and were genotypically +/+ for the Kr locus . Thus , the presence of hopTum-l caused retention of the Kr1-specific defective ftz expression pattern in embryos that did not inherit the Kr1 mutation . These results demonstrate that hopTum-l can cause transgenerational inheritance of epigenetic changes at a transcriptional level . To identify the epigenetic alterations caused by Kr mutations , we examined the DNA methylation status of the 620-bp minimal ftz enhancer in the ftz-lacZ transgene , as the expression of this ftz-lacZ is epigenetically modified by Kr1 . DNA methylation is the predominant epigenetic modification , and methylation of CpG islands is responsible for gene silencing and genomic imprinting in mammals [5–7] . There is evidence for the presence of DNA methylation in Drosophila [22 , 23] . Drosophila has a Dnmt2-like DNA methyltransferase that mediates methylation of cytosine residues in vivo [24] , although the biochemical activity of Drosophila Dnmt2 as a DNA methyltransferase is still to be shown . Methylated cytosines in both CG and CT dinucleotides have been found in many transposons and repetitive sequences in Drosophila genomic DNA [25] , and increased promoter DNA methylation is associated with gene silencing [26] . We first assessed the methylation status of the ftz minimal enhancer ( Figure 3A ) by digesting total genomic DNA with a methylation-sensitive restriction enzyme BstUI , which cuts unmethylated but not methylated CGCG sequences , followed by quantification of the undigested DNA by PCR . By comparing the time courses of BstUI digestion of genomic DNA samples isolated from Kr+/− versus wild-type control flies , we concluded that the former is more resistant to BstUI digestion ( Figure 3B and 3C , top panels ) . Digestion of the same DNA samples with a methylation-insensitive restriction enzyme HaeIII produced no differences between the two samples ( Figure 3B and 3C , bottom panels ) . These results suggest that the minimal enhancer of ftz-lacZ in Kr+/− flies is more methylated than in wild-type flies . We next investigated whether the Kr-dependent differential methylation of the ftz minimal enhancer can be passed to the next generation . We crossed Kr1/CyO ftz-lacZ flies to hopTum-l/+ and wild type females , respectively , and isolated genomic DNA from the F1 flies that inherited the ftz-lacZ transgene . We analyzed the methylation status of the 620-bp minimal ftz enhancer using methylation-sensitive and -insensitive restriction digests as described above . Indeed , we found the ftz enhancer in F1 flies of hopTum-l/+ females and Kr1/CyO ftz-lacZ males was more resistant to a methylation-sensitive restriction enzyme than the ftz enhancer in F1 flies of +/+ females and Kr1/CyO ftz-lacZ males ( Figure 3D and 3E ) , consistent with the idea that hopTum-l promotes transgenerational inheritance of epigenetic changes . We employed a second method to confirm that the promoter of the ftz-lacZ transgene has increased DNA methylation in Kr mutants and that this methylation status is transgenerationally inheritable in the presence of hopTum-l maternal mutation . We isolated total genomic DNA from embryos of different parental genotypes , digested with restriction enzymes , and incubated with antibodies against methylated cytosine . Quantification of immunoprecipitated DNA indicates that that the ftz-lacZ fragment was more methylated in embryos of Kr1/CyO ftz-lacZ flies ( Figure 3F ) and the higher levels of methylation was maintained in embryos from Kr1/CyO ftz-lacZ fathers and hopTum-l mothers ( Figure 3G ) . Finally , to further demonstrate the differential methylation of the ftz minimal enhancer in different genetic backgrounds or pedigrees , we treated the genomic DNA samples with sodium bisulfite , which converts cytosines ( C ) to thymidines ( T ) , and then cloned and sequenced independent clones for each sample . Sequencing results indicated the presence of two CG ( or CT ) -rich “islands” in the ftz minimal enhancer that are preferentially methylated in Kr+/− samples or in embryos of Kr1/CyO ftz-lacZ father and hopTum-l mothers ( Figure 4 ) . Thus , Kr mutations indeed induce epigenetic alterations , as exemplified by increased DNA methylation in the ftz minimal enhancer , and such alterations are normally erased , but are transmitted to the next generation if an overactivated JAK kinase is present in the early embryo . Since the epigenetic effects of Kr mutations involve DNA methylation , we investigated the effects of inhibiting DNA methylation on the ability of Kr mutations in promoting hopTum-l tumorigenesis . We raised flies in food containing the DNA methyltransferase inhibitor 5-aza-2′-deoxycytidine ( 5-aza-dC ) and determined the effects of drug treatment on hopTum-l–dependent blood tumor formation . When raised at 100 μM 5-aza-dC ( a nonlethal dose ) , hopTum-l/+ flies exhibited dramatically increased tumors compared with untreated hopTum-l/+ flies , with TI increased from 0 . 41 ± 0 . 05 ( untreated; n = 116 ) to 1 . 27 ± 0 . 15 ( treated; n = 68; p < 0 . 001 ) . Such results are in line with TSA treatment ( see above ) . Similar to the effects of TSA treatment , when wild-type male flies raised in 5-aza-dC were crossed to hopTum-l/+ females and allowed to produce eggs in the absence of the drug , the F1 flies exhibited increased TIs ( Figure 5 ) , but no TI increase was detected in the reciprocal cross ( unpublished data ) , suggesting a maternal hopTum-l-dependent transgenerational inheritance . Interestingly , we found that treatment with 5-aza-dC , although by itself promotes hopTum-l tumorigenesis , abolished the ability of Kr mutations to epigenetically enhance tumors , such that when Kr1/CyO male flies raised on 5-aza-dC food were crossed to hopTum-l/+ females , the epigenetic effects ( associated with CyO ) , but not the genetic effects of Kr , were abolished ( Figure 5 ) . Thus , the DNA methylation methyltransferase inhibitor 5-aza-dC both promotes hopTum-l tumorigenesis and inhibits Kr epigenetic effects . These results suggest that hopTum-l-induced blood tumors can be both enhanced by a general loss of genomic DNA methylation and suppressed by preventing Kr mutation–induced methylation in specific promoters .
Many of the M ( Tum-l ) genes that exhibited paternal-effect modifications encode products with known chromatin remodeling functions . These include HP1 , Rpd3 , and several Suppressor of variegation ( Su[var] ) mutations . It is conceivable that flies heterozygous for these mutations have altered chromatin states that could directly influence the epigenetic state of the zygote , leading to paternal effects as shown recently in mice [27] . However , the M ( Tum-l ) genes that exhibited epigenetic effects on Tum-l tumorigenicity also include those whose functions in chromatin modification are not obvious . These include transcription factors such as Kr and signaling molecules such as the Notch ligand Serrate ( Ser ) . This observation suggests that genetic mutations in genes other than those encoding chromatin remodeling proteins may also cause epigenetic alterations . Although Kr is expressed only in 20% of the early embryo , lacking Kr causes profound patterning defects , resulting in deletion or defects in over 70% of embryonic segments [28] . As a first zygotically expressed “gap” gene , Kr is in the top tier of the regulatory hierarchy that controls pattern formation of the whole organism [28] . Thus , Kr mutations can affect expression of genes that are not directly regulated by Kr . A Kr neomorphic allele ( Krif ) has been shown to affect eye development by an epigenetic mechanism [29] . Our results indicate that the Kr mutation , which likely acts early on , results in the establishment of an epigenetic signature in the genome in the form of methylation of particular promoters , such as the ftz promoter . Repression of certain “tumor suppressor genes” may explain the enhancement of the hopTum-l tumorigenic phenotype by Kr mutations . As an epigenetic modification , DNA methylation is believed to be mitotically stable . In support of this notion , we detected similar methylation patterns in the ftz-lacZ promoter in embryos and adult flies of Kr heterozygotes ( Figure 4 ) . Although we have not directly examined germ cells , the transgenerational phenomenon suggests that the Kr-dependent epigenetic signature extends to germ cells , which give rise to sperm and eggs . We envision the possibility that the epigenetic signature of germ cells is established early together with somatic cells , and can be affected by mutations in Kr , which might have a global reach in the early embryo . Alternatively , there is constant communication between germ cells and somatic cells during animal development , such that their epigenetic states will stay in “sync . ” The precise mechanisms by which germ cells acquire the epigenetic states of somatic cells remain to be investigated . When hopTum-l is inherited from the mother , its product , a hyperactive JAK kinase , is present in the embryo from the very beginning as a maternal contribution . In contrast , when inherited from the father , the hopTum-l gene product is not present in the early embryo but is expressed as a zygotic gene . Zygotic genes are not transcribed until the midblastula transition or later . The parent-of-origin effect of hopTum-l on the ability of Kr1 to modify its tumorigenicity suggests the following scenario . The M ( Tum-l ) mutations are capable of altering the state of the chromatin , resulting in epigenetic changes in the genome . These “epigenetic marks” can be maintained through mitosis and meiosis and transmitted to the F1 progeny , where they are normally erased in the zygote during early embryogenesis . However , the hopTum-l mutation , if present in the early embryo as a maternal-effect mutation , is able to preserve certain epigenetic alterations of parental origin . In other words , hopTum-l may play a role in counteracting a mechanism that erases epigenetic marks of parental origin during early embryogenesis .
All crosses were carried out at 25 °C on standard cornmeal/agar medium . All fly stocks , including hopTum-l , Kr alleles , CyO [ftz-lacZ] , and the Bloomington Deficiency Kit Stocks , are from the Bloomington Drosophila Stock Center ( http://flystocks . bio . indiana . edu/ ) . Accession numbers for mutations used in this study are list in Table S1 . Hematopoietic tumors induced by hopTum-l were scored in adult flies , which manifest as melanotic masses most frequently found in the abdomen ( see Figure 1A ) , but were also found in other parts of the body . Tumors of all sizes and locations were scored . Typically more than 200 progeny flies were scored for each cross . More than two independent crosses were scored and the results averaged . Tumorigenicity was quantified by TI , which is defined as the sum of tumor size times occurrence , and divided by the total number of flies of a particular genotype ( TI = ∑[tumor size × n]/N , where n is the number of occurrences for a particular tumor size and N is the total number of flies counted for a particular genotype ) . Tumor size 1 is defined as a tumor with a diameter equal to the width of an average abdominal segment ( see Figure 1A ) . TI 1 . 0 is equivalent to all flies of a category each having a 1 . 0 size tumor . To eliminate genetic background effects , hopTum-l and Kr1 heterozygotes were outcrossed to a y1 w1 stock for ten generations . hopTum-l was monitored by the presence of melanotic tumors in the females in each generation . To recover Kr1 from the outcrossed progeny , ten y w virgin females were selected after five generations of outcrossing and individually crossed to a y1 w1; Sco/CyO ftz-lacZ stock ( in y1 w1 background ) . Three males from the F1 of each cross were individually backcrossed to y1 w1; Sco/CyO ftz-lacZ flies ( to maintain a stock ) and the same male was testcrossed to Kr2/CyO flies . The presence of Kr1 was inferred by noncomplementation in the testcross , and a y1 w1; Kr1/CyO ftz-lacZ male was used to repeat the same outcrossing procedure one more round to establish an outcrossed y1 w1; Kr1/CyO ftz-lacZ stock . Anti-H3Ac and anti-H3 ( both from Upstate , http://www . upstate . com/ ) were used as 1:1 , 000 dilutions in Western blots , sheep anti-5-meCytidine ( Abcam , http://www . abcam . com/ ) was used for precipitating methylated DNA . For treatment with HDAC or methyltransferase inhibitors , flies were cultured in food containing TSA ( 4 . 5 μM; Sigma , http://www . sigmaaldrich . com/ ) , sodium butyrate ( 10 mM , Sigma ) , or 5-aza-dC ( 100 μM; MP Biomedicals , http://www . mpbio . com ) at 25 °C . To detect β-gal expression from the ftz-lacZ transgene , mouse anti-β-gal ( 1:1 , 000; Promega , http://www . promega . com/ ) and a biotinylated secondary antibody and the ABC Elite Kit ( Vector Laboratories , http://www . vectorlabs . com/ ) were used for whole-mount immunostaining of embryos . Signals were detected with DAB solution according to the manufacturer's recommendations . Stained embryos were dehydrated with ethanol , mounted with Euparal , and photographed with an Axiophot microscope using DIC optics . Gemonic DNA was isolated using the DNeasy Tissue kit ( Qiagen ) according to the manufacturer's instructions with minor modifications . Thirty 1–2-d-old adult flies or 100 μl of 0–12-h embryos of desired genotypes were homogenized in 180 μl of PBS and 20 μl of proteinase K ( 1 mg/ml ) per manufacturer's protocol . The samples were treated with DNase-free RNase A ( Sigma ) for 2 h at 37 °C prior to column purification . For restriction digests , 3 μg of genomic DNA was incubated with 10 units of BstUI ( New England Biolabs , http://www . neb . com/ ) or 10 units of HaeIII ( New England Biolabs ) in 60 μl of total volume at 37 °C . At different time points , an aliquot of the digests was removed and heated at 80 °C to inactivate the restriction enzyme . One microliter of each sample was used for PCR amplification with primers specific to ftz-lacZ ( forward: 5′-CCCAGGGATCGGACGTAATGTTAT-3′; reverse: 5′-GGATGTGCTGCAAGGCGATTAAGT-3′ ) . Bisulfite treatment was carried out with the EpiTect Bisulfite Kit ( Qiagen , http://www1 . qiagen . com/ ) according to the manufacturer's instructions . Genomic DNA ( 2 μg ) was treated in Bisulfite Mix . Treated genomic DNA was amplified with the following strand-specific primers ( forward: 5′-TTTAGGGATTGGATGTAATGTTAT-3′; reverse: 5′-AAATATACTACAAAACAATTAAAT-3′ ) . The PCR fragments were cloned into pGEM-T vectors ( Promega ) and independent plasmid DNA isolates were sequenced . Sequencing was carried out by Gene Gateway ( http://www . genegateway . com/ ) . For immunoprecipitation , genomic DNA was first digested to completion with EcoRI and BamHI ( New England Biolabs ) . Digested genomic DNA ( 2 μg ) in 200 μl was used for immunoprecipitation with 5 μl of anti-5-meC ( Abcam ) or control antibody at 4 °C overnight , together with protein-G beads that had been preabsorbed with sonicated single-stranded salmon sperm DNA . The antibody complex was centrifuged and washed and eluted . The presence of ftz-lacZ promoter sequence was quantified by PCR with the above primers .
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It is well known that many genetic mutations in oncogenes or tumor suppressors can cause or greatly increase a person's susceptibility to cancer . It is generally assumed that persons should feel relieved if they have not inherited the particular “cancer-causing” mutation carried by their parents . However , we found that , under certain circumstances , fruit flies carrying tumor suppressor gene mutations can pass the increased tumor risk to all offspring , even those that have not inherited the particular mutation . A likely scenario is that many genetic mutations can lead to epigenetic alterations , that is , changes in the chemical modifications of DNA or the proteins that bind to DNA in the chromosomes , and these changes can have global effects on cell function . Normally , these epigenetic alterations are wiped out and reset in the early embryo , but under certain circumstances such alterations can be inherited . Interestingly , we found evidence that a particular oncoprotein , an overactivated form of a cell-signaling molecule called JAK kinase , can counteract the epigenetic resetting program that normally operates in the early embryo . Thus , the failure of epigenetic reprogramming allows the inheritance of parental epigenetic alterations that affect susceptibility to tumors .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"drosophila",
"genetics",
"and",
"genomics"
] |
2007
|
Evidence for Transgenerational Transmission of Epigenetic Tumor Susceptibility in Drosophila
|
Leptospirosis is known to be an important cause of weather disaster-related infectious disease epidemics . In 2011 , an outbreak of leptospirosis occurred in the relatively dry district of Anuradhapura , Sri Lanka where diagnosis was resisted by local practitioners because leptospirosis was not known in the area and the clinical presentation was considered atypical . To identify the causative Leptospira associated with this outbreak , we carried out a cross-sectional study . Consecutive clinically suspected cases in this district were studied during a two-and-a-half-month period . Of 96 clinically suspected cases , 32 ( 33 . 3% ) were confirmed by qPCR , of which the etiological cause in 26 cases was identified using 16S rDNA sequencing to the species level . Median bacterial load was 4 . 1×102/mL ( inter-quartile range 3 . 1–6 . 1×102/mL ) . In contrast to a 2008 Sri Lankan leptospirosis outbreak in the districts of Kegalle , Kandy , and Matale , in which a predominance of Leptospira interrogans serovars Lai and Geyaweera was found , most cases in the 2011 outbreak were caused by Leptospira kirschneri . Seven ( 21 . 9% ) confirmed cases had acute renal failure; five ( 15 . 6% ) had myocarditis; severe thrombocytopenia ( <20 , 000/uL ) was seen in five ( 15 . 6% ) cases . This outbreak of leptospirosis in the relatively dry zone of Sri Lanka due primarily to L . kirschneri was characterized by markedly different clinical presentations and low leptospiremia . These observations and data demonstrate the public health relevance of molecular diagnostics in such settings , possibly related to the microgeographic variations of different Leptospira species , but of particular value to public health intervention in what appears to have been a regionally neglected tropical disease .
Sri Lanka is a tropical island located southeast of India . Annual rainfall varies from <1 , 500 mm in “dry” zones , where water reservoirs may dry to completeness , to 5 , 000 mm in the “wet” zones with a mean annual temperature between 26 . 5°C to 28 . 5°C . Based on Ministry of Health notification data , leptospirosis is common in nine of 24 districts in Sri Lanka with annual disease incidence rates varying from 31 to 164/100 , 000 population [1] . All leptospirosis endemic districts are within wet zones and have an annual rainfall in excess of 2 , 000 mm . In wet zone districts , paddy farming activities , high rainfall , moist soil , year-round water retention in paddy fields , the use of buffalo in agriculture and peri-domestic animal farming in rural areas provide ideal environments for the transmission of leptospirosis . In 2011 , a large outbreak of leptospirosis was observed in Anuradhapura district of Sri Lanka , which was not previously classified as an endemic area for leptospirosis . Anuradhapura is located in North Central province of Sri Lanka , in the dry zone of the country . The annual rainfall is 1 , 200–1600 mm and the mean annual temperature is ∼30°C . In the Anuradhapura area , paddy farming is carried out by traditional , full-time farmers and provides the main mode of income . Paddy fields are often much larger than leptospirosis-endemic areas . There are no wetlands or marshy lands in these areas , except the paddy fields during working seasons . Paddy field work depends on irrigation systems , so that between farming seasons , paddy fields become completely dry ( Figure 1 ) . The soil structure , water quality and ecological systems in Anuradhapura contrast with those of the wet zones of Sri Lanka where leptospirosis is endemic . In January , 2011 , there was a 7-fold increase in rainfall compared to the previous year , with flooding in Anuradhapura district affecting nearly 300 , 000 people [2] . In 2011 , after two weeks of massive flooding in Anuradhapura , physicians from Anuradhapura reported an increase in the number of febrile patents with liver and renal complications . Based on clinical presentation alone this cluster of suspected cases was considered to be an outbreak of leptospirosis [3] . There were 18 suspected leptospirosis cases reported from the Teaching Hospital Anuradhapura ( THA ) in January rising to 82 clinically suspected cases in February-more than the total number of suspected cases from the whole district during the eight year period , 2000–2007 ( Figure 2 ) . This represents the first large reported outbreak of leptospirosis in a non-endemic area in Sri Lanka . Because the clinical presentation of these suspected cases was different from that reported previously , some clinicians challenged the diagnosis of leptospirosis-especially since only a few cases could be diagnosed serologically , i . e . by microscopic agglutination test ( MAT ) . However the performance of the gold standard serological test for leptospirosis-the MAT-was limited because only genus-specific antigen ( Leptospira biflexa serovar Patoc ) was used for detection of anti-leptospiral antibodies . In the present study we use a recently published qPCR assay [4] to attempt to confirm that this outbreak of febrile illness with renal and liver complications was indeed due to leptospirosis , and if so , to quantify the bacterial load and determine whether this flood-associated outbreak was caused by the same strains implicated in the island-wide outbreak of 2008 [5] .
We carried out a prospective hospital based study in THA from the 21st of February to the 12th of May 2011 . The heavy rains continued until the first week of February resulting in most of the paddy fields in the area become partially submerged even during the harvesting period which extended to April . The appearance of new leptospirosis cases declined after the first week of May ( around 3–4 weeks after the end of the harvesting period ) . THA is the only tertiary care institute in the district of Anuradhapura , Sri Lanka . It has four general medicine wards with an average of 1500 admissions per month . The study population included all febrile patients >13 years of age with probable leptospirosis admitted to medical wards of THA . Probable cases of leptospirosis were identified using clinical criteria as outlined by the epidemiology unit of Sri Lanka ( Table 1 ) [6] . Based on previous observations [5] , this definition was relaxed to include “any patient suspected as having leptospirosis” by a treating physician . The protocol and case definitions for patient recruitment , sample collection and molecular methods were designed to be consistent with the protocol used during previous outbreak investigation in 2008 [5] . All suspected patients admitted to THA during the study period were screened using a clinical data checklist on admission to the ward and throughout the hospital stay . Socio-demographic data and exposure history were obtained using an interviewer-administered questionnaire . A data extraction sheet was used to collect laboratory and other investigation data from patient records . In this study , we used a published qPCR assay validated previously using Sri Lankan samples [4] . A total of 5 mL of venous blood was obtained from suspected cases following standard procedures . Samples were sent to the Faculty of Medicine , Rajarata University of Sri Lanka within two hours of collection . Serum was separated; serum and whole blood samples were stored at −20°C until analyzed [7] . For use in preparing standard curves ( with spiked bacteria ) and negative controls , venous blood was collected from a healthy individual . Exponential-phase L . interrogans serovar Copenhageni strain L1-130 [8] cultured in liquid EMJH media was inactivated in 10% formalin for 15 minutes then counted in a Petroff-Hausser counting chamber ( Hausser Scientific ) . Known numbers of live L . interrogans L1-130 were then spiked into whole blood or serum and diluted to give final concentrations of 100 to 108 Leptospira/mL . DNA was extracted from spiked samples as described below . For Leptospira-negative controls , unspiked whole blood or serum from the same healthy individual was extracted as described . PCR reaction mixes were prepared using the iQ Supermix ( Biorad ) with final primer and probe concentrations of 0 . 5 µM and 0 . 2 µM , respectively , and 5 µL DNA ( samples/standard curves and Leptospira-negative controls ) or no-template control in a total reaction volume of 20 µL . Samples were amplified using a previously published qPCR assay [8] . All reactions were performed in triplicate . Fluorescence was measured at the end of the each cycle . The cycle threshold was set to two standard deviations above the mean fluorescence value for the first three cycles . A positive PCR was defined when all three replicates had a fluorescence signal above threshold . Reactions with one or two positive replicates were repeated and confirmed ( or labeled as not-detected ) . All positives had a quantitative signal within the linear part of the standard curve greater than the cycle threshold . All qPCR positive samples were amplified using a previously published nested PCR protocol [5] . This single-tube nested PCR was used to amplify a region of the 16S ribosomal DNA gene specific for pathogenic and intermediate Leptospira spp . The PCR primers were rrs- outer-F ( 5-CTCAGAACTAACGCTGGCGGCGCG-3′ ) , rrs- outer-R ( 5′-GGTTCGTTACTGAGGGTTAAAACCCCC-3′ ) , rrs-inner-F ( 5′-CTGGCGGCGCGTCTTA-3′ ) , and rrs-inner-R ( 5′-GTTTTCACACCTGACTTACA-3′ ) . PCR products were purified using the USB ExoSAP-IT PCR product cleanup kit ( Affymetrics ) according to manufacturer's instructions . At least two forward and two reverse sequencing reads were obtained for all samples; sequences were assembled and aligned using Geneious software then the alignments trimmed to yield a 443 bp region for phylogenetic analysis [positions 89–531 of the rrs gene of L . interrogans serovar Lai strain 56601 ( NC_004342 ) for phylogenetic analysis . For species identification , maximum likelihood trees with support from bootstrap 500 replicates were created in MEGA5 . Data were analyzed using SPSS version 17 . All categorical data were presented as proportions . Statistical tests for categorical data were done using chi-square test . This study conformed to the Helsinki Declaration and to local legislation . All participants gave written informed consent to participate in this study . Ethical clearance was obtained from the Research and Ethics Committee , Faulty of Medicine and Allied Sciences , Rajarata University of Sri Lanka .
From 21st of February to 12th of May 2011 , 96 probable leptospirosis cases were enrolled ( Figure 3 ) . Of these probable cases , 61 ( 63 . 5% ) were adult males . The mean age of the study sample was 40 years ( Standard deviation ( SD ) +/−12 years ) . None of these patients were admitted directly to the medical wards of THA . Before coming to THA , all 96 patients sought health care from either private ( 33 , 34 . 3% ) or from other government facilities . These 96 cases were from 14 administrative divisions ( of the total of 20 ) showing a large scale rather than a focal outbreak . The median duration of fever on admission was four days ( interquartile range; IQR 3–7 ) . Paddy farmers accounted for 63 . 5% of the study sample . Of the 96 clinically suspected cases of leptospirosis , 32 ( 33% ) were confirmed using qPCR . Bacterial load in serum/blood ranged from 102 to 104 Leptospira/mL among 32 positive cases ( Figure 4 ) . Median bacterial load was 4 . 1×102 Leptospira/mL ( inter-quartile range 3 . 1–6 . 1×102/mL ) . The 16S rRNA gene could be amplified and sequenced from 26 ( 81 . 3% ) of these qPCR-positive samples . Based on phylogenetic analysis of the 16S rRNA gene , L . kirschneri was the most common cause of disease among outbreak cases ( n = 20 ) , with strains belonging to L . borgpetersenii and L . interrogans also identified ( Figure 5 ) . All 32 confirmed cases reported exposure to either a single ( 14 , 43 . 8% ) or multiple sources ( 18 , 46 . 2% ) of natural/man-made water catchments during the three-week period prior to the onset of disease ( Table 2 ) . Direct exposure to floodwater was reported by seven cases . 27 ( 84 . 4% ) were engaged in paddy farming activities and all 27 reported farming as the main income source for their families . Regular exposure in paddy fields ( >2 days per week ) during this period was reported by 22 ( 67 . 8% ) confirmed cases . Though frequent exposure in paddy farms was slightly more common in confirmed cases , the difference was not statistically significant . The presence of skin wounds was observed among 11 ( 34 . 4% ) confirmed cases . However , 23 ( 71 . 9% ) of the confirmed cases had skin breaches in the form of cracks around the heel , which was equally common among suspected ( unconfirmed ) patients . The median duration of fever among confirmed cases was 6 days ( IQR 2–8 days ) compared to 7 days ( IQR 2–10 days ) among probable cases . This difference was not statistically significant . Median duration of hospital stay among confirmed cases was 5 days ( IQR 3–6 days ) . All patients had fever and headache . Of the symptoms/signs included in the proposed WHO surveillance case definition , oliguria was the most common symptom ( Table 3 ) . Of the 32 confirmed cases , seven ( 21 . 9% ) had acute renal failure as confirmed by serum creatinine >1 . 5 mmol/L; two had ultrasound evidence of acute renal parenchymal disease . In addition , 17 ( 53 . 1% ) cases had elevated blood urea ( >40 mg/dL ) . Elevated liver enzymes ( SGPT/SGOT ) were observed in 15 ( 46 . 9% ) and serum bilirubin >2 was recorded for 6 cases . Of five ( 15 . 6% ) patients with myocarditis , four had ECG changes and two were confirmed by echocardiogram; three of them required inotropic drugs and two required positive pressure ventilation . There were no fatalities . Leukocytosis of more than 11 , 000/mL was observed in nine ( 28 . 1% ) cases , and in six ( 18 . 8% ) , leukopenia of less than 4000/mL was observed . All confirmed cases had a normal neutrophil count . Thrombocytopenia ( <150 , 000/µL ) was observed in 25 ( 78 . 1% ) patients during the course of illness; in five ( 15 . 6% ) , platelet count was less than 20 , 000/µL .
In this study we demonstrate important principles of a combined public health and clinical approach to the identification and management of a difficult-to-diagnose neglected tropical disease in a resource-poor setting . A large outbreak of leptospirosis occurred in 2010 in the district of Anuradhapura , Sri Lanka , a notably dry zone of Sri Lanka where an unusually rain-associated flooding preceded the outbreak . The context of this outbreak , different from the vast experience of leptospirosis in Sri Lanka that occurs in wet zones associated with paddy farming , impeded clinical diagnosis and public health intervention . In the two and half months period extending from third week of February 2011 , because of our previous experience and molecular diagnostic infrastructure development , we confirmed 32 of 96 ( 33 . 3% ) clinically suspected cases using a previously published qPCR assay , which demonstrated that bacteria load in patients ranged from 102 to 104 Leptospira/mL . This outbreak , the first reported in this so-called dry zone in Sri Lanka , was characterized by atypical clinical and biological features of leptospirosis . In contrast to previous outbreak reports , we found that most cases in the 2011 outbreak in Anuradhapura were caused by strains belonging to L . kirschneri , not previously known to be a common cause of human leptospirosis in Sri Lanka . Acute renal failure and myocarditis were common in the study population and the proportion of patients with complications was higher than the previous outbreak ( Table 4 ) . Renal failure and myocarditis were confirmed among 21 . 9% and 15 . 6% cases compared to 14 . 8% and 7 . 1% in 2008 outbreak . In addition , 53 . 1% of the patient had high blood urea nitrogen showing higher rate of acute kidney injury . In our previous studies using nearly identical study protocols including the laboratory tests to confirm leptospirosis [5] , the observations regarding infecting Leptospira species and serovar were different from the present study . Previous outbreak reports from Kandy , Matale and Kegalle show L . interrogans to be responsible for the vast majority of human leptospirosis in Sri Lanka [4] , [5] , [9] . Although , L . interrogans has historically been the pre-eminent cause of human leptospirosis in Sri Lanka [10] , [11] , [12] , isolates of L . borgpetersenii serovar Ceylonica were reported in 1964 [13] and L . kirschneri serovar Ratnapura was isolated in 1966 from a patient and subsequently from buffalo and cattle [11] . L . kirschneri has not been a reported cause of human leptospirosis in Sri Lanka since these original reports in the 1960s . However , because of its location in the dry zone , no recent studies have included the district of Anuradhapura . No studies are also available from this region to show the presence of leptospirosis disease or Leptospira species/serovars affecting livestock or other animals in the region . Severe complications were more common in the current study than reported previously . During the 2008 outbreak investigation thrombocytopenia and leukopenia were uncommon . Acute renal failure was observed among 14 . 8% of the confirmed cases and myocarditis among 8 . 1% . In the present study , ARF was high and also more than 50% of the confirmed cases had renal function impairment . Liver failure was not observed , but more than 50% of confirmed cases had elevated liver enzymes . In the previous report range of bacterial load was up to 106/mL that was much higher than the present study . Whether these differences are due to strain or host factors is unknown . But , is noteworthy that while L . interrogans was the confirmed cause of 27/29 infections in the 2008 outbreak , the majority of cases reported here were caused by L . kirschneri . However , in the 2008 outbreak report , inclusion criteria were slightly less stringent resulting in the inclusion of a higher proportion of mild to moderate cases [5] . Thus , the rates of severe complications reported here might be partially due to the particular selection bias . One another concern about these marked variation in Leptospira strain and the clinical disease is whether this is due to microgeographical variations . It has been shown for other disease like malaria [14] , [15] and schistosomiasis [16] , [17] , [18] that microgeography may have a major influence of disease epidemiology . Geochemistry is well described as a major contributory factor in human health [19] . The previous reports are from the central , hilly , wet zones of the country , which is only around 100 km away from the present study site . However , the microgeography of these two areas are different in relation to elevation , rainfall , temperature , soil structure , crop and the ecology . Despite the extensive literature available on leptospirosis , studies on the microgeographic variation of Leptospira is scarce . We recommend further studies on this aspect based on our preliminary findings , which would be useful in disease control and prevention strategies . This study shows that the unusual clinical features observed during the 2011 leptospirosis outbreak in Sri Lanka could be due to uncommon L . kirschneri strains that arose in the context of dry rather than wet season epidemiology and perhaps due to changing human-animal interactions or introduction of novel Leptospira to the region . Whether these differences are due microgeographical variations of Leptospira strains or due to changing populations of reservoir animals necessitates further investigation .
|
Leptospirosis outbreaks occur predictably in Sri Lanka after seasonal rains and flooding in the endemic wet zone . Molecular investigations with quantification of a post-flood leptospirosis outbreak in the non-endemic dry zone of Sri Lanka in 2011 suggest variation of biological , clinical , and molecular characteristics compared to previous reported leptospirosis outbreaks in the endemic areas , probably showing a micro-geographic variation of leptospirosis . This work demonstrates the direct clinical and public health relevance of modern molecular diagnostic technologies to identifying an endemic neglected tropical disease where previously not suspected , especially in the resource-poor setting .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"bacterial",
"diseases",
"infectious",
"diseases",
"molecular",
"epidemiology",
"infectious",
"disease",
"epidemiology",
"environmental",
"epidemiology",
"epidemiology",
"neglected",
"tropical",
"diseases",
"leptospirosis"
] |
2014
|
Regional Differences of Leptospirosis in Sri Lanka: Observations from a Flood-Associated Outbreak in 2011
|
Infection by Leptospira interrogans has been causally associated with human and equine uveitis . Studies in our laboratories have demonstrated that leptospiral lipoprotein LruA and LruB are expressed in the eyes of uveitic horses , and that antibodies directed against LruA and LruB react with equine lenticular and retinal extracts , respectively . These reactivities were investigated further by performing immunofluorescent assays on lenticular and retinal tissue sections . Incubation of lens tissue sections with LruA-antiserum and retinal sections with LruB-antiserum resulted in positive fluorescence . By employing two-dimensional gel analyses followed by immunoblotting and mass spectrometry , lens proteins cross-reacting with LruA antiserum were identified to be α-crystallin B and vimentin . Similarly , mass spectrometric analyses identified β-crystallin B2 as the retinal protein cross-reacting with LruB-antiserum . Purified recombinant human α-crystallin B and vimentin were recognized by LruA-directed antiserum , but not by control pre-immune serum . Recombinant β-crystallin B2 was likewise recognized by LruB-directed antiserum , but not by pre-immune serum . Moreover , uveitic eye fluids contained significantly higher levels of antiibodies that recognized α-crystallin B , β-crystallin B2 and vimentin than did normal eye fluids . Our results indicate that LruA and LruB share immuno-relevant epitopes with eye proteins , suggesting that cross-reactive antibody interactions with eye antigens may contribute to immunopathogenesis of Leptospira-associated recurrent uveitis .
Infectious disease caused by spirochetes of the genus Leptospira is a veterinary and public health problem of global proportions [1] , [2] . Humans and other mammals are exposed to the organism when they contact groundwater contaminated with urine from carrier animals . The disease in humans varies from a mild flu-like form to a more severe syndrome involving multiorgan failure and death [3] . Uveitis is a common complication of systemic infection in humans affecting one or both eyes [4] . In equines , infection is mainly associated with spontaneous abortion in mares and recurrent uveitis [3] . After an initial infection , some horses develop a recurrent inflammation of the uveal tract of eye ( iris , ciliary body and choroid ) , known as equine recurrent uveitis ( ERU ) or ‘moon blindness’ . First described in 1819 by James Wardrop as a “specific inflammation” of uveal origin , it is the most common cause of blindness in horses worldwide [5] , [6] with a prevalence of approximately 8–10% in the United States [7] . Onset of the disease is usually acute with variable degrees of severity and duration . The acute phase is followed by a quiescent phase of no or low inflammation [8] . Subsequent recurrence of inflammation results in pronounced lesions with guarded prognosis for preservation of visual acuity [8] , [9] , [10] , [11] . The Appaloosa breed and horses with MHC class I haplotype ELA-A9 have been observed to be at increased risk of developing uveitis [12] , [13] . Leptospira interrogans serovar Pomona is the most common and well-documented infectious cause of ERU in the United States [14] . Its association with pathogenic leptospires has been well established by presence of high titers of leptospiral agglutinins in the blood and aqueous humor [15] , [16] , by isolation of Leptospira from ocular fluids [17] , [18] and the detection of leptospiral DNA by polymerase chain reaction in vitreous humor of uveitic horses [17] . Initial evidence of the association was provided by Morter et al . [19] when they induced uveitis in ponies by subcutaneous injection of guinea pig blood containing live L . interrogans serovar Pomona . The resulting ocular pathology in experimental ponies was found to be similar to that of spontaneous cases of Leptospira-associated ERU . By using ERU uveitic fluids to screen a lambda phage library of L . interrogans , we identified leptospiral lipoproteins , LruA and LruB , associated with recurrent uveitis in horses [20] . Uveitic equine eye fluids contained significantly higher levels of immunoglobulin A ( IgA ) and IgG specific for LruA and LruB than did companion sera , indicating strong local antibody responses . Moreover , monospecific antiserum to LruA and LruB reacted with extracts of equine ocular tissue . In the present study we have examined the reactivity of LruA- and LruB-antiserum with sections of lens and retinal tissue and identified the ocular proteins involved in the interaction . In addition , the significance of the identified autoantigens was assessed by measuring their immuno-reactivities in eye fluids of uveitic and healthy animals .
All animals were handled in strict accordance with relevant national and international guidelines , and all animal work was approved by the University of Kentucky Institutional Animal Care and Use Committee ( IACUC#2009-0477 ) . Eye fluids and companion sera from horses of varied age , breed , and origin were obtained from a commercial horse slaughter plant in North America . Eyes with gross evidence of uveitis were enucleated after slaughter , and aqueous humor was removed with a 10-ml syringe and stored at −20°C . The eyes were placed in 10% formaldehyde for subsequent embedding , sectioning , and staining with hematoxylin and eosin for histological examination . Eye fluids and sera were assayed for antibodies to serovars Pomona , Canicola , Icterohemorrhagiae , Hardjo , Bratislava , and Grippotyphosa in the microscopic agglutination test ( MAT ) [20] . Eye fluids and sera from each horse were also tested by ELISA using recombinant antigens LigA , Lk73 . 5 and Qlp42 ) . Extracts were prepared from the ciliary body , cornea , lens , and retina of a normal eye from a young horse serologically negative for Leptospira , as described by Parma et al . , 1985 [21] . Identification , cloning , and expression of recombinant LruA and LruB has been described previously [20] . Briefly , following PCR amplification of chromosomal DNA of L . interrogans serovar Pomona type kennewicki ( JEN4 ) with gene-specific primers , amplicons were inserted into pET-15b ( Novagen , Madison , WI ) . Recombinant plasmids were transformed into Escherichia coli BL21 ( DE3 ) ( Novagen , Madison , WI ) , and recombinant His-tagged proteins were isolated and their purity tested as previously described [20] . Three New Zealand white rabbits were immunized to obtain polyclonal antiserum directed against recombinant LruA . Lenses were dissected from the eyes of three healthy horses immediately after euthanasia , frozen in liquid nitrogen , stored at −70°C , and later embedded in tissue freezing medium for mounting in a Tissue-Tek ( Miles , Elkhart , IN ) cryostat . Sections ( 8–10 µ ) were placed on glass slides treated with 2% solution of 3-aminopropyltriethoxysilane in acetone . The sections were fixed in acetone at 20°C for 20 min followed by two washes with phosphate buffer saline ( PBS , pH 7 . 4 ) for 5 min each . Blocking was performed using 2% bovine serum albumin ( BSA; Sigma , St . Louis , MO ) in PBS for 30 min . Sections were again washed thrice with PBS and incubated with 1∶100 polyclonal rabbit antiserum or pre-immune serum overnight at 4°C in a humidifying chamber . Sections were washed three times for 5 min each and subsequently incubated with 1∶250 dilution of FITC conjugated goat anti-rabbit IgG ( Invitrogen , Carlsbad , CA ) for 1 h at room temperature in a humidifying chamber . Slides were mounted in a mounting medium containing anti-fading reagent Mowiol ( EMD Chemicals , Gibbstown , NJ ) and screened by epifluorescence microscopy ( Axioscope-20; Zeiss , Thornwood , NY , USA ) and image analysis was carried out using the QUIPS-XL and QUIPS-AKS system ( Vysis , Downer's Grove , IL , USA ) . The same IFA protocol as above was used for testing lens tissues obtained from two healthy sheep . Lenticular and retinal aqueous extracts were separated by two-dimensional polyacrylamide gel electrophoresis ( 2-D PAGE ) using MultiPhor-II system ( GE Healthcare , Piscataway , NJ ) . Briefly , the aqueous extracts were subjected to isoelectric focusing using precast IPG strips ( Bio-Rad , Hercules , CA ) for 3000 V· h ( 500 V , 6 h , 10°C ) . Strips were then equilibrated and subjected to conventional sodium dodecylsulfate-12 . 5% polyacrylamide gel electrophoresis ( SDS-PAGE ) . Gels were either stained with SYPRO Ruby ( Invitrogen ) or transferred to nitrocellulose membranes for immunoblot analysis with LruA- or LruB-directed antiserum . Immunoblot positive protein spots were extracted from gels and analyzed by matrix-assisted laser desorption ionization-time-of-flight ( MALDI-TOF ) mass spectrometry ( University of Louisville Mass Spectrometry Core Laboratory , Louisville , KY ) . Spectrometry outputs were compared with known sequences using Mascot ( Matrix Science , Boston , MA ) . Recombinant human α-crystallin B ( Abcam , Cambridge , MA ) , purified vimentin from bovine lens ( Sigma ) or human recombinant β-crystallin B2 ( Abnova ) were separated by SDS-PAGE , transferred to a nitrocellulose membrane and blocked with 5% nonfat dry milk in Tris-buffered saline ( 20 mM Tris , 150 mM NaCl , 0 . 05% Tween 20 , pH 7 . 5 ) . Membranes were incubated with LruA or LruB-antiserum ( 1∶400 ) followed by incubation with protein G conjugated to horseradish peroxidase ( Zymed , San Francisco , CA ) . Membranes were developed with the SuperSignal West Pico enhanced chemiluminescence substrate ( Pierce ) , and bands were visualized with BioMax Light film ( Kodak ) . ELISA measured alpha-crystallin B , vimentin and β-crystallin B2 antibody levels in leptospiral uveitic and normal eye fluids as described previously [20] , [22] . Briefly , ELISAs were performed in Maxisorp 96-well plate wells ( Nalge-Nunc , Rochester , NY ) coated with 200 ng human recombinant alpha-crystallin B ( Abcam ) , purified vimentin from bovine lens ( Sigma ) or human recombinant β-crystallin B2 ( Abnova ) followed by blocking with 5% nonfat dry milk . Uveitic and normal eye fluids ( 1∶100 ) were added and incubated for 1 h at 37°C . Bound antibodies were detected using HRP conjugated Protein G ( 1∶4000; Zymed , San Francisco , CA ) . Plates were developed using ready-to-use 3 , 3′ , 5 , 5′-tetramethyl benzidine substrate solution ( 1-Step Turbo TMB-ELISA , Thermo Scientific , Rockford , IL ) . Reactions were stopped by addition of 2N H2SO4 , 50 µl/well . Absorbance was read at 450 nm in a Spectramax plate reader using SoftMax Pro ( Molecular Devices , Sunnyvale , CA ) . Statistical analyses were performed using Student's t-test assuming unequal variances .
In a previous study [20] LruA-antiserum was shown to recognize a ∼22 kDa protein in lens extract and a ∼65 kDa protein in ciliary body extract . In the same work , LruB-antiserum reacted with a ∼30 kDa band in retinal extract . To further examine the observed cross-reactivity between equine ocular tissue and LruA and LruB specific antisera , immunofluorescent assays were performed . Frozen lenticular and retinal tissue sections ( 8–10 µ ) were fixed , blocked and incubated with antiserum or preserum to LruA and LruB ( diluted 1∶100 ) . The lens fibers showed uniform homogenous pattern of fluorescence when incubated with LruA-specific antiserum but not with normal rabbit serum ( Figure 1A and B ) . Similarly , fluorescence was observed when equine retinal tissue sections were incubated with LruB-specific antiserum but not with normal rabbit serum ( Figure 1C and D ) . The positive fluorescence seen in retinal tissue incubated with LruB-antiserum was restricted to one or more deeper retinal layers , which include the inner limiting membrane , layer of nerve fiber and may be the ganglion cell layer , in contrast to a diffused positive fluorescence seen in lens tissue sections incubated with LruA-antiserum . In addition , sclera and choroid were devoid of any fluorescence in the same tissue section ( Figure 1 ) . Similar results were obtained with lenticular tissues from a healthy sheep . Sections of sheep lens incubated with LruA-antiserum showed positive fluorescence but not when these sections were incubated with pre-immune serum ( not shown ) . The reactivities of equine and sheep lenticular tissue sections with LruA-antiserum indicated that the observed interaction is not unique to equines lens . To identify the eye protein ( s ) recognized by LruA-directed antibodies , proteins in equine lenticular extract were separated on two-dimensional polyacrylamide gels , transferred to nitrocellulose membranes and probed with LruA-specific antiserum ( Figure 2 A and B ) . LruA-antiserum recognized protein spots with apparent molecular masses of approximately 20 and 60 kDa . The immunoblot was aligned with the stained gel , to locate the corresponding protein spots which were then subjected to mass spectrometric analysis . The 20 and 60 kDa protein spots were identified as α-crystallin B and vimentin , respectively ( Table 1 ) with 66% and 44% coverage ( not shown ) . Alpha-crystallin B and vimentin have molecular masses of 20188 Daltons and 53727 Daltons , respectively . Attempts to identify ciliary body protein ( s ) reactive to LruA-specific antiserum by this method have not yet been successful . Similarly , LruB-antiserum recognized three spots of retinal proteins ( Figure 2C and D ) , which were identified by mass spectrometry to be β-crystallin B2 ( Table 1 ) . Mammalian α-crystallin B protein sequences are highly conserved across species ( Figure 3 ) . Therefore , purified recombinant human α-crystallin B was used in immunoblot analyses to confirm α-crystallin B as a cross-reacting antigen . LruA-directed antiserum , but not the pre-immune serum , reacted with recombinant α-crystallin B , indicating that this lenticular protein is indeed the cross-reacting antigen ( Figure 4A and B ) . The amino acid sequence identity between equine and bovine vimentin is 91% ( not shown ) . So , purified vimentin from bovine lens was used in immunoblot to examine its reactivity to LruA-directed antiserum . LruA-directed antiserum but not the pre-immune serum , reacted with purified vimentin in an immunoblot ( Figure 4C and D ) . Recombinant human β-crystallin B2 was used in immunoblot analyses to confirm β-crystallin B2 as a cross-reacting antigen . LruB-directed antiserum , reacted with recombinant β-crystallin B2 , indicating that this lenticular protein is indeed the cross-reacting antigen ( Figure 5A ) . Pre-immunization serum did not react with β-crystallin B2 ( Figure 5B ) . The biological significance of α-crystallin B , vimentin and β-crystallin B2 as a cross-reacting antigen was investigated by examining antibody levels against these lenticular or retinal proteins in eye fluids obtained from clinical cases of leptospiral uveitis and healthy controls . ELISA was performed using recombinant α-crystallin B , purified vimentin or recombinant β-crystallin B2 as coating antigens . Antibody levels to α-crystallin B , vimentin and β-crystallin B2 ( Figure 6 ) were found to be significantly elevated in uveitic compared to normal eye fluids ( p<0 . 001 ) .
The pathogenesis of leptospiral uveitis is currently under investigation and several possible mechanisms have been proposed [6] , [9] , [12] , [17] , [20] , [21] , [23] , [24] , [25] , [26] , [27] , [28] . How leptospires survive in the eye , causing breach of the ocular immune privilege and initiation of pro-inflammatory changes , is not understood . Although direct Leptospira-mediated injury to eye structures is possible , a growing body of evidence suggests that autoimmune responses to ocular tissue components play a significant role in pathogenesis [6] , [9] , [12] , [17] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] . Parma et al . [21] demonstrated reactivity of anti-equine cornea antibodies with Leptospira and binding of Leptospira and cornea specific antibodies to equine cornea [21] . Subsequently , an antigenic relationship between equine lens and leptospires was proposed by the same group [27] . Electron microscopic studies revealed that the antigenic protein of L . interrogans that shares epitopes with equine cornea and lens is not exposed on the outer surface of leptospires [28] . However , in those studies , specific leptospiral and/or ocular proteins involved in the antigenic relationship were not identified . In this study , we have shown that the lenticular proteins , α-crystallin B and vimentin , cross-react with LruA and retinal protein , β-crystallin B2 , cross-reacts with LruB confirming our previous observations of reactivity of LruA and LruB antibodies with equine lens and retina , respectively . Alpha-crystallin B and vimentin are critical for maintaining lens clarity and thus visual acuity [29] . Alpha-crystallin is the principal constituent of the lens and acts as a molecular chaperone that keeps other lens proteins from precipitating [30] . Disruption of this function may lead to impairment of light refraction and potentially vision . Alpha-crystallin B is a 175-amino acid small heat shock protein and shares high interspecies sequence homology ( Figure 3 ) . Its involvement in several disease states including uveitis , Alexander disease , Alzheimer's , Creutzfeldt-Jacob disease and multiple sclerosis are under investigation [31] , [32] , [33] . In addition to lens and central nervous system ( CNS ) , it is also present in many other tissues including skeletal muscles and kidney epithelial cells . Vimentin is an important structural determinant in the human lens cell and is mainly expressed in the epithelium of the lens . In a previous study , high expression of vimentin was negatively correlated with the normal differentiation of the lens fibers . In that study , animals developed pronounced cataract and extensive lens degeneration as a result of impairment of lens fiber cell differentiation [34] . A study on expression of vimentin in lens epithelium of age-related cataract suggested that damage to the lens epithelial cells might initiate a decrease in vimentin expression leading to degradation of the lens cytoskeleton [35] . Recently , small interfering RNA ( siRNA ) mediated downregulation of human pigment epithelium-derived factor ( PEDF ) expression in primary human lens epithelial cells was shown to result in a decrease in the expression of vimentin and increase of α-crystallin B expression [29] . Interestingly , serum and ocular levels of PEDF have been shown to decrease in uveitic horses , but not the normal horses [36] , [37] . Beta-crystallin B2 is present in lens and non-lenticular tissues , including the retina . The appearance and accumulation of beta-crystallin B2 in neural retina coincides with its functional maturation [38] . Recently , antibodies against α-crystallin A , α-crystallin B and β crystallin B1 were found to be significantly elevated in uveitis patients and seroreactivity was found to be significantly associated with cortical cataract [39] . In another study , Çelet and colleagues [31] reported an elevated humoral response to α-crystallin B in neuro-Behçet's disease and Guillain-Barré syndrome . We recently demonstrated that LruA and LruB were recognized by antibodies from Behçet's and Fuchs uveitis patients , without any evidence of those patients having been exposed to Leptospira [22] . Both of these diseases are believed to be autoimmune diseases [22] , [40] , [41] , [42] , [43] , [44] , [45] . In the same study , we also observed an association in humans between high levels of antibodies recognizing LruA and LruB and the presence of cataract [22] . The high levels of antibodies cross-reactive with LruA and LruB in patients with Fuchs or Behçet's uveitis , and the strong association of LruA and LruB antibodies with cataract could be due to increased levels of antibodies to the common autoantigens , α-crystallin B , vimentin and β-crystallin B2 , in those diseases . Also , elevated levels of LruA- and LruB-antibodies in sera of human patients with leptospiral uveitis [22] and reactivity of LruA- and LruB-antiserum with human alpha-crystallin B and β-crystallin B2 suggest a similar phenomenon in human leptospiral uveitis . We are presently pursuing those hypotheses to determine the causes of leptospiral and non-leptospiral uveitis . A linear amino acid similarity or a conformational homology between microbial and host proteins is a potential basis for molecular mimicry . The limited linear amino acid similarity between these leptospiral proteins and their respective cross-reacting ocular proteins ( not shown ) suggests similarities at the conformational level . Studies to identify the cross-reactive epitopes are underway . In conclusion , we have identified two lens proteins and a retinal protein that react with antiserum directed against LruA and LruB , leptospiral proteins expressed in uveitic eyes . The presence of antibodies recognizing α-crystallin B , vimentin and β-crystallin B2 in uveitic , but not normal eye fluids , strongly suggests a role for these antibodies in Leptospira-associated recurrent uveitis . In the immune privileged ocular environment , it is likely that the early phase of leptospiral infection involves a non-inflammatory immune responses specific for LruA and LruB . Resulting antibodies may interact with cross-reacting proteins in lens and retinal tissues and may therefore initiate a process of desequestration of these ocular antigens , and possibly other components . How early after an initial infection this interaction results in development of the changes in eye , and what other pro-inflammatory changes , if any , are required remains to be determined .
|
Leptospira is the most common infectious cause of uveitis , a potentially debilitating inflammation of the eye . In our earlier work , we discovered that eye fluids of uveitic horses contain high levels of antibodies directed against novel leptospiral proteins , which we named LruA and LruB ( Leptospiral recurrent uveitis associated proteins A and B ) . Significantly , antibodies raised against LruA and LruB also recognize lens and retinal tissue . We have now identified the cross-reactive eye proteins as alpha-crystallin B , vimentin and beta-crystallin B2 . We also demonstrated that ocular fluids from uveitic horses contain high levels of antibodies recognizing alpha-crystallin B , vimentin and beta-crystallin B2 . These data suggest that antibodies directed against leptospiral LruA and LruB during infection can also react with eye proteins , alpha-crystallin B , vimentin and beta-crystallin B2 , potentially contributing to the severity of this eye disease .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"infectious",
"diseases/bacterial",
"infections",
"infectious",
"diseases/neglected",
"tropical",
"diseases",
"infectious",
"diseases/tropical",
"and",
"travel-associated",
"diseases",
"microbiology/cellular",
"microbiology",
"and",
"pathogenesis"
] |
2010
|
Cross-Reactivity of Antibodies against Leptospiral Recurrent Uveitis-Associated Proteins A and B (LruA and LruB) with Eye Proteins
|
The Australasian and South American marsupial mammals , such as kangaroos and opossums , are the closest living relatives to placental mammals , having shared a common ancestor around 130 million years ago . The evolutionary relationships among the seven marsupial orders have , however , so far eluded resolution . In particular , the relationships between the four Australasian and three South American marsupial orders have been intensively debated since the South American order Microbiotheria was taxonomically moved into the group Australidelphia . Australidelphia is significantly supported by both molecular and morphological data and comprises the four Australasian marsupial orders and the South American order Microbiotheria , indicating a complex , ancient , biogeographic history of marsupials . However , the exact phylogenetic position of Microbiotheria within Australidelphia has yet to be resolved using either sequence or morphological data analysis . Here , we provide evidence from newly established and virtually homoplasy-free retroposon insertion markers for the basal relationships among marsupial orders . Fifty-three phylogenetically informative markers were retrieved after in silico and experimental screening of ∼217 , 000 retroposon-containing loci from opossum and kangaroo . The four Australasian orders share a single origin with Microbiotheria as their closest sister group , supporting a clear divergence between South American and Australasian marsupials . In addition , the new data place the South American opossums ( Didelphimorphia ) as the first branch of the marsupial tree . The exhaustive computational and experimental evidence provides important insight into the evolution of retroposable elements in the marsupial genome . Placing the retroposon insertion pattern in a paleobiogeographic context indicates a single marsupial migration from South America to Australia . The now firmly established phylogeny can be used to determine the direction of genomic changes and morphological transitions within marsupials .
The phylogenetic relationships among the four Australasian and three South American marsupial orders have been intensively debated ever since the small species Dromiciops was taxonomically moved from Didelphimorphia into the new order Microbiotheria and the cohort Australidelphia was erected based on ankle joint morphology [1] . Australidelphia comprises the four Australasian marsupial orders and the South American order Microbiotheria , a close relationship suggesting a complex ancient biogeographic history of marsupials . However , the exact phylogenetic position of Microbiotheria within Australidelphia has so far eluded resolution . Moreover , sequence-based attempts to resolve the positions of the South American opossums ( Didelphimorphia ) and the shrew opossums ( Paucituberculata ) , which appear some few million years apart in the South American fossil layers close after the Cretaceous-Tertiary boundary [2] , relative to Australidelphia have so far been futile ( e . g . , [3] , [4] ) . The two recently sequenced marsupial genomes of the South American opossum ( Monodelphis domestica ) [5] and a kangaroo , the Australian tammar wallaby ( Macropus eugenii ) , provide a unique opportunity to apply a completely new approach to resolve marsupial relationships . The insertion patterns of retroposed elements , pieces of DNA that are copied via RNA intermediates and pasted randomly elsewhere in the genome , have successfully resolved the more than 130 million-year-old branch of therian mammals [6] and early placental mammalian divergences [7] as well as relationships within other mammalian orders [8] . Because the insertion sites are effectively random and parallel insertions or exact excisions are very rare [9] , the shared presence of retroposed elements at identical orthologous genomic locations of different species , families , or orders is a virtually homoplasy-free indication of their relatedness . Thus , the interpretation of retroposon markers is simple and straightforward: the presence of one of these elements in the orthologous genomic loci of two species signals a common ancestry , while its absence in another species signals a prior divergence [10] . No other sequenced mammalian genome has shown as high a percentage of discernible retroposed elements as marsupials ( 52% ) [5] , an extremely large number of possible informative markers . In addition , because young retroposed elements can insert into older elements , but older , inactive elements are not capable of inserting into younger ones , nested retroposon insertion patterns provide invaluable information about the relative times during which given retroposon families integrated into genomes . We used the transposition in transposition ( TinT ) application [11] to screen for such nested transpositions and to provide a complete picture of the succession of ancient retroposon activities so as to aid in the proper selection of element groups for resolving different parts of the marsupial tree .
After a complete screening of the opossum and kangaroo genomic sequences using the TinT algorithm , we recovered 8 , 245 and 4 , 499 nested retroposon insertions , respectively ( Table S1 ) . We then calculated the frequencies and time scales of short interspersed element ( SINE ) insertions using the likelihood approach implemented in TinT . The resulting pattern ( Figure 1 ) revealed three different groups of retroposed SINEs: ( 1 ) elements specific for the lineage leading to opossum ( RTESINE1 , SINE1_Mdo , SINE1a_Mdo ) , ( 2 ) elements specific for the lineage leading to kangaroo ( WALLSI1-4 , WSINE1 ) , and ( 3 ) a compiled group of elements active in both marsupial lineages . These three groups of elements were then used as a basis to screen for phylogenetically informative markers present in ( 1 ) the opossum lineage , ( 2 ) the branches leading to kangaroo , and ( 3 ) to find marsupial monophyly markers . Three different search strategies ( see Materials and Methods ) revealed ∼217 , 000 retroposon-containing genomic loci . Highly conserved exonic primers were generated for 228 loci and experimentally tested on a small set of species . After carefully screening the sequences , we selected 32 loci based on criteria outlined in the Materials and Methods section for amplification in 20 marsupial species ( Table S2 ) . We carefully aligned and analyzed approximately 440 marsupial sequences to reveal 53 informative markers ( Figure 2 , Table 1 ) . Ten of the phylogenetically informative markers accumulated in the metatherian genome since their split from placental mammals , approximately 130 million years ago ( MYA ) [12] , [13] , and before the earliest divergence of the modern marsupial mammals , 70–80 MYA [3] , [14] . All ten are absent in other mammals , significantly confirming the monophyly of marsupials ( p = 2 . 0×10−5; [10 0 0] [15] ) . The other 43 phylogenetically informative retroposon markers provide significant support for most of the basal splits within marsupials . The earliest marsupial divergence was previously impossible to resolve based on sequence data , which could not distinguish between Paucituberculata and Didelphimorphia as the sister group to Australidelphia [14] , [16]–[19] . We identified two markers ( MIR3_MarsA ) in the South American shrew opossums ( Paucituberculata ) that were also present in all Australidelphia but absent in Didelphimorphia ( Figure 2 ) . Albeit not significant ( p = 0 . 1111; [2 0 0] ) , this is the first molecular support for the earliest branching of Didelphimorphia , establishing it as the sister group to the remaining six marsupial orders . However , as significant support for this important marsupial branch requires three or more conflict-free markers [15] , we attempted to find additional retroposons for the marsupial root . To find the third marker for the supported topology ( Figure 2 ) , a MIR3_Mars element present in kangaroo plus Paucituberculata but absent in opossum , we recovered ten additional loci from in silico screening; two contained the previously detected markers and eight contained new retroposons . Unfortunately , experimental verification showed that the absences of MIR3_Mars in opossum were due to non-specific deletions . On the other hand , we also did not find any loci with MIR3_Mars elements present in opossum plus Paucituberculata but absent in kangaroo , which would have supported the alternative of a close relationship between Didelphimorphia and Paucituberculata . We then screened for markers that would support the alternative hypothesis of Paucituberculata being the sister to all marsupials by performing an exhaustive in silico pre-screening for orthologous MIR3_Mars elements present in short introns of opossum and kangaroo . Starting from ∼6 , 000 potentially informative loci , we selected 39 highly conserved MIR3_Mars-containing introns . However , experimental verification showed that all of the elements were also present in the order Paucituberculata ( Rhyncholestes ) , thus supporting the monophyly of marsupials ( data not shown ) , but not the basal divergence . Assuming that , in the entire genomes , there are more than just the two detected diagnostic insertions for the root , an expanded search including larger introns and conserved intergenic regions is required to find significant support for this branch . Such relaxed search conditions are expected to provide a huge number of additional markers spread over the entire marsupial tree , but will require extensive additional computational and experimental work . Molecular estimates have placed the earliest divergences of Marsupialia in the Late Cretaceous , 65–85 MYA [3] , [4] , [14] . To resolve placental mammalian Cretaceous divergences [20] , large amounts of sequence data were crucial to gain sufficient phylogenetic signal , which is a plausible explanation for the difficulties encountered in trying to resolve this branch in previous marsupial investigations [3] , [4] , [14] . However , morphological data have revealed several characters from the skull and postcranium , supporting Didelphimorphia as the sister to all marsupials [21] , consistent with our two molecular markers . Leaving the base of the tree for the time being , 13 of the original 53 markers were present in the South American Microbiotheria and the four Australasian orders but not in either Didelphimorphia or Paucituberculata , significantly supporting the monophyly of Australidelphia [1] ( p = 6 . 3×10−7; [13 0 0]; Figure 2 ) . The large number of phylogenetically informative markers indicates a long phylogenetic branch and/or a high degree of retroposon activity and fixation in the ancestral Australidelphia lineage . The branch separating Australidelphia from Didelphimorphia and Paucituberculata is one of the strongest supported and evolutionarily longest inter-ordinal branches in the marsupial tree [3] , [4] . The fossil Australian marsupial Djarthia murgonensis is the oldest , well-accepted member of Australidelphia . Thus , combined with the lack of old Australidelphian fossils from South America , the most parsimonious explanation of the biogeography of Australidelphia is of an Australian origin [22] . However , the poor fossil record from South America , Antarctica , and Australia does not exclude that Djarthia , like Dromiciops , could be of South American origin and had a pan-Gondwanan distribution . Additional fossils from Australia or South America will shed more light on the early Australidelphian relationships and their biogeography . Four markers significantly support the monophyletic grouping of the four Australasian orders to the exclusion of Microbiotheria ( p = 0 . 0123; [4 0 0]; Figure 2 ) . Several studies have presented evidence for the monophyly of the Australasian orders; these have typically been based solely on nuclear protein-coding genes such as ApoB , BRCA1 , IRBP , RAG1 , and vWF [4] , [17] , [19] , albeit with relatively low support values . By contrast , other sequence-based studies , relying completely or partially on mitochondrial data , find the South American order Microbiotheria nested within the Australasian orders [3] , [16] , [23] . Thus , the two competing hypotheses , Microbiotheria nested within or outside Australasian orders , have confounded the search for a reliable marsupial phylogeny . Two studies tried to combine the nuclear and mitochondrial data using different approaches to achieve a larger dataset with higher probability of resolving the marsupial phylogeny [14] , [18] . Only R/Y-coding , removing of sites [18] , or partitioning [14] reduced possible artefacts from the mitochondrial data enough to reach a topology consistent with the retroposon markers . However , both studies gave low support for the position of Microbiotheria , illustrating the difficulties in resolving a short branch using sequence data under difficult conditions , such as possible nucleotide composition bias problems and randomization of fast evolving sites . The support from two independent sources of phylogenetic information , our retroposon markers and nuclear genes [4] , [17] , [19] , invalidates the mitochondrial results [3] , [16] , [23] . Complete mitochondrial genomes can give misleading signals , as was demonstrated for the incorrect position of Monotremata among mammals [24] , and can even mislead phylogenetic reconstruction when mixed with nuclear data . The position of Microbiotheria has been intensely debated since the cohort Australidelphia was first suggested based on tarsal evidence [1] . After decades of uncertainty derived from molecular and morphological data , we have uncovered four independent diagnostic retroposon insertions that finally place the South American order Microbiotheria at its correct place in the marsupial tree ( Figure 2 ) . Therefore , we propose the new name Euaustralidelphia ( “true Australidelphia” ) for the monophyletic grouping of the four Australasian orders Notoryctemorphia , Dasyuromorphia , Peramelemorphia , and Diprotodontia . The relationship among the four Australasian orders is not resolved , and of special interest is the phylogenetic position of the marsupial mole , Notoryctes typhlops , which has been debated for a long time [3] , [4] , [14] , [17]–[19] , [21] , [23] . The marsupial mole is the only burrowing marsupial and is found in the deserts of Australia . The eyes of the marsupial mole are vestigial and the fore- and hind limbs are morphologically derived due to the burrowing lifestyle . The derived morphology and the fact that the marsupial mole is the single species in the order Notoryctemorphia have complicated attempts to resolve its phylogenetic position relative to the other three Australian orders . Most analyses of molecular sequence data find the marsupial mole closely related to the orders Dasyuromorphia and Peramelemorphia , but the support values are generally weak [3] , [4] , [14] , [17]–[19] , [23] , and the exact phylogenetic position relative to the other two orders is yet to be determined . During the retroposon screening one marker was found supporting a grouping of Notoryctes , Dasyuromorphia , and Peramelemorphia ( p = 0 . 3333 [1 0 0] ) . The single retroposon marker is in agreement with the results from the sequence data . Extended screening of retroposons can provide additional evidence for the position of the marsupial mole among marsupials and which of the orders , Dasyuromorphia or Peramelemorphia , is the sister group . Of the original 53 markers , 18 of them provide significant support for the monophyly of each of the five multi-species marsupial orders: five for Didelphimorphia ( p = 0 . 0041; [5 0 0] ) , three each for Paucituberculata , Dasyuromorphia , and Diprotodontia ( p = 0 . 037; [3 0 0] ) , and four for Peramelemorphia ( p = 0 . 0123; [4 0 0] ) . Four of the remaining markers provide non-significant support for various intra-ordinal relationships of Diprotodontia ( Figure 2 ) . Two of them support the division between Vombatiformes ( wombats and koala ) and Phalangerida ( kangaroos , possums ) ( p = 0 . 1111; [2 0 0] ) , challenging the results from mitochondrial sequence-based studies ( [3] , but see [25] ) , and one marker each supports the grouping of the possums Tarsipes and Pseudocheirus and that of the kangaroos Macropus and Potorous ( p = 0 . 3333 [1 0 0] ) . One final marker supports the grouping of the Didelphis and Metachirus . The outstanding advantage of using retroposon presence/absence data for phylogenetic reconstructions is the low probability of insertion homoplasy . Independent parallel insertions of identical elements or exact deletions are extremely rare [9] , but nevertheless not completely negligible , especially after genome-wide in silico screening of rare informative loci . LINE1-mobilized elements , in particular , show a slight preference for a TTAAAA consensus insertion motif [26] , but on the other hand , such elements are rare in the deep phylogenetic branches of marsupials ( Figure 1; Figure S1 ) . Excluding the more frequent near identical insertions or unspecific deletions requires careful aligning and interpretation of orthologous informative markers ( see Materials and Methods and Dataset S1 ) . Another possible source of errors is incomplete lineage sorting ( polymorphism during speciation ) or ancestral hybridization that can affect any marker system . Particularly short internal branches of a tree ( rapid speciation ) and biased in silico pre-screening for potential phylogenetically informative loci are exposed to such effects [27] . The available genomes of the opossum and the kangaroo placed us in the advantageous situation of independently pre-screening two distant branches of the marsupial tree . All 53 experimentally verified markers confine a phylogenetic tree free of any marker conflicts . Fourteen of them were randomly inserted as a second marker in specific loci . For most internal branches we found significant support for the underlying prior hypothesis by three or more markers with a clear rejection of alternative hypotheses . Given the limitations just mentioned , the retroposon marker system identified a clear separation between the South American and Australasian marsupials . Thus , the current findings support a simple paleobiogeographic hypothesis , indicating only a single effective migration from South America to Australia , which is remarkable given that South America , Antarctica , and Australia were connected in the South Gondwanan continent for a considerable time . The search for diagnostic South American or Australidelphian marsupial morphological characters has been so far confounded by the lack of a resolved marsupial phylogeny [21] , [22] , [28] . The newly established marsupial tree can now be applied not only to morphological and paleontological studies but also to clearly distinguish genomic changes .
The marsupial classification of Aplin and Archer [29] has been followed throughout the text . Representatives of all seven marsupial orders were included for retroposon screening . Except for the two single-species orders , at least two species per order were investigated . For all orders except Didelphimorphia , representative species were chosen to cover the deepest splits within each order . Didelphimorphia: Monodelphis domestica ( gray short-tailed opossum ) , Didelphis virginiana ( Virginia opossum ) , Metachirus nudicaudatus ( brown four-eyed opossum ) . Paucituberculata: Rhyncholestes raphanurus ( Chilean shrew opossum ) , Caenolestes fuliginosus ( silky shrew opossum ) . Microbiotheria: Dromiciops gliroides ( monito del monte ) . Notoryctemorphia: Notoryctes typhlops ( marsupial mole ) . Dasyuromorphia: Phascogale tapoatafa ( brush-tailed phascogale ) , Dasyurus geoffroii ( western quoll ) , Sminthopsis crassicaudata ( fat-tailed dunnart ) , Myrmecobius fasciatus ( numbat ) . Peramelemorphia: Macrotis lagotis ( bilby ) , Perameles gunnii ( eastern barred bandicoot ) , Isoodon obesulus ( southern brown bandicoot ) . Diprotodontia: Tarsipes rostratus ( honey possum ) , Pseudocheirus peregrinus ( common ringtail possum ) , Trichosurus vulpecula ( common brushtail possum ) , Macropus robustus ( wallaroo ) , Potorous tridactylus ( long-nosed potoroo ) , Vombatus ursinus ( common wombat ) . The marsupial genome harbors about 500 different families of interspersed repeats [30] . Several retroposon families were active around and after the split of Australasian [31] and South American marsupials and potentially encrypt information about their phylogeny . For successful and focused experimental retroposon screening it is invaluable to have , a priori , a map of the ancestral retroposon activities . The previously developed TinT method [11] relies on a numeral compilation ( Table S1 ) of nested transpositions ( TinT ) extracted from RepeatMasker coordinates and visualized after calculating their maximal activity probabilities . For experimental application , 24 subtypes of small SINE elements , active over the range of marsupial evolution , were pre-selected for the TinT analysis ( Figure 1 ) . The complete statistics of SINE elements in M . domestica and M . eugenii are given in Figure S2 . The assembled genome of M . domestica ( MonDom5 ) and the draft genome of M . eugenii were used to pre-select potential phylogenetically informative intronic retroposon loci . Three different in silico high-throughput strategies , implemented in specially developed C-scripts , were applied to extract the genomic information . The 228 loci extracted by these three strategies were experimentally analyzed in a small subset of eight representative marsupial species ( see strategy A ) . The sequences from the experimental screening were aligned and carefully inspected for ( 1 ) identical genomic insertion points of retroposed elements , ( 2 ) identical element orientation , ( 3 ) identical element subtypes , ( 4 ) as far as available , concurrent element flanking repeats , ( 5 ) shared diagnostic indels , and ( 6 ) the consistency of insertion in representative species . The 32 selected loci mentioned above ( in A–C ) were determined to be phylogenetically informative ( elements present at orthologous genomic locations in two or more species ) and were screened in a larger taxon sampling comprised of 20 marsupials covering all seven orders ( see taxon sampling ) . After sequencing , 53 phylogenetically informative retroposon markers were identified from the 32 introns . More than one informative marker was recovered in each of 15 of the introns , due to independent retroposon insertions ( Table 1 ) , and an additional 18 autapomorphic insertions were found . Total DNA was extracted from tissues using the standard phenol-chloroform protocol [32] . Approximately 10–50 ng DNA was used in each 25 µl PCR amplification using ThermoPrime Taq ( ABgene , Hamburg ) with 1 . 5 mM MgCl2 . All PCR reactions were prepared for high throughput in 96-well plates and the DNA was amplified using the touchdown PCR strategy , decreasing the annealing temperature stepwise by 1°C for the initial ten cycles , followed by 25 cycles at 45°C annealing temperature ( for primers see Table S3 ) . The initial screening was performed using eight representative marsupial species ( see above ) and PCR products were visualized on 1% agarose gels to detect presence/absence patterns via the size shifts of fragments . The PCR products indicating such size shifts were purified and ligated into the TA cloning vector pDrive ( Qiagen , Hilden ) . Ligations were left overnight at 7°C and transformed into XL1-Blue competent cells . Colonies were PCR screened using standard M13 primers . For each positive PCR product , at least two colonies were sequenced . All sequence alignments were conducted using Se-Al [33] . Sequences were screened for retroposons using the RepeatMasker program ( http://www . repeatmasker . org/RMDownload . html ) and a specific retroposon library ( available upon request ) . From the markers in Table 1 we built a presence/absence ( 1/0 ) data matrix of retroposons ( Figure S3 ) . The strict consensus , most parsimonious tree was reconstructed using the irrev . up option of character transformation implemented in PAUP*4 . 0b10 [34] in a heuristic search performed using 1 , 000 random sequence addition and tree-bisection and reconnection ( TBR ) branch swapping . Because strictly marsupial-specific retroposons were investigated , the hypothetical human outgroup was coded 0 . The resulting tree had a length of 53 and a consistency index of 1 . The tree topology shown in Figure 2 refers to the derived parsimony tree . Due to the complexity and randomness of retroposon insertions , there are an extremely large number of possible unique character states ( insertion sites ) , and maximum parsimony analyses converge to maximum likelihood estimators [35] . Evidence from retroposon markers is considered to be statistically significant when three or more markers are found supporting one node ( i . e . , when p<0 . 05 ) [15] .
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Ever since the first Europeans reached the Australian shores and were fascinated by the curious marsupials they found , the evolutionary relationships between the living Australian and South American marsupial orders have been intensively investigated . However , neither the morphological nor the more recent molecular methods produced an evolutionary consensus . Most problematic of the seven marsupial groups is the South American species Dromiciops gliroides , the only survivor of the order Microbiotheria . Several studies suggest that Dromiciops , although living in South America , is more closely related to Australian than to South American marsupials . This relationship would have required a complex migration scenario whereby several groups of ancestral South American marsupials migrated across Antarctica to Australia . We screened the genomes of the South American opossum and the Australian tammar wallaby for retroposons , unambiguous phylogenetic markers that occupy more than half of the marsupial genome . From analyses of nearly 217 , 000 retroposon-containing loci , we identified 53 retroposons that resolve most branches of the marsupial evolutionary tree . Dromiciops is clearly only distantly related to Australian marsupials , supporting a single Gondwanan migration of marsupials from South America to Australia . The new phylogeny offers a novel perspective in understanding the morphological and molecular transitions between the South American and Australian marsupials .
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[
"Abstract",
"Introduction",
"Results/Discussion",
"Materials",
"and",
"Methods"
] |
[
"evolutionary",
"biology",
"computational",
"biology/genomics",
"evolutionary",
"biology/evolutionary",
"and",
"comparative",
"genetics"
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2010
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Tracking Marsupial Evolution Using Archaic Genomic Retroposon Insertions
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Despite extensive study , little is known about the origins of the mutualistic bacterial endosymbionts that inhabit approximately 10% of the world's insects . In this study , we characterized a novel opportunistic human pathogen , designated “strain HS , ” and found that it is a close relative of the insect endosymbiont Sodalis glossinidius . Our results indicate that ancestral relatives of strain HS have served as progenitors for the independent descent of Sodalis-allied endosymbionts found in several insect hosts . Comparative analyses indicate that the gene inventories of the insect endosymbionts were independently derived from a common ancestral template through a combination of irreversible degenerative changes . Our results provide compelling support for the notion that mutualists evolve from pathogenic progenitors . They also elucidate the role of degenerative evolutionary processes in shaping the gene inventories of symbiotic bacteria at a very early stage in these mutualistic associations .
Obligate host-associated bacteria often have reduced genome sizes in comparison to related bacteria that are known to engage in free-living or opportunistic lifestyles [1] . This is exemplified by inspection of the genome sequences of mutualistic , maternally transmitted , bacterial endosymbionts of insects , many of which have been maintained in their insect hosts for long periods of evolutionary time [2] . Often these obligate endosymbionts maintain only a small fraction of the gene inventory that is found in related free-living counterparts [3]–[5] , indicating that the obligate host-associated lifestyle facilitates genome degeneration and size reduction . At a simple level , the process of genome degeneration in obligate endosymbionts can be viewed as a streamlining of the gene inventory to yield a minimal gene set that is compatible with the symbiotic lifestyle . Genes that have no adaptive benefit are inactivated and deleted as a consequence of mutations that accumulate under relaxed selection at an increased rate in the asexual symbiotic lifestyle as a result of frequent population bottlenecks occurring during symbiont transmission [6] . Although we now have a detailed understanding of the mechanisms and evolutionary trajectory of genome degeneration in ancient obligate insect symbionts , the fundamental question of how these mutualistic associations arise remains to be answered . Studies focusing on insect-bacterial symbioses of recent origin show that closely related bacterial endosymbionts are often found in distantly related insect hosts [7] , [8] . This could be explained by the interspecific transmission of symbionts , mediated by parasitic wasps and mites that facilitate the transfer of symbionts between distantly related hosts [9] , [10] . Horizontal symbiont transmission could also be mediated by intraspecific mating , as demonstrated in the pea aphid [11] . Another possibility is that symbionts could be acquired de novo from an environmental source . Symbiont acquisition , at least initially , requires the symbiont to overcome or evade the insect immune response . Given that many insects are known to possess a potent immune system that repels invading microorganisms [12] , it has been assumed that mutualistic symbionts arise from pathogenic progenitors that have evolved specialized molecular mechanisms to facilitate evasion of the immune response and invasion of insect tissues [2] . In support of this notion , it has been shown that the genomes of recently acquired mutualistic insect endosymbionts maintain genes similar to virulence factors and toxins that are found in related plant and animal pathogens [13]–[18] . In the current study we describe the discovery of a novel human-infective bacterium , designated “strain HS” , isolated from a patient who sustained a hand wound following impalement with a tree branch . Phylogenetic analyses show that strain HS is a member of the Sodalis-allied clade of insect endosymbionts . Comparative analyses of the genome sequences of strain HS and related insect symbionts suggest that close relatives of strain HS gave rise to mutualistic associates in a wide range of insect hosts .
A 71-year-old male presented to his primary care physician for a routine physical examination three days after sustaining a puncture wound to the right hand . The patient fell and was impaled between the thumb and forefinger by a ∼1 cm diameter branch while removing branches from a dead crab apple tree . Upon presentation the patient denied fever or other constitutional symptoms and had a mild peripheral blood monocytosis ( 11 . 8%; reference range = 1 . 7–9 . 3% ) . A palpable cyst was noted in the right hand at the sight of impalement . Warm compresses were applied and cephalexin was prescribed at a dose of 500 mg four times daily for 10 days . The patient was evaluated again three days later due to continuing wound pain . The cyst was drained by aspiration and serosanguineous fluid was submitted for Gram stain and bacterial culture . The Gram stain showed scattered white blood cells , but no bacteria were visualized . A follow-up visit seven days later revealed the presence of an abscess , although the patient was afebrile and without local lymphadenopathy . The abscess was again drained by aspiration and the patient was advised to consult an orthopedic surgeon for evaluation . Subsequent surgery , approximately six weeks later , removed several foreign bodies from the wound and the patient recovered on a second course of cephalexin without incident . Two days after the original cyst aspiration , small numbers of gram negative rods resembling enteric bacteria were isolated on MacConkey agar at 35°C and 5% CO2 . Colonies were wet , mucoid , variable in size , and slowly fermented lactose . The isolate could not be definitively identified by a manual phenotypic method ( RapID ONE , Remel , Lenexa KS ) and was misidentified as Escherichia coli at 98% confidence by an automated system ( Phoenix , BD Diagnostics , Sparks , MD ) . Phylogenetic analysis of 16S rRNA placed strain HS in a well-supported clade comprising Sodalis-allied insect endosymbionts sharing >97% sequence identity in their 16S rRNA sequences ( Figure 1 ) , which is a commonly used threshold for species-level conservation among bacteria [19] . Aside from strain HS , the closest non-insect associated relative of this clade is Biostraticola tofi , which was isolated from a biofilm on a tufa deposit in a hard water rivulet [20] . However , B . tofi shares only 96 . 5% sequence identity in 16S rRNA with its closest insect associated relative ( S . glossinidius ) , while strain HS shares >99% sequence identity with the primary endosymbionts of the grain weevils Sitophilus oryzae and S . zeamais and with recently discovered endosymbionts from the chestnut weevil , Curculio sikkimensis and the stinkbug , Cantao occelatus [21]–[23] . Analysis of a protein-coding gene , groEL , corroborated these findings , confirming that strain HS is a close relative of the grain weevils , chestnut weevil and stinkbug endosymbionts ( Figure 1 ) . To compare the genome sequences of strain HS and related Sodalis-allied endosymbionts , we aligned a draft sequence assembly of strain HS , comprising a total of 5 . 15 Mb of DNA in 271 contigs , with the complete genome sequences of the tsetse fly secondary endosymbiont , S . glossinidius ( 4 . 3 Mb ) [24] , [25] , and the recently completed sequence of Sitophilus oryzae primary endosymbiont ( SOPE; 4 . 5 Mb ) . The resulting alignments ( Figure 2 ) reveal a remarkable level of conservation in gene content and organization between strain HS , S . glossinidius and SOPE . To determine if this high level of conservation is simply a consequence of the close evolutionary relationship between these bacteria , we also constructed a whole genome sequence alignment between strain HS and Dickeya dadantii , which represents the next most closely related free-living bacterium whose whole genome sequence is available ( Figure S1 ) . This alignment shows that strain HS and D . dadantii are substantially more divergent in terms of their gene inventories , consistent with the notion that they occupy distinct ecological niches . Considering the alignments between strain HS , S . glossinidius and SOPE , it is notable that while the genome sequences of strain HS and S . glossinidius display an increased level of co-linearity , the relationship between strain HS and SOPE is predicted to be closer based on the fact that they share a higher level of genome-wide sequence identity ( Figure 2 ) . The genome sequences of strain HS and S . glossinidius demonstrate a typical pattern of polarized nucleotide composition in each replichore ( G+C skew , Figure 2 ) , whereas the SOPE genome has numerous perturbations in G+C skew that must result from recent chromosome rearrangements . These rearrangements likely arose as a consequence of intragenomic recombination events between repetitive insertion sequence ( IS ) -elements , which are highly abundant in the SOPE genome ( Figure S2 ) , and have been documented as a causative agent of deletogenic rearrangements in other bacteria [26]–[28] . Although the gene inventories of strain HS , S . glossinidius and SOPE share many genes in common , as expected given their close evolutionary relationship , each bacterium also maintains a fraction of unique genes . In strain HS we identified a total of 1 . 9 Mb of DNA encoding genes not found in either S . glossinidius or SOPE that are classified in a wide range of functional categories ( Figure 3 ) . This indicates that strain HS has many unique genetic and biochemical properties , and is consistent with the observation that strain HS , unlike the fastidious and microaerophilic S . glossinidius [14] , grows under atmospheric conditions on minimal media . In addition , strain HS maintains a number of unique genes sharing high levels of sequence identity with virulence factors found in both animal and plant pathogens , including an Hrp-type effector protein that is characteristically utilized by plant pathogenic bacteria [29] ( Table S1 ) . This may be indicative of the ability of strain HS to sustain infection in plant tissues . In comparison with strain HS , the unique fractions of the S . glossinidius and SOPE chromosomes are composed almost exclusively of components of mobile genetic elements , including integrated prophage islands and IS-elements . Following excision of these mobile genetic elements in silico prior to alignment , the resulting genome sequences of S . glossinidius ( 3 . 21 Mb ) and SOPE ( 3 . 15 Mb ) represent near-perfect subsets of the strain HS genome ( Figure 2 ) , indicating that S . glossinidius and SOPE are abridged derivatives of a strain HS-like ancestor . To further understand genetic differences between strain HS , S . glossinidius and SOPE , we analyzed three genomic regions containing relatively high densities of pseudogenes in both S . glossinidius and SOPE ( Figure 4 ) . The most notable finding to arise from this comparison is the absence of pseudogenes in the three genomic regions of strain HS . Furthermore , our comparative analysis shows that S . glossinidius and SOPE each have a unique complement of pseudogenes . Indeed , even for orthologous genes that have been inactivated in both S . glossinidius and SOPE , mutations leading to gene inactivation in each insect symbiont genome are distinct , indicating that gene inactivation and loss took place independently in S . glossinidius and SOPE , mostly as a consequence of small frameshifting indels . However , it should also be noted that the reductions observed in the gene inventories of S . glossinidius and SOPE are very similar at the level of functional categories , indicating that the insect-associated lifestyle imposes similar constraints on the retention of genes encoding core functions such as replication , transcription , translation and energy generation ( Figure 3 ) . In order to determine the number of pseudogenes throughout the genome of strain HS , we performed a manual annotation and careful inspection of the complete draft strain HS sequence assembly . Out of a total of 4 , 002 intact candidate ORFs identified in the draft annotation ( Table S1 ) , only 48 ( including phage and IS elements ) were found to be translationally frameshifted or truncated by more than 10% of the size of their most closely related orthologs in the GenBank database ( Table 1 ) . This finding stands in stark contrast to the gene inventories of both S . glossinidius and SOPE , in which pseudogenes represent a substantial fraction of their total genomic coding capacity ( Figure 2 ) [24] , [25] . Thus , for both S . glossinidius and SOPE , the predominant evolutionary trajectory following obligate insect association involved the inactivation and/or loss of a substantial component of the ancestral ( strain HS-like ) gene inventory . The close evolutionary relationships between strain HS , S . glossinidius and SOPE indicate that the respective insect symbioses are recent in origin . This raises the possibility that a subset of selectively neutral genes in the S . glossinidius and SOPE genomes have not yet accumulated mutations that lead to disruption of their open reading frames . Such “cryptic” pseudogenes are assumed to have no adaptive benefit in the symbiosis and are expected to accumulate nonsense and/or frameshifting mutations in the future [30] . To determine if the genomes of S . glossinidius and SOPE maintain cryptic pseudogenes , we compared the average size of all strain HS genes with the average sizes of strain HS orthologs that are classified either as intact , absent ( lost via large deletion ) or pseudogenes ( visibly disrupted ) in the S . glossinidius and SOPE genomes ( Figure 5 ) . First , it is important to note that the average size of the absent strain HS orthologs in S . glossinidius and SOPE is not significantly different from the average size of all strain HS ORFs , indicating that large deletion events are not significantly biased with respect to size . However , in both S . glossinidius and SOPE , genes in the pseudogene class were found to have a larger average size in comparison to all strain HS orthologs . Similarly , genes in the intact class were found to have a smaller size in comparison to all strain HS orthologs . This can be explained by the fact that larger genes have an increased likelihood of accumulating at least one disrupting mutation in a given time frame . Based on the same logic , we can infer that the intact gene class contains a subset of smaller , cryptic pseudogenes that have not yet had sufficient time to accumulate any nonsense or frameshifting mutations . Furthermore , since the difference between the average size of intact and disrupted genes is significantly larger in SOPE ( 192 bases ) in comparison to S . glossinidius ( 77 bases ) , it follows that SOPE likely maintain a larger number of cryptic pseudogenes than S . glossinidius . In a previous study , the numbers of cryptic pseudogenes in the recently derived aphid symbiont , Serratia symbiotica , were estimated by extrapolation from a Poisson distribution of disrupting mutations found in existing pseudogenes [30] . The expectation of a Poisson distribution is based on the assumption that the switch to an insect-associated lifestyle leads to the synchronous relaxation of selection on genes no longer required for persistence in an insect host [30] . In the case of both SOPE and S . glossinidius , plots of the densities of disrupting mutations in pseudogenes indicate that the data is overdispersed relative to a Poisson distribution ( Figure 6 ) . This effect is exacerbated when current ORF sizes are used for the calculation of mutation densities . This results from the fact that large deletions erase any evidence of previous disrupting mutations . In order to estimate the numbers of cryptic pseudogenes in SOPE and S . glossinidius , we used a Monte Carlo simulation in which a randomly selected class of candidate pseudogenes , selected from all strain HS genes , was permitted to accumulate random disrupting mutations over time , in accordance with ORF size . In this simulation , both pseudogene counts and size differences between the strain HS orthologs of intact and disrupted S . glossinidius and SOPE genes were recorded at regular intervals . The simulation was repeated with an increasing number of neutral genes until the size difference and pseudogene count matched the empirically determined values shown in Figure 5 and Table 1 . For S . glossinidius and SOPE , matches were obtained when the predicted numbers of genes evolving under relaxed selection reached 1 , 470 and 1 , 530 , respectively ( Figure 7 ) . Thus , although S . glossinidius and SOPE are predicted to have almost the same numbers of genes evolving under relaxed selection , the degeneration of pseudogenes is at a more advanced stage in S . glossinidius , and SOPE has a larger proportion of neutral genes that have not yet acquired any obvious disrupting changes . Assuming that the relaxation of selection was imposed synchronously at the onset of obligate insect-association , these results suggest that the SOPE-weevil symbiosis originated more recently than the S . glossinidius-tsetse fly symbiosis . This is further supported by a comparison of the estimates of corrected mutation density derived from the simulation ( Figure 7 ) . While SOPE is estimated to maintain only 2 disrupting mutations/kb of pseudogenes , S . glossinidius is estimated to maintain more than twice that density of disrupting substitutions ( 4 . 39 disrupting mutations/kb ) . On a related note , we were unable to utilize dN/dS ratios to identify cryptic pseudogenes in SOPE or S . glossinidius . This is likely due to the fact that stochastic variation resulting from differences in expression level , codon bias and other factors greatly exceeds any signal resulting from a recent relaxation of selection . The transition to obligate insect-association is also known to catalyze base composition bias and accelerated polypeptide sequence evolution on the part of the symbiont [31] . The results outlined in Table 1 show that the genomic GC-contents of S . glossinidius and SOPE are lower than that of strain HS . However , to avoid any bias arising from the differential gene content of these organisms , we also performed comparative analyses focusing solely on orthologous sequences . This facilitated the comparison of 1 , 355 intact genes and 1 , 376 pseudogenes shared between strain HS and S . glossinidius , and 1 , 414 intact genes and 1 , 194 pseudogenes shared between strain HS and SOPE . Although the symbioses in the current study are anticipated to be relatively recent in origin , comparisons focusing on these shared sequences also show that both S . glossinidius and SOPE have reduced GC-contents relative to strain HS ( Figure 8 ) . This effect is most notable at 4-fold degenerate ( GC4 ) sites in S . glossinidius , which demonstrate the highest levels of sequence divergence and AT-bias in comparison to orthologs from strain HS . Assuming that the onset of AT-bias is coincident with the origin of symbiosis , this further supports the notion that the symbiosis involving S . glossinidius is more ancient in origin . It is also notable that the number of substitutions at the 2nd codon position sites of pseudogenes ( dGC2 , Figure 8 ) is elevated by approximately the same extent ( relative to intact genes ) in S . glossinidius and SOPE . This implies that pseudogenes have been evolving under relaxed selection for approximately the same proportion of time since each symbiont diverged from strain HS . However , given that sequence divergence at silent sites ( GC4 ) is greater between strain HS and S . glossinidius , this again invokes the interpretation that pseudogenes arose earlier in the S . glossinidius line of descent . It is also interesting to note that the level of divergence at GC2 sites ( dGC2 , Figure 8 ) relative to GC4 sites ( dGC4 , Figure 8 ) is greater in SOPE than in S . glossinidius . This can be explained by the fact that the pairwise comparison between strain HS and SOPE is expected to capture an increased proportion of mutations that are fixed in the insect-associated phase of life in which selection on polypeptide evolution is anticipated to be more relaxed . Considering only those mutations that have led to gene inactivation , we found that the relative ratios of truncating ( large ) indels , frameshifting ( small ) indels and nonsense mutations are similar in SOPE and S . glossinidius ( Table 2 ) . Inspection of the data reveals that small frameshifting deletions constitute the most abundant class of mutations leading to gene inactivation . However , it should be noted that the effects of large deletions are , for obvious reasons , not captured in our analyses . Another important point is that IS-element insertions appear to have contributed relatively little to the overall spectrum of mutations leading to gene inactivation in SOPE , representing only 10% of the total count . Indeed , the majority of IS-elements in SOPE are located either in intergenic regions or , more commonly , clustered inside other IS-elements . One potential explanation is that IS-element insertions in genic sequences might be more deleterious towards processes of transcription and/or translation in the cell , such that pseudogenes with IS-element insertions are preferentially deleted relative to pseudogenes with nonsense point mutations or small indels . However , it is conspicuous that clustering of IS-elements has also been reported for mobile DNA elements found in eukaryotes , including the MITE elements found in plants [32] and mosquitoes [33] , and the Alu and L1 elements found in the human genome [34] . The relative paucity of IS-elements in genic DNA is surprising given the fact that the SOPE genome has such a large number of pseudogenes that provide neutral space for IS-element colonization . However , the inability of IS-elements to occupy this territory can be rationalized as a consequence of an inherited adaptive bias that facilitates the avoidance of genic insertion . This makes sense when considering the perspective of an IS-element residing in a free-living bacterium that has relatively few dispensable genes . It also explains the propensity for IS-elements to insert themselves into the sequences of other IS-elements , because the safety of this approach has already been validated by natural selection . Clearly , in the case of SOPE , when the opportunity arose for expansion into novel territory ( i . e . neutralized genic sequences ) , IS-elements were largely unable to overcome these basic evolutionary directives .
Phylogenetic analysis of strain HS indicates that it shares a close relationship with the Sodalis-allied endosymbionts that are found in a wide range of insect hosts , including tsetse flies , weevils , lice and stinkbugs . In terms of 16S rRNA sequence identity , strain HS is most closely related to endosymbionts found in the chestnut weevil , Curculio sikkimensis and the stinkbug , Cantao occelatus . Interestingly , only limited numbers of these insects maintain Sodalis-allied endosymbionts in their natural environment [21]–[23] , suggesting that they do not maintain persistent ( maternally-transmitted ) infections . Furthermore , it is notable that the sequences from strain HS , C . sikkimensis and C . occelatus are localized on very short branches in our phylogenetic trees , indicating that these particular lineages are evolving slowly in comparison to other Sodalis-allied endosymbionts . This low rate of molecular sequence evolution , along with the observation that the strain HS genome shows no sign of the characteristic degenerative changes that are known to accompany the transition to the obligate host-associated lifestyle , leads us to propose that strain HS represents an environmental progenitor of the Sodalis-allied clade of insect endosymbionts . Closely related members of the Sodalis-allied clade of insect endosymbionts have now been identified in a wide range of distantly related insect taxa , including some that are known to feed exclusively on plants and others that are known to feed exclusively on animals [8] . Although strain HS was isolated from the wound of a human host , it is difficult to assess the extent of its pathogenic capabilities , due to the fact that antibiotic treatment commenced three days prior to microscopic examination and culturing . In addition , the available evidence indicates that the original source of the infection was a branch from a dead crab apple tree . This implies that strain HS was present either on the bark or in the woody tissue of this tree , possibly acting as a pathogen or saprophyte . Furthermore , it is interesting to note that C . sikkimensis and C . occelatus , whose symbionts are most closely related to strain HS , are both known to feed on trees [35] , [36] . In addition , some wood and bark-inhabiting longhorn beetles , including Tetropium castaneum ( Figure 1 ) have recently been found to maintain Sodalis-allied endosymbionts [37] . Moreover , the ability of strain HS to persist in both plant and animal tissues is compatible with the observation that diverse representatives of both herbivorous and carnivorous insects have acquired Sodalis-allied symbionts . In a comparative sense , relationships involving the Sodalis-allied endosymbionts are considered to be relatively recent in origin . Indeed , evidence of host-symbiont co-speciation only exists in the case of grain weevils , Sitophilus spp . , which were estimated to have co-evolved with their Sodalis-allied endosymbionts for a period of around 20 MY , following the replacement of a more ancient lineage of endosymbionts in these insects [38] , [39] . The notion of a recent origin of the Sodalis-allied endosymbionts is further supported by the fact that the whole genome sequence of S . glossinidius is substantially larger than that of long-established mutualistic insect endosymbionts , and is close to the size of related free-living bacteria [24] . However , the S . glossinidius genome does have an unusually low coding capacity resulting from the presence of a large number of pseudogenes [24] , [25] . This suggests that S . glossinidius is at an intermediate stage in the process of genome degeneration , in which many protein coding genes have been inactivated by indels and nonsense mutations but have not yet been deleted from the genome . In the current study we show that the genome of the grain weevil symbiont , SOPE , is at a similar stage of degeneration as evidenced by the presence of a comparable number of pseudogenes and a large number of repetitive insertion sequence elements . In a comparative sense , it is interesting to note that SOPE and strain HS share a substantially higher level of sequence similarity , genome-wide , in comparison to S . glossinidius and strain HS ( Figure 2 ) . In the context of the progenitor hypothesis , the disparity in the relationship between strain HS , SOPE and S . glossinidius can be explained by the idea that there may be a substantial level of diversity among free-living relatives of the Sodalis-allied symbionts in the environment , and that we simply happened to characterize a representative that is more closely related to the ancestral progenitor of SOPE . While this is likely to be true to some extent , the close relationship between strain HS and SOPE can also be explained by the notion that the SOPE-grain weevil symbiosis has a more recent origin than the S . glossinidius-tsetse symbiosis . Our results provide several compelling lines of evidence in support of this idea . Most significantly , we found that the pseudogenes of S . glossinidius contain a higher average density of disrupting mutations relative to their counterparts in SOPE . This suggests that the pseudogenes of S . glossinidius have been evolving under relaxed selection for a longer period of time , consistent with the hypothesis of a more ancient origin of host association catalyzing the neutralization of these genes . In addition , the genome of SOPE is predicted to have a larger proportion of “cryptic” pseudogenes; genes evolving neutrally that have not yet had sufficient time to accumulate nonsense or frameshifting mutations that disrupt their translation . Finally , it is notable that the GC4 sites of S . glossinidius have a higher AT-content than those of strain HS and SOPE ( Figure 8 ) . Assuming that the AT-bias at GC4 sites accumulates in a clock-like manner following the onset of the symbiosis , this again supports a more ancient origin for the symbiosis involving S . glossinidius . In the current study , a comparative analysis of the genome sequences of strain HS , SOPE and S . glossinidius has provided an unprecedentedly detailed view of the nascent stages of genome degeneration in symbiosis . Taken together , our results indicate that irreversible degenerative changes , including gene inactivation and loss , in addition to base composition bias , commence rapidly following the onset of an obligate relationship . Indeed , the close relationship observed between strain HS and SOPE illustrates the potency of the degenerative evolutionary process at an early stage in the evolution of a symbiotic interaction . This is exemplified by the fact that SOPE is predicted to have lost 55% of its ancestral gene inventory ( 34% via gene loss and 21% via gene inactivation ) in a period of time sufficient to incur a substitution frequency of only 4 . 3% at the highly variable GC4 sites of intact protein coding genes ( Figure 8 ) . Although estimates of genome wide synonymous clock rates vary by several orders of magnitude in bacteria [40] , an estimate of μs = 2 . 2×10−7 , derived recently for another insect endosymbiont , Buchnera aphidicola [41] , places the divergence of strain HS and SOPE at only c . 28 , 000 years , which is much more recent than previous estimates obtained for the origin of the SOPE symbiosis [38] , [39] . While the broad distribution of recently derived endosymbionts in phylogenetically distant insect hosts has previously been attributed to interspecific symbiont transfer events [10] , [11] , the results outlined in the current study indicate that diverse insect species can also acquire novel symbionts through the domestication of bacteria that reside in their local environment . In the case of S . glossinidius and SOPE , our comparative analyses support the notion that these symbionts were acquired independently , as evidenced by the presence of distinct mutations in shared pseudogenes . This also implies that symbionts rapidly become specialized towards a given host , likely restricting their abilities to switch hosts . Although the current study highlights the first description of a close free-living relative of the Sodalis-allied symbionts , it should be noted that environmental microbial diversity is vastly undersampled [42] . Thus , it is conceivable that close relatives of extant insect endosymbionts , such as strain HS , are widespread in nature and provide ongoing opportunities for a wide range of insect hosts to domesticate new symbiotic associates . Furthermore , since many insects serve as vectors for plant and animal pathogens [43] , it is conceivable that mutualistic associations arise as a consequence of the domestication of vectored pathogens . This hypothesis is compelling because such pathogens are not expected to negatively impact the fitness of their insect vectors [44] and under those circumstances the transition to a mutualistic lifestyle could be achieved without any need to attenuate virulence towards the insect host .
Strain HS was isolated on MacConkey agar at 35°C and 5% CO2 . 16S rRNA and groEL sequences were amplified from strain HS using universal primers . Following cloning of PCR products , eight clones were sequenced from each gene and consensus sequences were used in phylogenetic analyses . Sequence alignments were generated for 16S rRNA and groEL using MUSCLE [45] . PhyML [46] was then used to construct phylogenetic trees using the HKY85 [47] model of sequence evolution with 25 random starting trees and 100 bootstrap replicates . Synchronous cultures of Sitophilus oryzae and Sitophilus zeamais were reared on organic soft white wheat grains and corn kernels respectively , and maintained at 25°C with 70% relative humidity . Bacteriomes ( containing the bacterial endosymbionts SOPE and SZPE ) were isolated from 5th instar S . oryzae and S . zeamais larvae by dissection and homogenized at a sub-cellular level to release bacteria from host bacteriocyte cells; bacterial cells were then separated from host cells via centrifugation ( 2 , 000×g , 5 min ) . Total genomic DNA was then isolated from bacteria using the Qiagen DNeasy Blood & Tissue Kit ( Qiagen , Valencia , CA ) . Six mg of genomic DNA was hydrodynamically sheared in 5 mM Tris , 1 mM EDTA , 100 mM NaCl ( pH 8 ) buffer to a mean fragment size of 10 kb . The sample was washed and concentrated by ultrafiltration in a Centricon-100 ( Millipore , Billerica , MA ) and eluted in 250 µl of 2 mM Tris ( pH 8 ) . The fragments were end-repaired by treatment with T4 DNA polymerase ( New England Biolab , Beverly , MA ) to generate blunt ends . The DNA was then extracted with phenol/chloroform , ethanol precipitated , and 5′ phosphorylated with T4 polynucleotide kinase ( NEB ) . Ten mM of double-stranded , biotinylated oligonucleotide adaptors were blunt-end ligated onto the sheared genomic fragments at room temperature for 25 h using 10 , 000 cohesive end units of high concentration T4 DNA ligase ( NEB ) . Unligated adaptors were removed by ultrafiltration in a Centricon-100 . The adaptored fragments were bound to streptavidin-coated magnetic beads ( Invitrogen ) , and after binding and washing , the adaptored genomic fragments were eluted in 10 mM TE ( pH 8 ) . Fragments in the 9 . 5–11 . 5 kb size range were gel purified after separation on a 0 . 7% 1× TAE agarose gel , and the purified DNA was electroeluted from the agarose and desalted by ultrafiltration in a Centricon-100 . pWD42 vector ( GenBank: AF129072 . 1 ) was linearized by digestion with BamHI ( NEB ) at 37°C for 4 h , extracted with phenol/chloroform , ethanol precipitated and resuspended in 100 ml of 2 mM Tris ( pH 8 . 0 ) . Ten picomoles of double-stranded , biotinylated oligo adaptors were ligated onto the BamHI-digested vector at 25°C for 16 hrs using 4 , 000 units of T4 DNA ligase ( NEB ) . Unligated adaptors were removed by ultrafiltration in a Centricon-100 . The adaptored vector was bound to streptavidin-coated magnetic beads and the non-biotinylated adaptored vector was eluted in 10 mM TE ( pH 8 ) . One hundred ng each of adaptored vector and genomic DNA were annealed without ligase in 10 ml of T4 DNA ligase buffer ( NEB ) at 25°C for one hour . Two ml aliquots of the annealed vector/insert were transformed into 100 ml of XL-10 chemically competent E . coli cells ( Agilent Technologies , Santa Lara , CA ) and plated on LB agar plates containing 20 µg/ml ampicillin . A total of 23 , 808 bacterial colonies were picked into 96-well microtiter dishes containing 600 ml of terrific broth ( TB ) +20 µg/ml ampicillin and grown at 30°C for 16 h . Fifty ml aliquots were removed from the library cultures , mixed with 50 ml of 14% DMSO , and archived at −80°C . The 200 ml cultures were diluted 1∶4 in TB amp and runaway plasmid replication was induced at 42°C for 2 . 25 h . Plasmid DNA was purified by alkaline lysis , and cycle sequencing reactions were performed with forward and reverse sequencing primers using ABI BigDye v3 . 1 Terminator chemistry ( Applied Biosystems , Foster City , CA ) . The reactions were ethanol precipitated , resuspended in 15 ul of dH2O , and sequence ladders were resolved on an ABI 3730 capillary instrument prepared with POP-5 capillary gel matrix . Following elimination of any sequences encoding contaminating plasmid vector or host insect sequences , 38 , 755 shotgun reads were assembled using the Phusion assembler [48] using the paired-end sequences as mate-pair assembly constraints . Contig assemblies were viewed and edited in Consed [49] , and reads with high quality ( Phred>20 ) discrepancies were disassembled . After inspection and manual assembly to extend contigs , gaps were closed by iterative primer walking ( 895 primer walk sequence reads ) and gamma-delta transposon-mediated full-insert sequencing of plasmid clones ( 6 , 165 sequence reads across 103 transposed plasmid clones ) using an established protocol [50] . The average insert size of the plasmid library in the finished SOPE assembly was found to be 8 . 2 kb . The SOPE fosmid library was constructed using the Epicenter EpiFOS Fosmid Library Production Kit ( Epicentre Biotechnologies , Madison , WI ) , using SOPE total genomic DNA . 1 , 404 paired-end reads were generated from 702 fosmid inserts and mapped onto the assembly derived from the plasmid shotgun sequencing for validation ( Figure S2 ) . Strain HS genomic DNA was isolated from liquid culture using the Qiagen DNeasy Blood & Tissue Kit ( Qiagen , Valencia , CA ) . Five micrograms of total genomic DNA was used to construct a paired-end sequencing library using the Illumina paired-end sample preparation kit ( Illumina , Inc . San Diego , CA ) with a mean fragment size of 378 base pairs . This library was then sequenced on the Illumina GAIIx platform generating 26 , 891 , 485 paired-end reads of 55 bases in length . Paired-end reads were quality filtered using Galaxy [51] , [52] and low quality paired-end reads ( Phred<20 ) were discarded . The remaining 17 , 054 , 405 reads were then assembled using Velvet [53] with a k-mer value of 37 , with expected coverage of 119 and a coverage cutoff value of 0 . 296 . The resulting assembly consisted of 271 contigs with an N50 size of 231 , 573 and a total of 5 , 135 , 297 bases . No sequences were found to share significant sequence identity with genes encoding plasmid replication functions , suggesting that strain HS does not maintain any extrachromosomal elements . The assembled draft genome sequence of strain HS was annotated by automated ORF prediction using GeneMark . hmm [54] . The annotation was then adjusted manually in Artemis [55] using the published Sodalis glossinidius genome sequence [25] as a guide . ORFs were annotated as putatively functional only if ( i ) their size was ≥90% of the most closely related ORF derived from a free-living bacterium in the GenBank database , and ( ii ) they did not contain any frameshifting indel ( s ) . Curation of the strain HS genome sequence was performed in Artemis [55] . ORFs were classified into COG categories using the Cognitor software [56] . Syntenic links shown in Figure 2 were determined by pairwise nucleotide alignments between strain HS contigs and S . glossinidius ( GenBank: NC_007712 . 1 ) or the finished SOPE genome using the Smith-Waterman algorithm as implemented in the cross_match algorithm [49] . Figure 2 was prepared from data obtained from these alignments using CIRCOS [57] . The metrics depicted in Table 1 , Table 2 , and Figure 8 were computed from pairwise nucleotide sequence alignments of strain HS , S . glossinidius and SOPE ORFs using custom scripts . Candidate genes were classified as intact orthologs when their alignment spanned >99% of the HS ORF length ( or 90% for ORFs <300 nucleotides in size ) and did not contain frameshifting indels or premature stop codons . A simple Monte Carlo approach was implemented to simulate the evolution of pseudogenes in S . glossinidius and SOPE . The simulation facilitated the progressive accumulation of random mutations in all strain HS orthologs of both intact genes and pseudogenes identified in the current S . glossinidius or SOPE gene inventories . Mutations accumulated in proportion to ORF size in a randomly selected class of neutral genes of user-defined size over a defined number of mutational cycles . At preset cycle intervals , the simulation recorded ( i ) the difference in size between intact and disrupted sequences , ( ii ) the number of neutral genes that have accumulated one or more disrupting mutations , and ( iii ) the density of disrupting mutations , which was calculated based on the cumulative size of all neutral genes . The GenBank accession numbers for sequences used in Figure 1 are as follows: Endosymbiont of Circulio sikkimensis 16S rRNA , ( AB559929 . 1 ) , groEL , ( AB507719 ) ; Vibrio cholerae 16S rRNA , ( NC_002506 . 1 ) , groEL , ( NC_002506 . 1 ) ; Dickeya dadantii 16S rRNA ( CP002038 . 1 ) , groEL , ( CP002038 . 1 ) ; Escherichia coli 16S rRNA , ( NC_000913 . 2 ) , groEL , ( NC_000913 . 2 ) ; Candidatus Moranella endobia 16S rRNA , ( NC_015735 ) , groEL , ( NC_015735 ) ; Sodalis glossinidius 16S rRNA , ( NC_007712 . 1 ) , groEL , ( NC_007712 . 1 ) ; Yersinia pestis 16S rRNA , ( NC_008150 . 1 ) , groEL , ( NC_008150 . 1 ) ; Wigglesworthia glossinidia 16S rRNA , ( NC_004344 . 2 ) , groEL , ( NC_004344 . 2 ) ; Candidatus Blochmannia pennsylvanicus 16S rRNA , ( NC_007292 ) , groEL , ( NC_007292 ) ; Endosymbiont of Cantao ocellatus 16S rRNA , ( AB541010 ) , groEL , ( BAJ08314 ) ; Endosymbiont of Columbicola columbae 16S rRNA , ( AB303387 ) , groEL , ( JQ063388 ) ; Sitophilus zeamais primary endosymbiont 16S rRNA , ( AF548142 ) , groEL ( JX444567 ) ; Sitophilus oryzae primary endosymbiont 16S rRNA , ( AF548137 ) , groEL ( AF005236 ) ; Strain HS 16S rRNA , ( JX444565 ) , groEL ( JX444566 ) . The GenBank accession numbers for sequences used in Figure 4 are as follows: Strain HS Figure 4A ( JX444569 ) , Figure 4B ( JX444571 ) , Figure 4C ( JX444572 ) ; Sitophilus oryzae primary endosymbiont Figure 4A ( JX444568 ) , Figure 4B ( JX444570 ) , Figure 4C ( JX444573 ) .
|
Many insects harbor symbiotic bacteria that perform diverse functions within their hosts . However , the origins of these associations have been difficult to define . In this study we isolate a novel bacterium from a human infection and show that this bacterium is a close relative of the Sodalis-allied clade of insect symbionts . Comparative genomic analyses reveal that this organism maintains many genes that have been inactivated and lost independently in derived insect symbionts as a result of rapid genome degeneration . Our work also shows that recently derived Sodalis-allied symbionts maintain a significant population of “cryptic” pseudogenes that are assumed to have no beneficial function in the symbiosis but have not yet accumulated mutations that disrupt their translation . Taken together , our results show that genome degeneration proceeds rapidly following the onset of symbiosis . They also highlight the potential for diverse insect taxa to acquire closely related insect symbionts as a consequence of vectoring bacterial pathogens to plants and animals .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"organismal",
"evolution",
"genetic",
"mutation",
"genome",
"evolution",
"gene",
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"microbiology",
"sequence",
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"databases",
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"types",
"microbial",
"evolution",
"molecular",
"genetics",
"tsetse",
"fly",
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"complexity",
"comparative",
"genomics",
"biology",
"gene",
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] |
2012
|
A Novel Human-Infection-Derived Bacterium Provides Insights into the Evolutionary Origins of Mutualistic Insect–Bacterial Symbioses
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The Zcchc11 enzyme is implicated in microRNA ( miRNA ) regulation . It can uridylate let-7 precursors to decrease quantities of the mature miRNA in embryonic stem cell lines , suggested to mediate stem cell maintenance . It can uridylate mature miR-26 to relieve silencing activity without impacting miRNA content in cancer cell lines , suggested to mediate cytokine and growth factor expression . Broader roles of Zcchc11 in shaping or remodeling the miRNome or in directing biological or physiological processes remain entirely speculative . We generated Zcchc11-deficient mice to address these knowledge gaps . Zcchc11 deficiency had no impact on embryogenesis or fetal development , but it significantly decreased survival and growth immediately following birth , indicating a role for this enzyme in early postnatal fitness . Deep sequencing of small RNAs from neonatal livers revealed roles of this enzyme in miRNA sequence diversity . Zcchc11 deficiency diminished the lengths and terminal uridine frequencies for diverse mature miRNAs , but it had no influence on the quantities of any miRNAs . The expression of IGF-1 , a liver-derived protein essential to early growth and survival , was enhanced by Zcchc11 expression in vitro , and miRNA silencing of IGF-1 was alleviated by uridylation events observed to be Zcchc11-dependent in the neonatal liver . In neonatal mice , Zcchc11 deficiency significantly decreased IGF-1 mRNA in the liver and IGF-1 protein in the blood . We conclude that the Zcchc11-mediated terminal uridylation of mature miRNAs is pervasive and physiologically significant , especially important in the neonatal period for fostering IGF-1 expression and enhancing postnatal growth and survival . We propose that the miRNA 3′ terminus is a regulatory node upon which multiple enzymes converge to direct silencing activity and tune gene expression .
Non-canonical poly ( A ) polymerases ( PAPs ) comprise a family of enzymes highly conserved across Eukaryota and capable of catalyzing the template-independent transfer of uridines and adenines onto single-stranded RNA substrates [1] , [2] . Several non-canonical PAPs , including the uridyltransferase Zcchc11 ( PAPD3/TUT4 ) , can mediate 3′ terminal nucleotide additions to mature miRNA [3] , [4] , [5] . The uridylation or adenylation of mature miRNAs does not impact miRNA quantity , but instead limits miRNA silencing of select , targeted transcripts [3] , [5] . By other means , Zcchc11 can regulate quantities of mature miRNA . In mouse embryonic stem cell lines , Zcchc11 recognizes complexes of Lin28 and pre-let-7 and adds an oligouridine tail to the 3′ terminus of the pre-miRNA , preventing maturation and/or enhancing degradation of the precursor [6] , [7] . Knockdown of Zcchc11 or Lin28 in stem cell lines increases mature let-7 and decreases pluripotency markers [6] , [8] , [9] . Both of these models propose that uridylation by Zcchc11 circumvents miRNA-mediated transcript silencing , but they invoke disparate mechanisms . While not mutually exclusive , they have never been concurrently examined , and each has been demonstrated only in reductionist cell line systems focusing on small subsets of miRNAs . The degree to which miRNA quantity and/or sequence diversity may be remodeled by Zcchc11 ( or any non-canonical PAP ) and the roles of Zcchc11 in integrated biological systems remain speculative and represent major knowledge gaps .
We derived a line of mutant mice from embryonic stem cells carrying a gene-trap insertion in the fourth intron of the 31 exon Zcchc11 gene [10] . This mutation ( Figure S1A ) was upstream of all known protein domains and effectively ablated Zcchc11 expression ( Figure 1A and Figure S1B ) . Because Zcchc11 is necessary to limit let-7 in embryonic stem cell lines [6] , [7] , we anticipated that its loss might be incompatible with development and viability in utero . Surprisingly , there was no evidence of decreased fitness through gestation , with offspring from heterozygous parents born in Hardy-Weinberg equilibrium ( Figure 1B ) and displaying normal morphology and weights ( Figure 1C ) . Furthermore , we observed no increase in let-7 content in Zcchc11-deficient embryonic stem cells compared to embryonic stem cells that were wild type or heterozygous for Zcchc11 ( Figure 1D ) . Thus , Zcchc11-deficient embryonic stem cells were not disadvantaged , and Zcchc11 is dispensable for embryonic stem cell maintenance . By post-partum day 8 , we observed approximately 50% mortality in the Zcchc11-deficient mice ( Figure 1B ) . While initially similar in size , Zcchc11-deficient pups grew more slowly than wild type littermates in the first week ( Figure 1C ) . Most organs were proportionately affected , suggesting systemic effects ( Figure 1E ) . Early bodyweight differences were sustained for months ( Figure S1C ) , but otherwise those mutants surviving beyond the perinatal period displayed no defects in lifespan or gross or histologic morphology ( Figure S1D ) . In contrast to the more restricted tissue expression observed in adult mice [3] , Zcchc11 expression was found to be nearly ubiquitous in the first days after birth with protein levels in all organs examined except the thymus decreasing over time ( Figure 1F ) . These findings highlight the neonatal period as a time when Zcchc11 is fundamental to mouse growth and development . To address the predominant functions for Zcchc11 in miRNA biology in vivo , we used deep sequencing of small RNAs to simultaneously determine mature miRNA quantity and end-modification in an unbiased and comprehensive fashion . We focused our deep sequencing efforts on the liver because it has a relatively homogenous population of cells , Zcchc11 is strongly expressed in this tissue in young mice , and there was precedent for miRNA terminal modifications in the liver [4] . Libraries of small RNAs were constructed from 3 different paired sets of 8-day-old livers from Zcchc11+/+ or Zcchc11−/− sex- and littermate-matched mice . More than 85 million sequences were obtained , which exhibited a total of >75% alignment to miRBase ( Table S1 ) . Since Zcchc11-mediated uridylation of let-7 precursors in embryonic stem cells diminishes mature let-7 abundance [6] , [7] , we expected to observe increased quantities of these and perhaps other miRNAs in the Zcchc11-deficient livers . Instead , all detectable miRNAs exhibited a strong correlation in quantity between wild type and Zcchc11-deficient mice ( Figure 2A ) . There were no significant differences due to genotype in the content of any mature miRNA in the liver . Cross-method validation using qRT-PCR analyses of representative miRNAs of interest confirmed that levels of let-7a , let-7b , let-7c , miR-122 , miR-139 , and miR-379 were equivalent ( Figure 2B ) . A third independent method , northern blotting , also confirmed no increase in let-7a due to Zcchc11 deficiency ( Figure 2C ) . Consistent with this observation , there was no aberrant expression of core miRNA machinery , including Drosha , Dicer , and Argonaute in Zcchc11-deficient livers ( Figure 2D ) . Because mature miRNA levels were unchanged , we considered that Lin28 proteins might be absent from these tissues . While Lin28a was undetectable , Lin28b was present in young mouse livers and was unaffected by Zcchc11 deficiency ( Figure 2E ) . This may be relevant , since Lin28a is more specifically tied to Zcchc11 regulation of let-7 , relating to cytoplasmic localization [11] . Altogether , the present data suggest that the high level of Zcchc11 expression in the young liver does not influence mature miRNA abundance , including let-7 family members . In addition to precursor modification , Zcchc11 family members are capable of uridylating or adenylating the 3′ termini of mature miRNAs [3] , [4] , [5] , [12]_ENREF_13 . While the general pattern of small RNA read lengths from our deep sequence libraries was similar between genotypes , with sequence lengths of 21–23 nucleotides predominating , there were significantly fewer 23 nucleotide-long sequences in the livers of the Zcchc11-deficient mice ( Figure 3A ) . To identify potential enzymatic additions by Zcchc11 , aligned sequences were interrogated at the position 1 nucleotide beyond the 3′ terminal residue listed in miRBase [13] , [14] . Potential uridylation and adenylation events , included if present at levels of 1 or more sequence per 1 , 000 reads , were identified for many miRNA species in the livers of wild type mice ( Figure S2 ) . Some of these highly modified species , such as miR-26b and miR-122 , match those identified previously [4] , [5] , while others represent novel modification targets . We compared the levels of terminal adenines and uridines in mutant mice for each of the miRNAs that were so modified in the livers of wild type mice . A waterfall plot depicting the changes in terminal adenines between the livers of wild type and Zcchc11-deficient mice revealed a balanced distribution with a mean centered around 0 ( Figure 3B–3C ) . Conversely , the distribution of terminal uridines was significantly skewed , with most of the sequences bearing terminal uridines occurring less frequently in the absence of Zcchc11 , suggesting that miRNA uridylation was broadly decreased in Zcchc11-deficient livers ( Figure 3B–3C ) . The expression of other non-canonical PAPs , including PAPD5 , GLD-2 , and Zcchc6 , were unchanged in the livers of Zcchc11 deficient mice ( Figure 3D–3E ) . Terminal uridines that are 3′ of the expected mature miRNA sequence and dependent on Zcchc11 most likely represent enzymatic additions , but they could also arise from alternative processing of the pre-miRNA . To complement the above analyses , we analyzed miRNAs with terminal uridines that were not genomically encoded and therefore could only result from enzymatic addition , referred to as unambiguous uridylation events . For miRNAs observed in every library from both genotypes , 179 different species showed evidence of unambiguous uridine additions in all 3 wild type libraries , whereas only 118 did so across mutant libraries ( p<0 . 001 , χ2 test ) . This analysis supports the conclusions of the more comprehensive analyses above ( Figure 3 ) , together indicating definitively that Zcchc11 deficiency decreases the terminal uridylation of mature miRNAs . These results provide the first compelling evidence , to our knowledge , that Zcchc11 plays a specific and essential role in the length and uridylation of a broad swath of mature miRNAs in vivo . Of the miRNAs ending in uridine more frequently in wild type livers compared to mutants , many were predicted to target IGF-1 ( Figure 3B ) , a growth factor which is liver-derived and essential to early growth and survival in mammals [15] , [16] . The IGF-1 3′-UTR is highly polymorphic [17] . We found that an approximately 6 . 5 kb 3′-UTR was predominant in 8 day old mouse livers ( Figure 4A ) . We cloned this isoform from the livers of C57BL/6 mice and incorporated it into a reporter plasmid to assess the effect of Zcchc11 on the IGF-1 3′-UTR . Addition of this UTR significantly decreased reporter expression vs . the coding region alone ( Figure 4B ) , as would be expected for a long 3′-UTR that likely contains many negative regulatory elements . To test the effect of Zcchc11 expression on the IGF-1 3′-UTR , this reporter was co-transfected into cells along with enhanced GFP ( EGFP , control ) , wild type Zcchc11 , or Zcchc11 mutants lacking enzymatic activity . Overexpression of wild type Zcchc11 significantly increased levels of the IGF-1 3′-UTR reporter ( Figure 4C ) , indicating that Zcchc11 facilitates IGF-1 expression through its 3′-UTR . Importantly , a catalytically null mutant Zcchc11 , in which 2 aspartic acid residues necessary for uridyltransferase activity were changed to alanines [3] , was significantly less capable of amplifying the IGF-1 reporter ( Figure 4C ) . Moreover , an N-terminal deletion mutant of Zcchc11 lacking the C-terminal half of the protein and devoid of the uridyltransferase domain , PAP-associated domains , and RNA-binding zinc knuckles was completely incapable of altering IGF-1 expression ( Figure 4C ) . Thus , Zcchc11 can enhance IGF-1 expression through a uridyltransferase-dependent mechanism . Of the multiple miRNAs which had terminal uridines that required Zcchc11 in our deep sequencing datasets and were predicted to target the 3′ UTR of IGF-1 ( Figure 3B ) , we examined the ability of 4 ( miR-126-5p , miR-194-2-3p , miR-379 and Let-7d ) to suppress IGF-1 expression . Cells were co-transfected with the IGF-1 3′-UTR luciferase reporter construct along with either miRNA mimetics or a control non-targeting sequence . MiR-126-5p , miR-194-2-3p , and miR-379 , but not Let-7d , significantly silenced the IGF-1 reporter ( Figure 5A ) . We next assessed the influence of terminal uridine additions on the silencing activity of these miRNAs by comparing the effects on the IGF-1 reporter of unmodified miRNA mimetics to those with 2 uridines added onto the 3′ end . The uridylation of miR-126-5p or miR-379 significantly diminished IGF-1 silencing by these miRNAs , while uridylation of miR-194-2-3p had no effect ( Figure 5B ) . These data demonstrate that uridylation of specific miRNA species may influence silencing . Interestingly , varying the length of the terminal uridine tail , to reflect the different forms observed for each of these miRNAs in our deep sequencing datasets , had minimal impact for both miR-126-5p and miR-379 ( Figure 5C–5D ) , demonstrating that even a single uridine is sufficient to mitigate silencing by these miRNAs . Terminal uridylation did not completely eliminate silencing effects , suggesting that these end modifications provide tuning ability rather than a binary on-off switch . The effects of adding uridine ( s ) to the 3′ terminus were modest in comparison to the effects of altering bases in the seed region of the 5′ terminus , which completely eliminated repression of the IGF-1 3′-UTR reporter ( Figure 5E ) , indicating that the effects described here reflect a scaling of canonical miRNA activity . Interestingly , when 2 miRNAs targeting the IGF-1 3′-UTR were simultaneously co-transfected , silencing was minimally influenced if only 1 of the 2 was uridylated , but it was very effectively attenuated when both were uridylated ( Figure 5F ) . These data indicate that uridylation events have cumulative effects across the set of miRNAs targeting a given 3′-UTR . The combination of such effects provides a wide dynamic range over which expression can be tuned . The above results , demonstrating that Zcchc11 contributes to the uridylation of miRNAs which target IGF-1 and that miRNA uridylation relieves silencing to enhance IGF-1 expression , suggested the possibility that the decreased size and survival of Zcchc11-deificient mice may be associated with decreased expression of IGF-1 in vivo . Supporting this hypothesis , Zcchc11 deficiency reduced hepatic IGF-1 expression to approximately half of wild type levels ( Figure 6A ) . To differentiate regulation of the IGF-1 transcript in the liver from upstream signals , we examined STAT5 phosphorylation in the liver and growth hormone ( GH ) in the blood . Neither was affected by Zcchc11 deficiency ( Figure 6B–6C ) , suggesting a local hepatocyte role for this enzyme in regulating IGF-1 mRNA . To test whether IGF-1 expression was selectively enhanced or whether many diverse growth factors were dependent upon Zcchc11 in neonatal mouse livers , we performed a PCR array for mouse growth factor transcripts . Only IGF-1 was strongly expressed and diminished by Zcchc11 deficiency in these livers ( Figure 6D ) . The impact of Zcchc11 deficiency on IGF-1 was comparable to its impact on IL-6 , which was previously demonstrated to depend on Zcchc11 [3] . These data reveal that Zcchc11 effects are transcript-specific rather than transcriptome-wide , and that IGF-1 is a particular target of Zcchc11 regulation . We also measured the expression of histone H3 in these livers , since this protein contributes to cell proliferation and is enhanced by Zcchc11 in some but not other cell lines [18] , . There was no effect of Zcchc11 deficiency on histone H3 content in the young mouse liver ( Figure 6E ) . Reflecting the changes in liver transcript , Zcchc11 deficiency reduced circulating IGF-1 concentrations to about half of wild type levels ( Figure 6F ) , suggesting that diminished IGF-1 could contribute to systemic phenotypes . We conclude that Zcchc11 is essential to facilitating IGF-1 expression during neonatal periods .
The present communication reports , to our knowledge , the first studies of Zcchc11 in vivo . We find that Zcchc11 is widely expressed across multiple tissues shortly after birth and that in neonatal mice Zcchc11 deficiency results in a failure to thrive , associated with diminished IGF-1 expression . Mice with a complete IGF-1 deficiency have perinatal lethality and decreased growth rates [15] , [16] consistent with , but more severe than , the phenotypes observed in the Zcchc11-deficient mice . Like the Zcchc11-deficient mice , genetic engineering that reduces but does not eliminate IGF-1 signaling causes proportionally decreased growth [20] , [21] . Zcchc11 deficiency results in decreased uridylation of miRNAs in the liver , including miRNAs that target the IGF-1 3′-UTR . Gene expression tied to this 3′-UTR is enhanced by the uridylation of miRNAs or the increased expression of Zcchc11 . Altogether , we interpret these results as supporting a model in which the uridylation of miRNAs by Zcchc11 in the neonatal liver is essential for optimal IGF-1 expression and its promotion of growth and survival through the early postnatal period . However , Zcchc11 is a large and multifunctional protein , and we recognize that the abilities of Zcchc11 to uridylate other substrates [19] and to exert uridylation-independent activities [18] may additionally contribute to the complex phenotypes of Zcchc11-deficient mice . The decreased growth rate of Zcchc11-deficient mice is complementary to the increased growth rate observed in Lin28a-overexpressing transgenic mice [22] . Knockdown of Lin28 and Zcchc11 in cell lines results in similar phenotypes , and these proteins physically interact [6] , [7] , [8] . These similarities support the concept that Zcchc11 and Lin28 are involved in overlapping pathways in vivo . However , the suggestion that Zcchc11-Lin28 interactions may be essential for embryonic stem cell maintenance [6] , [7] , [8] is not supported by our observation that Zcchc11 deficiency does not impact growth or survival during embryogenesis . Like Zcchc11 , Lin28 proteins are particularly expressed in the tissues of young mice [23] . It will be of great interest to learn whether mice with deficiencies in Lin28a , Lin28b or both have phenotypes involving perinatal lethality and decreased growth , as observed with the Zcchc11-deficient mice . Untemplated uridines and adenines on mature miRNAs are consistent findings in deep sequencing analyses [5] , [24] , but the mechanisms and significance of such terminal additions have been difficult to discern . Previous in vitro studies had suggested that Zcchc11 is one of several enzymes capable of end-modifying mature miRNAs and that miRNA sequence variety might regulate transcript expression [3] , [12] . The only other mouse model of PAP mutation , mice deficient in PAPD4/GLD-2 , has no reported growth or survival phenotype [4] , [25] . Along with our data that other PAPs were expressed in the neonatal livers of the Zcchc11-deficient mice , these findings conclusively demonstrate that the different PAPs have unique and non-overlapping roles in vivo . Unlike GLD-2 , Zcchc11 is critical for thriving through the neonatal period , likely due in part to its ability to enhance hepatic IGF-1 expression . In embryonic stem cell lines , Zcchc11 knockdown increases let-7 levels , due to Zcchc11-mediated uridylation of precursors [6] , [7] . The Zcchc11-deficient mice allowed the examination of miRNA regulation in primary cells of living animals , and they revealed that Zcchc11 is not an essential determinant of mature let-7 or any mature miRNA quantity in the neonatal liver . Furthermore , insertional mutagenesis of Zcchc11 did not increase let-7 quantities in primary embryonic stem cells derived from these mice . The relationships among these terminal uridyltransferases ( Zcchc11 and Zcchc6 ) , Lin28 proteins ( a and b ) , and let-7 miRNAs in embryonic stem cells are complex and dynamic [26] , [27] . The present data show that Zcchc11 is not an absolute requirement for stem cell maintenance or low levels of let-7 . There may be other conditions in which precursor uridylation by Zcchc11 is essential to regulating let-7 , such as perhaps early embryogenesis when Lin28a is especially active . This mouse model will serve as a useful resource for determining if and when this uridyltransferase enzyme may influence miRNA biogenesis or content . In contrast to the unchanged abundance of miRNAs in the liver , mature miRNA lengths and sequences were altered by Zcchc11 deficiency . Mature miRNAs in the liver were longer and more likely to end in uridine , including untemplated uridines , when Zcchc11 was present . Thus , our data show that Zcchc11 functions to uridylate mature miRNAs in vivo . In addition to providing unprecedented evidence of an enzyme actively uridylating the 3′ terminus of miRNAs in vivo , these mice yield new insights into the scope of Zcchc11 modification of the miRNome . Rather than targeting only one individual miRNA , as has been previously documented for Zcchc11 and other PAPs [3] , [4] , [5] , we show here that Zcchc11 targets the 3′ terminus of multiple miRNAs . This broad substrate repertoire dramatically increases the potential targeting power of Zcchc11 . Importantly , most of the end-modifications observed varied by a small number of terminal nucleotides , and we observed that even a single uridine addition was sufficient to alleviate silencing activity . Such mono-uridylation by Zcchc11 appears to distinguish the effect of Zcchc11 on mature miRNAs from that described for pre-miRNAs , which is processive and results in a string of uridines being added [6] , [28] . Our data further expand our understanding of the molecular implications of miRNA uridylation by demonstrating that coordination of miRNA uridylation events across a 3′-UTR have combinatorial effects . The ability to adjust the silencing activity of many miRNAs targeting one transcript provides a wide dynamic range for enhancing gene expression . The exonuclease Nibbler was recently identified as capable of shortening miRNAs by removing terminal nucleotides [29] , [30] . Such enzymes may counter-balance the nucleotidyltransferase activities of PAPs like Zcchc11 . The abundance and remarkable stability of miRNAs suggest that mechanisms regulating miRNA activity are crucial , but they are only beginning to be elucidated [31] . We propose that the miRNA 3′ terminus functions as a critical regulatory node that is remodeled by diverse enzymes to adjust miRNA silencing and tune gene expression . The present results support this nascent paradigm by demonstrating essential in vivo roles of Zcchc11 in miRNome remodeling and postnatal development . Zcchc11 mediates mature miRNA uridylation , facilitates hepatic IGF-1 expression , and enhances growth and survival through the neonatal period .
A mouse embryonic stem ( ES ) cell line ( RRR277 ) containing a gene-trap insertion in the Zcchc11 gene was obtained from BayGenomics at the Mutant Mouse Regional Resource Center at University of California-Davis [10] . C57BL/6 blastocysts were microinjected with mutant ES cells to create chimeric mice that were subsequently backcrossed onto a C57BL/6 genetic background for at least 10 generations . The gene-trap genomic insertion site was located within intron 4 of Zcchc11 , generating a fusion protein containing the first 314 amino acids of Zcchc11 joined in frame with the β-galactosidase reporter . All known conserved protein motifs and domains in Zcchc11 are downstream of this site . The mutant allele was detected by genomic PCR using the primers listed in Table S2 . All murine studies were performed under approval of the Boston University School of Medicine IACUC . Eight-cell stage mouse embryos were collected in M2 medium ( Millipore ) from superovulated Zcchc11+/+ , Zcchc11+/− , or Zcchc11−/− females mated with Zcchc11+/+ or Zcchc11+/− males . Embryos were cultured to the early blastocyst stage in KSOM ( Millipore ) supplemented with 2i ( 1 µM PD0325901 and 3 µM CHIR99021 ( Cayman Chemical ) ) , followed by 48 hours of culture in Neurobasal medium supplemented with N2 , B27 ( Invitrogen ) 2i and Leukemia Inhibitory Factor ( LIF , Millipore ) at 37°C , under 5% CO2 . The resulting expanded blastocysts were cultured on laminin ( Sigma , 10 µg/ml ) coated tissue culture plastic in N2B27+2i+LIF until the blastocysts had attached and outgrowths were visible ( 3–4 days ) . These ES outgrowths were recovered by mouth pipette , disaggregated to single cells with 0 . 25% Trypsin ( Invitrogen ) and plated on laminin-coated tissue culture plastic to establish embryonic stem cell lines . Cell line genotype was determined by amplifying wild type and mutant products of Zcchc11 alleles . H1299 cells were plated at 2 . 5×106 cells/well in 6-well plates and transfected with 0 . 5 µg reporter construct , 0 . 25 µg phRLTK control reporter ( Promega ) and 200 nM miRNA mimetic using 4 µl/well Lipofectamine 2000 ( Invitrogen ) . EGFP or Zcchc11 constructs were transfected at 3 µg/well . Luciferase was measured using the Dual Luciferase Reporter Assay System ( Promega ) . Mimetics were duplexed siRNA ( Dharmacon ) ; sequences are presented in Table S2 . Liver growth factors were measured by PCR Array ( SA Biosciences ) using 1 µg RNA pooled from 4 Zcchc11+/+ or 4 Zcchc11−/− mice . IGF-1 and 18S rRNA qRT-PCR was performed using the TaqMan RNA-to-Ct kit ( Applied Biosystems ) with primers and probes shown in Table S2 . Small RNAs were reverse transcribed using the TaqMan miRNA assay system ( Applied Biosystems ) . Northern blot analysis was performed on 15 µg of total RNA electrophoresed through a 1% agarose-formaldehyde gel and immobilized on a BrightStar-Plus nylon membrane . For probe creation , IGF-1 cDNA ( Open Biosystems ) was digested out of a pCMV-Sport6 vector using XbaI and SalI restriction enzymes ( NEB ) . GAPDH probes were purchased ( SA Biosciences ) . Antibodies were obtained from Cell Signaling Technologies except goat anti-Zcchc11 ( ProSci ) , Rabbit anti-Goat ( R&D ) , Rabbit anti-GLD-2 ( Abgent ) , and Rabbit anti-PAPD5 ( Genetex ) . Whole blood was collected from the hepatic vein of 10-week old mice or by cardiac puncture from 8-day-old litters of Zcchc11+/− breeding pairs; serum was separated and components were measured using mouse IGF-1 and GH ELISA Kits ( R&D Systems ) . All Zcchc11 overexpression studies were performed using the same plasmid backbone as the control Enhanced GFP ( pEGFP-N , Clontech ) . Creation of the Zcchc11 and DADA mutant plasmids has been previously described [3] . The N-terminal region of Zcchc11 was PCR amplified from the Zcchc11 plasmids using Phusion High Fidelity DNA Polymerase ( NEB ) with the primers indicated in Table S2 . The resulting product was ligated between NotI and BsrGI sites in the full length Zcchc11 plasmid . The full-length IGF-1 3′ UTR ( Accession # NM_001111274 . 1 ) was amplified from a cDNA library of whole liver RNA from 8 day-old C57BL/6 mice with Herculase II DNA polymerase ( Agilent ) using primers shown in Table S2 . The resulting band was ligated between SacI and MluI restriction sites into the pMir-Report expression vector ( Ambion ) . All plasmid constructs were sequenced . Livers from 8 day-old mice were stored in RNA later ( Qiagen ) . Half of each liver was homogenized in BioPure RNA isolation reagent ( BiooScientific ) with 0 . 5 mm zirconium oxide beads using a bullet blender ( Next Advance ) . RNA <30 nt long was purified from phenol-chloroform extracted RNA using a FlashPAGE fractionator ( Ambion ) . Small RNA library creation was performed using an adapted Illumina small RNA sample prep v1 . 5 . 0 . Briefly , small RNA samples were adaptor ligated with one of four different 3′ adaptors using T4 RNA Ligase 2 ( NEB ) to allow multiplex sequencing of Zcchc11+/+ and Zcchc11−/− samples . A conserved 5′ adaptor was added using truncated T4 RNA Ligase 2 . Samples were gel extracted on a 10% TBE-Urea gel to remove adaptor only ligation products . cDNA was created using SuperScript III Reverse Transcriptase ( Invitrogen ) then PCR amplified with GoTaq DNA Polymerase ( Promega ) . Adaptor and primer sequences are shown in Table S2 . The resulting library was sequenced on an Illumina Genome Analyzer IIx . In total , three libraries were created each containing RNA from two Zcchc11+/+ and two Zcchc11−/− sex matched littermates . Libraries were uploaded to the NCBI Sequence Reads Archive ( Accession Number: SRA059070 ) . All library analyses were performed using the Genomic Workbench software platform ( CLC Biosystems ) . First , sequences were sorted by adaptor barcode and perfectly matched adaptor sequences were trimmed while ambiguous/unrecognizable adaptor sequences were discarded . The samples were then grouped , counted and aligned to v16 . 0 of miRBase [13] , [14] allowing no more than 2 internal mismatches and no more than 5 at the 3′ end of the sequence . For identification of sequence modifications the libraries were aligned to the mouse genome annotated with v9 . 0 of the mouse database from the UCSC genome browser . SNP detection was performed using the neighborhood quality score algorithm [32] to identify sequence variants occurring at greater than one per 1 , 000 sequences . Full genome alignment was used to empower the use of quality scores ( average quality score of 15 , minimum central quality of 20 , with a window length of 5 ) for SNP detection . SNP position alignment data was exported for further analysis and adenylation/uridylation was defined as A or T variants occurring at the position one nucleotide beyond the published 3′ terminal end of the miRNA . Unambiguous uridylation events were defined as substitutions of a non-genomic U for any other nucleotide at the 3′ terminal end of miRNA .
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MicroRNAs ( miRNAs ) are molecules that regulate gene expression , usually serving silencing functions . Mechanisms regulating miRNAs are poorly understood . In test tube experiments , the enzyme Zcchc11 adds uridines to the ends of miRNAs and their precursors , with uridyation of miRNA precursors decreasing the quantities of mature miRNAs and uridylation of mature miRNAs decreasing their silencing activity . Whether , when , and to what effect Zcchc11 alters miRNA in living animals has never previously been reported . To understand functions of Zcchc11 in integrative biology , we generated mice deficient in Zcchc11 . Mutant mice were born normally , but some died soon after birth and survivors grew poorly . No miRNA quantities were changed in tissues sampled from these mice , but mature miRNAs were less likely to have additional uridines on their ends . Some miRNAs that were uridylated by Zcchc11 targeted a critical growth factor known as insulin-like growth factor 1 ( IGF-1 ) , but they did so less effectively when uridylated . Zcchc11-deficient mice had decreased amounts of IGF-1 in the liver and blood . These data reveal that Zcchc11 is an important enzyme in living animals for uridylating mature miRNAs , enhancing IGF-1 expression , and promoting neonatal growth and survival , suggesting a novel mode of gene regulation that is biologically significant .
|
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2012
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Zcchc11 Uridylates Mature miRNAs to Enhance Neonatal IGF-1 Expression, Growth, and Survival
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Demonstrations of both pro-apoptotic and pro-survival abilities of Fas ( TNFRSF6/CD95/APO-1 ) have led to a shift from the exclusive “Fas apoptosis” to “Fas multisignals” paradigm and the acceptance that Fas-related therapies face a major challenge , as it remains unclear what determines the mode of Fas signaling . Through protein evolution analysis , which reveals unconventional substitutions of Fas tyrosine during divergent evolution , evolution-guided tyrosine-phosphorylated Fas proxy , and site-specific phosphorylation detection , we show that the Fas signaling outcome is determined by the tyrosine phosphorylation status of its death domain . The phosphorylation dominantly turns off the Fas-mediated apoptotic signal , while turning on the pro-survival signal . We show that while phosphorylations at Y232 and Y291 share some common functions , their contributions to Fas signaling differ at several levels . The findings that Fas tyrosine phosphorylation is regulated by Src family kinases ( SFKs ) and the phosphatase SHP-1 and that Y291 phosphorylation primes clathrin-dependent Fas endocytosis , which contributes to Fas pro-survival signaling , reveals for the first time the mechanistic link between SFK/SHP-1-dependent Fas tyrosine phosphorylation , internalization route , and signaling choice . We also demonstrate that levels of phosphorylated Y232 and Y291 differ among human cancer types and differentially respond to anticancer therapy , suggesting context-dependent involvement of Fas phosphorylation in cancer . This report provides a new insight into the control of TNF receptor multisignaling by receptor phosphorylation and its implication in cancer biology , which brings us a step closer to overcoming the challenge in handling Fas signaling in treatments of cancer as well as other pathologies such as autoimmune and degenerative diseases .
Fas ( CD95/APO-1/TNFRSF6 ) , a tumor necrosis factor ( TNF ) receptor superfamily member , is a well-known apoptosis activator . The binding with Fas ligand ( FasL ) can lead to the recruitment of Fas-associated protein with death domain ( FADD ) and procaspase-8 , forming the death-inducing signaling complex ( DISC ) . This results in the activation of the caspase cascade and , ultimately , apoptosis [1] . Fas was mainly considered as a tumor suppressor thanks to its familiar ability to promote programmed cell death ( apoptosis ) . However , accumulating evidence supports a significant role of Fas in the alternative non-death signaling leading to cell survival , proliferation , motility , epithelial-mesenchymal transition , cancer growth , and metastasis in some contexts [2] . While such conditional multisignaling of Fas has also been well demonstrated in several cancer models , including colon cancer [3–5] , the mechanism controlling these multisignals is unclear . Fas multiple signaling implies an efficient molecular switch mechanism that lends itself to a flexible formation of different signaling complexes depending on the type of signal being transmitted . One of such mechanisms to be considered is tyrosine phosphorylation . While it has been shown for almost two decades that both tyrosines in the intracellular domain , Y232 and Y291 , can be phosphorylated [6] , the role of their phosphorylation is not understood . Due to the lack of tools for functional analysis and site-specific phospho-tyrosine ( pY ) detection , there have been only few conflicting reports that infer functions of Fas phosphorylation [7–8] and , thus , limiting our understanding of the functions of each pY in Fas signaling . Taking a unique approach based on Fas protein evolution analysis , evolution-guided Fas pY proxy , and site-specific detection of Fas pY , we show for the first time that Fas death domain tyrosine phosphorylation is a dominant anti-apoptosis and a pro-survival mechanism . We discover that while phosphorylations at Y232 and Y291 share the anti-apoptotic function , they differ in terms of structural requirement and other functions . We also reveal the regulation of Fas pY by SFK/SHP-1-based system and the functions of death domain pY in the control of DISC formation and clathrin-dependent Fas endocytosis . Furthermore , we present the implication of the pY-based regulation of Fas signaling in different human cancers along with potential means to predict Fas signaling modes , which is crucial for Fas-related therapeutic design to achieve clinical success .
To date , the active role of each pY of the Fas death domain in apoptosis induction by FasL in the human cell system is unknown . To approach this issue , we turned toward the evolution of Fas protein as a guide by considering the substitutions of amino acids at these phosphorylation sites during the course of evolution . Multiple sequence alignment of Fas proteins from vertebrates illustrates that the side chain size and aromatic ring feature are highly conserved at position 232 . This , however , is not the case for position 291 , where neither the size nor the aromatic side chain of tyrosine is a substitution criterion ( Fig 1A; positions Y232 and Y291 of human Fas are used as references to indicate corresponding amino acid positions in other species throughout the text ) . Notably , substitution of Y by a small amino acid , cysteine ( C ) , is common among primates ( particularly in old world monkeys ) and rodents , which are relatively close to hominoids ( apes , including human ) . Further in evolutional distance , one can also observe the substitution of Y291 by a small amino acid , alanine ( A ) , in some fishes , including coelacanth ( the living fossil ) and cod . Small amino acid substitutions for Y291 in closely related species do not appear to impact the apoptotic functions of Fas . Previous work has shown that , like in human and mouse , Fas in cynomolgus monkey and rat that carries a C at position 291 could signal apoptosis upon ligation with an agonistic antibody [10] and FasL [8 , 11] , respectively . The observation that Y at the position 291 is interchangeable with C among closely related species whose Fas can function as an apoptosis inducer suggests that the presence of Y at this position and , thus , its phosphorylation is not essential for Fas apoptotic signal . Our observation that in several human cell types unphosphorylated mutants ( Y232F and Y291F ) could transmit apoptotic signals supports this conclusion ( Figs 1B , S1 and S2 ) . The above-mentioned results led us to hypothesize that the advantage of Fas death domain pY , if it occurred through evolution , was to provide a reversible switch from the apoptotic signal to other signals , e . g . , the survival signal . However , to clarify whether Fas pY plays active roles in cellular processes , an ability to induce and maintain , or mimic the properties of , the phosphorylated state of amino acid residues of interest in cells is required . A comparative genomic study of Raf kinases shows that pY could have evolved from smaller acidic amino acids such as aspartic acid ( D ) or glutamic acid ( E ) [12] . Thus , substituting D or E for pY may mimic the phosphorylated state of some proteins [13–16] . However , such substitutions require careful consideration to ensure that the observed results are not due to the change in the amino acid size . The common substitution between small amino acids and Y at position 291 , but not at position 232 , of Fas in vertebrates suggests that , depending on the sites , net charge can be more important than the details of the side chain structure . To investigate this issue , we performed evolution-guided , site-directed mutagenesis to examine the functional effects of the following amino acid substitutions on Fas: 1 . Size reduction by ( a ) substituting Y232 with C , as observed in reptile and in cases of human autoimmune lymphoproliferative syndrome ( ALPS ) [17] , ( b ) substituting Y291 with A and C , as observed in fish and mammals respectively; and 2 . negative charge addition by substituting Y232 and Y291 with the acidic D , which has a size comparable to C , the most common small amino acid substituting for Y in evolution of Fas . The features of amino acids used in the site-directed mutagenesis are summarized in S3 Fig . We observed that introducing a negative charge by Y232D mutation rescued cells from FasL-induced cell death ( Fig 2A and 2B ) . However , side chain size reduction by Y232C mutation also , to a lesser extent , rescued the cells , suggesting the importance of side chain size and aromatic ring of Y232 in Fas signaling . This is in accord with the high conservation of aromatic amino acids at this position in vertebrates ( Fig 1A ) . Since the complete rescue observed in Y232D-carrying cells could be a combined effect of the added negative charge and reduced side chain size , substituting a small acidic amino acid for pY ( as a single measure ) may not provide an adequate proxy for functional studies of phospho-Y232 ( pY232 ) . However , the situation differed for the 291 position where , similar to Y291F , size-reduction substitutions ( Y291A and Y291C ) had no impact on the apoptotic function ( Fig 2C and 2D ) and the formation of the DISC of Fas ( Fig 2E ) . In contrast , the negative-charge substitution , Y291D , completely abolished FasL-induced apoptosis and DISC formation ( Fig 2C and 2E ) . This indicates that the abolition of Fas apoptotic signaling was due to the negative charge of aspartic acid but not to its small size . Of note is that while Y291A mutation did not impact the apoptotic function of Fas , we observed an increase in cleaved fragments of caspase 8 in the DISC from stimulated Y291A cell lysate . This phenomenon did not cause any spontaneous cell death ( Fig 2C ) and could be related to non-apoptotic function of caspase 8 , as it is now well-known that the death-effector domains ( DEDs ) containing proteins , including caspases , not only regulate apoptosis but also other forms of cell death , including necroptosis , as well as other important cellular processes such as autophagy and inflammation ( see review [18] ) . Further studies are required to explore the roles and effects of basal activation of caspase 8 in Y291A-containing cells . Overall , these results demonstrate that pY291 and the aromatic side chain at this position are dispensable for cell death signaling and that the net charge at this site is more important for the protein's function than the detail of the side chain structure . Having established that Y291D mutation emulated the negative charge of pY291 independently of the side chain size reduction and loss of aromatic ring , we used the mutant as a proxy to examine how adding negative charge to this site by phosphorylation could modulate Fas signaling . To further demonstrate the anti-apoptotic effect exerted by the negatively charged 291 residue in the Fas signaling , we introduced Y291D mutant Fas ( death-off ) into cells that stably expressed wild-type , Y232F , or Y291F mutant Fas ( death-on ) whose apoptotic capacity had been established ( Fig 2 ) . We found that the expression of Y291D Fas exhibited a clear dominant-negative effect on all “death-on” Fas species investigated , reducing the level of dead cells by approximately 50% ( Fig 3A ) . In line with these data , when we introduced the “death-on” Fas species to cells stably expressing Y291D , a clear reduction in apoptosis-inducing capacity of the “death-on” Fas was observed when compared to cells expressing the control vector ( Fig 3B ) . The data suggest that a subset of Fas that is phosphorylated can extend its inhibitory effect to an unphosphorylated Fas population , rendering it inefficient in inducing apoptosis . Since both Y232 and Y291 can be phosphorylated , we examined whether phosphorylation of both tyrosines is required to turn off the death signal . Double mutations of Y232 and Y291 in SW480 and SW620 cells ( Fig 3C and 3D ) showed that negative-charge mutation at the 291 position alone was sufficient to completely block Fas-mediated cell death . The cell death inhibition in Y232F/Y291D mutant cells shows that maintaining unphosphorylated Y232 could not override the cell death blockage by Y291D mutation . This implies that FasL-induced apoptosis is rendered possible only when Y291 is dephosphorylated and that pY291 exerts dominant-negative effect on this apoptotic process . While it was evident that dephosphorylation at both Y232 and Y291 ( Y232F/Y291F ) allowed efficient FasL-induced apoptosis ( Fig 3C and 3D ) , it was unclear whether double tyrosine dephosphorylation was essential for this signal or if single dephosphorylation at Y291 sufficed for the apoptotic signal to proceed . While Y232D mutation partially presented the anti-apoptotic effect of side chain size reduction , the additional anti-apoptotic effect of negative charge at 232 site could still be observed ( Fig 2A and 2B ) . Thus , the observation that FasL-induced cell death in cells carrying Y232D/Y291F mutants remained completely blocked ( Fig 3C and 3D ) points toward the likelihood that pY232 also exerts dominant-negative effect on FasL-induced apoptosis . That the single dephosphorylation at 291 residue could not override the anti-apoptotic effect of negative-charge addition at 232 residue suggests that double dephosphorylation at both Y232 and Y291 is required for FasL-induced apoptosis . Fig 3E depicts some dominant-negative scenarios in which: 1 . an intramolecular dominant-negative effect on apoptosis occurs in a Fas molecule when at least one of the death domain tyrosines is phosphorylated; and 2 . an intermolecular dominant-negative effect occurs when Fas molecules carrying at least one death domain pY dominant-negatively suppress the apoptotic function of Fas molecules in the pro-apoptotic state ( i . e . , having both death domain tyrosines dephosphorylated ) . The 291YDTL motif of Fas has been suggested as a putative tyrosine ( Y ) -based sorting motif ( Yxxϕ; ϕ , a bulky amino acid; x , any amino acid ) for clathrin-dependent endocytosis ( CDE ) [19] . The Y in the motif is essential for binding to μ2 subunit of AP-2 and , in most cases , cannot be substituted by other aromatic acid residues or pY ( review [20] ) . Our data demonstrated that neither Y291D nor Y291F mutation impaired FasL internalization ( Fig 4A ) . Moreover , Y291D Fas expression resulted in a more efficient FasL uptake , indicating that the added negative charge favored the process and , thus , raising doubt regarding the function of Y291 as a critical component of a Y-based sorting motif for CDE . To address these issues , we investigated the involvement of CDE in FasL/Fas uptake . Our synchronized internalization study by immunofluorescence confirmed that neither Y291D nor Y291F inhibited FasL uptake upon its engagement and that the uptake was more efficient with Y291D mutant ( Fig 4B ) . The rapid FasL uptake ( within 10 minutes of activation ) was accompanied by the transport of a population of Fas to the perinuclear region ( Fig 4B ) . In cells carrying wild type and Y291F Fas , the inhibition of CDE by overexpression of a truncated form of AP180 protein ( AP180-C ) , which blocks the recruitment of clathrin to the plasma membrane [21] , caused only a small delay in FasL uptake , which was completed by 30 min of activation . This indicated that the inhibition of Fas/FasL uptake by CDE could be compensated by an alternative pathway , as we previously reported [22] . In contrast , AP180-C expression in cells expressing Y291D Fas strongly inhibited the FasL/Fas complex uptake along with the transport of Y291D Fas to the perinuclear region . Similarly , disrupting dynamin-dependent endocytosis by dynasore , a potent inhibitor to dynamin GTPase activity , led to a strong reduction of FasL/Fas uptake in cells carrying Y291D Fas but not in those carrying wild-type or Y291F Fas ( Fig 4C ) . These data demonstrate that FasL uptake and perinuclear transport of Y291D Fas relied on dynamin-dependent CDE . Unlike in the case of wild-type and Y291F Fas , in which dynamin-independent , clathrin-independent endocytosis ( CIE ) could compensate for the CDE blockage , the negatively charged Y291D committed the internalization of Fas to CDE . This implies that the constitutive pY291 engages Fas trafficking to CDE exclusively , hence the loss of the flexibility to carryout FasL-activated trafficking via compensatory CIE processes . Our observation that Y291D even promoted CDE in these cells suggests that Y291 may participate in CDE as a part of another sorting motif . We analyzed amino acids flanking Y291 and found the similarity between the acidic dileucine ( LL ) sorting motif ( D/E ) xxxL ( L/I ) and Fas sequence 289EAYDTLI295 . It is possible that the negative charge of Y291D mutation promoted the interaction between Fas and CDE adaptor proteins that binds the LL motif , such as AP2 . Coimmunoprecipitation showed that overexpression of Y291D Fas , but not wild-type or Y291F Fas , increased the association of Fas with AP2 ( Fig 4D ) , suggesting that the negative charge of pY291 may promote the sorting function of the LL motif . This is in line with the importance of phosphorylation in the LL motif in the internalization process , which has been previously reported [23–27] . That inhibiting dynamin-dependent CDE with dynasore sensitized cells to FasL-induced cell death ( Fig 4E ) also suggests that the function of pY291 in dynamin-dependent CDE contributed to the pro-survival signal of Fas . The contribution of Fas signaling to colorectal cell proliferation was demonstrated as Fas knockdown by siRNA led to a decrease in BrdU incorporation ( Fig 5A ) and sublethal doses of FasL increased viability of the cells ( S6A Fig ) . To determine the role of Y232 and Y291 phosphorylation in FasL-induced proliferation by site-directed mutagenesis while minimizing the interference from endogenous Fas in SW480 cells , we used stable SW480 cell lines overexpressing Fas proteins that carried silent mutations in the region targeted by an siRNA against Fas . This was to allow the reduction of background signals from endogenous Fas while maintaining that of overexpressed Fas . In cells treated by control siRNA , the FasL-induced proliferation depended on Fas pY , since abolition of tyrosine phosphorylation by the expression of Y232F and Y291F Fas reduced BrdU incorporation , while mimicking the negative charge of pY by Y291D Fas expression did not ( Fig 5B ) . The specific effects of Fas pY mutations in proliferation were confirmed in cells treated with Fas siRNA to reduce background proliferative signals from endogenous Fas . Following Fas siRNA treatment , the FasL-induced proliferation was reduced in control cells , while it increased significantly in cells carrying Y291D mutation , suggesting an active role of pY291 in a proliferative signal of Fas . As found for cells not treated with Fas siRNA , FasL-induced proliferation decreased in cell carrying Y232F and Y291F mutation that were subjected to Fas siRNA treatment , demonstrating a strong inhibitory role of Fas Y232 and Y291 dephosphorylation in FasL-induced proliferation . The importance of pY291 in FasL-induced proliferation was also supported by our observation that the expression of Y291D Fas led to an increase in viability when cells were treated with sublethal doses of FasL , while the expression of Y291F produced the opposite effect ( S6B and S6C Fig ) . In addition to promoting FasL-induced proliferation , phosphorylation of death domain tyrosine also promoted FasL-induced cell migration . Using Boyden chamber migration assay , we found that cells that overexpressed Y291D Fas mutant exhibited increased migration ability induced by FasL when compared to control cells and cells that expressed unphosphorylable mutants ( Fig 5C ) . Based on previously suggested involvement of SFKs in Fas signaling [11 , 28–29] , we examined their influence on FasL-induced cell death . Inhibiting SFKs by a Src family kinase inhibitor , PP2 , sensitized cells to FasL-induced cell death ( Fig 6A ) . On the other hand , PP2 also significantly reduces FasL-induced proliferation ( Fig 6B ) . The potentiation of FasL-induced cell death and the inhibition of FasL-induced proliferation by PP2 implied the role of SFKs in the phosphorylation of Fas death domain tyrosines . To further identify the SFKs involved , we subjected the cells to siRNA against Src and Yes-1 and found that suppressing either Src or Yes-1 could somewhat reduce the levels of pY232 and pY291 Fas ( Fig 6C ) . However , the effect was more pronounced when both Src and Yes-1 were simultaneously suppressed . This reflects the redundancy of SFK activities in the Fas tyrosine phosphorylation process , since suppression of Src or Yes-1 alone was not as efficient as suppressing both kinases in reducing pY232 and pY291 Fas . The functional redundancy among SFKs is well recognized [30] , and this notion is also supported by our observation that overexpression of either Src or Yes-1 could increase the level of pY232 and pY291 Fas ( S12 Fig ) . The protein tyrosine phosphatase , SHP-1 , has been implicated in Fas signaling [31] . Therefore , we investigated its involvement in Fas tyrosine phosphorylation process . We found that inhibiting SHP-1 activity by protein tyrosine phosphatase inhibitor I ( PTPiI ) protected the cells from FasL-induced cell death ( Fig 6D ) while promoting FasL-induced proliferation ( Fig 6E ) . This implied that SHP-1 might negatively regulate the phosphorylation of Fas death domain tyrosines . We further confirmed the role of SHP-1 in Fas dephosphorylation by demonstrating that overexpressing SHP-1 protein decreased pY232 and pY291 levels ( Fig 6F ) while suppressing SHP-1 expression by siRNA resulted in the opposite effect ( Fig 6G ) . By comparing several colon cell lines , we found that the relative Fas pY levels tended to increase with the cancer progression ( Fig 7A and 7B ) , implying that Fas pY might correlate to some contexts of human cancers . We therefore examined the relative levels of pY232 and pY291 in malignant tissues when compared to corresponding normal tissues of patients diagnosed with different types of cancers . We found that most patients having cancer of the colon , breast , or ovary also had an increased level of pY232 and/or pY291 . However , this was not the case in patients having cancer of the cervix or lungs ( Fig 7C ) . These diverse Fas pY profiles in different cancer types suggests that Fas signaling modes may be cancer type-dependent . Additional evidence supporting the involvement of Fas pY in human cancer comes from our observation that pY291 Fas levels decreased while pY232 Fas levels increased in the majority of rectal tumors after radiotherapy ( ± concurrent chemotherapy , Fig 7D ) , suggesting distinct regulation and functions of pY232 and pY291 in Fas signaling in rectal cancer in response to cancer therapy .
The functions of different phospho-tyrosines of Fas have not been distinguished to date . Using comparative genomics to guide functional analysis of Fas pY , in conjunction with site-specific detection of the phosphorylated death domain tyrosines , Y232 and Y291 , we show that the phosphorylation of both death domain tyrosines in human Fas is dispensable for FasL-induced apoptosis . Our findings in human colorectal cells and B-cells that demonstrate this claim are well supported by evolution data and functional data from other animal cell models , including macaques [10] and rats [8 , 11] . Guided by comparative genomics , which reveals an unconventional cysteine substitution for Y291 in primates and rodents , we show that the net charge at this site is more important for the protein's function than the size or details of the amino acid side chain . This has created the possibility of using acidic amino acid substitution as a proxy for pY291 and of demonstrating that pY291 is , rather , a pro-survival mechanism that confers apoptosis resistance and proliferative advantage while its dephosphorylation permits apoptotic process . The substitution of C for Y at 291 residue in old world monkeys is unique among the three Fas tyrosines found in primates . It suggests a low structural requirement from this tyrosine and that its role is , rather , in functional specificity . This amino acid exchange in Fas orthologs in closely related species may appear surprising and drastic considering amino acid sizes and properties . However , it serves as an example that , for certain protein functions , Y can be exchanged for a small , non-aromatic amino acid and that such Y ( or pY ) may have evolved from a smaller amino acid , as previously demonstrated [12] . Our finding that small amino acid substitutions occur at pY sites of Caspase 8 and are common in FAP-1 ( S7 and S8 Figs ) supports this point of view . The shift from small amino acids to ( p ) Y of Fas and other proteins in the Fas signaling pathway in primates implies a preferential shift to pY switch systems that can confer their functional plasticity and specificity in these species . We show that pY232 and pY291 of Fas have common features . They ( 1 ) are dispensable for Fas-induced apoptosis and ( 2 ) dominant-negatively inhibit apoptosis . Using the proxy Y291D , we also show that pY291 can promote FasL-induced cell proliferation and , thus , present pY291 as a reversible anti-apoptotic/pro-survival switch of Fas . This switch mechanism involves the function of pY291 in preventing FasL-induced DISC formation ( Fig 2E ) and promoting CDE of Fas ( Fig 4 ) . Previous work showed that the Y291F mutation of human Fas ( hFas ) in murine cells inhibited the downregulation of the hFas-antibody complex [19] . Since the 291YDTL motif is consistent with the Y-based sorting motif for CDE , one may infer that this decrease in the antibody-induced Fas downregulation was caused by Y → F substitution in the motif . However , we found that , in human cells , Y291F mutation did not inhibit the FasL uptake ( Fig 4 ) . Likewise , Y283F mutation of murine Fas ( corresponding to Y291F in hFas ) in murine T cells did not inhibit the downregulation of the surface Fas-FasL complex ( S9 Fig ) . Additionally , using the Y291D mutation , we further provide evidence suggesting that pY291 could enhance the uptake of the receptor via AP2-mediated CDE , which was important to its anti-apoptotic role . The fact that substitution of Y by other amino acids allowed Fas/FasL uptake by CDE suggests that the FasL-induced Fas uptake did not depend on Y291 as a part of the Y-based sorting motif but possibly of other motifs such as the acidic LL motif . Our finding is in line with previous reports for Vpu protein from HIV-1 subtype C [32] , in which the tyrosine was dispensable for the protein's cell surface transport but important for viral replication , while the LL motif was crucial for cell surface transport . Additionally , we offer an insight into distinct functions of Y232 and Y291 and their respective phosphorylation , which has not been addressed thus far: ( 1 ) the aromatic side chain of Y232 may contribute more to the structural integrity of the protein than that of Y291 ( Fig 1A ) ; ( 2 ) the size and details of the hydroxyl aromatic side chain of Y232 are essential for the functions of Fas , while the charge of Y291 is more important than the size and details of the aromatic side chain ( Fig 2 ) ; and ( 3 ) pY232 and pY291 are distinctly regulated in different types of cancer ( see below ) . SFKs are important mediators of tumor cell proliferation and survival and are involved in Fas signaling ( Fig 6A , [33–34] ) . Yet , how activities of SFKs exert an effect on Fas has been unclear thus far . We reveal an intricate regulation of Fas death domain phosphorylation by SFKs , leading to the inhibition of the apoptotic signal of Fas , and , thus , provide the first mechanistic link between SFKs , major drivers of tumor development and progression , and the control of Fas multisignaling . This is of clinical significance because , in tumors from glioblastoma multiforme patients , the expression of Yes and phosphorylation of SFKs , as well as an enhanced FasL expression , were observed in the zone of tumor–host interaction , suggesting their roles in glioma invasion [34] . This is in concert with the concept presented herein that , in cancer , Fas-mediated survival signaling is promoted by SFK-dependent tyrosine phosphorylation . Concerning the Fas pY dephosphorylation , it has been proposed that SHP-1 might be involved in this process in neutrophil , since it is associated with wild-type human Fas but not Y291A mutant in mouse lymphoblastic cells , and human SHP-1 from several cell lines could associate with phosphorylated peptides corresponding to the YxxL motif of death receptors [7] . We show that , in colorectal cells , pY232 and pY291 dephosphorylation is mediated by SHP-1 , which has been shown to effectively dephosphorylate Src substrates [35] and to negatively regulate colonic cells proliferation [36] . Our data support the notion that the pY-based proliferative/apoptotic switch system of Fas is regulated via phosphorylation by SFKs and dephosphorylation by SHP-1 , similar to that of caspase-8 [37–38] . This emphasizes the importance of the SFKs/SHP-1-based phosphorylation/dephosphorylation mechanism in the Fas multisignaling pathway . Multimodal signaling of Fas has been demonstrated in many cancer cell types , including colon [39] , breast [40] , and glioblastoma [34] . Currently , both pro-apoptotic and pro-survival roles of Fas are bases of therapeutics that aim either to activate Fas signaling ( APO010 agent targeting extracellular domain of Fas ) [41] or to inhibit Fas signaling triggered by FasL ( APG101 targeting FasL ) [42] . These approaches face a major challenge , since it has been unclear what determines the outcome of Fas signaling and when one role of Fas will dominate the other . Our observation that colon , breast , and ovarian malignant tissues from most patients we tested had higher levels of pY232 and/or pY291 than their corresponding normal tissues suggests the probability that the pro-survival signal of Fas may dominantly operate in these cancers . On the other hand , the opposite observation for tumors from lung cancer patients suggests the probability that Fas may not contribute to the pro-survival signal in lung cancer ( Fig 7C ) . Furthermore , we provide evidence that pY232 and pY291 can be distinctly regulated in cancer by showing opposing changes in their levels in rectal tumors in response to radiotherapy ( RT ± chemotherapy ) . The reduction of pY291 following the treatment may suggest a decrease in the FasL-induced pro-proliferative/anti-apoptotic signal of Fas conferred by pY291 , whereas the increase in pY232 may suggest an increase in other signaling events that involve the function of pY232 . This may include the involvement of pY232 in the cell cycle phase , since we also observed a Fas-dependent G2/M accumulation that depended on the phenolic hydroxyl group of Y232 ( Figs 2A and S10 ) . This G2/M accumulation was associated neither with the resistance to FasL-induced apoptosis ( Fig 2 ) nor an increase in FasL-induced cell proliferation ( Fig 5B ) , suggesting an additional role of pY232 that is independent of FasL-induced apoptotic and proliferative functions of the protein . Data presented here were obtained from a small number of patients . Thus , generalizations about the various extents of Fas phosphorylation in different types of cancer should be made with caution . However , our data revealing that the outcome of Fas signaling is determined by its pY status of the death domain and that the Fas pY status may differ among different cancer types and may respond to anticancer treatment provide a basis for further studies in larger sets of human cancer samples , as well as an opportunity to develop a practical means to predict the outcome of Fas signaling in different pathologies that can lead to the use of Fas pY screening to aid Fas-related therapeutic design and maximize the chance of therapeutic success . Overall , we provide the delineation of the pY-based control of Fas signaling , revealing differential evolutional criteria of the two death domain tyrosines , their regulatory elements , mechanistic links between this molecular switch system and the cellular outcome , and the implications in cancer . This information has far-reaching consequences , not only in cancer contexts but also in other pathologies in which Fas signaling is involved .
The rectal tumors were obtained from patients providing informed consent under protocols approved by the Clinic Institutional Review Board of the Département Recherche Clinique Innovation et Statistiques ( DRIS ) –Centre Antoine LACASSAGNE , Nice , France . Materials and additional detailed methods are listed in S1 Text . SW480 cells ( 2 . 5 x 105 cells/well ) were seeded in a 24-well plate for 24 h . The medium was then replaced with fresh RPMI+0 . 1% BSA containing 1 μg/ml mouse anti-Flag ( M2 ) + 1 μg/ml donkey anti-mouse Alexa Fluor 647 with or without 100 ng/ml FasL . The cells were then incubated at 37°C for a specified time to allow FasL-triggered stimulation . Thereafter , the plate was transferred to an iced water basin and the activation was stopped by adding ice-cold PBS . FasL that remained on the cell surface was removed using an ice-cold acid-stripping buffer ( 50 mM glycine , 100 mM NaCl , pH3 ) . After washing with ice-cold PBS , cells were analyzed for the internalized FasL ( crosslinked with mouse anti-Flag antibody and Alexa Fluor 647 anti-mouse antibody ) by flow cytometer ( LSRFortessa , Becton Dickinson ) . To assess the degree of FasL internalization , the median fluorescence intensity of the background control cells ( treated with anti-Flag and Alexa Fluor 647 without FasL ) was subtracted from the median fluorescence intensity of the FasL-treated cells to obtain the absolute fluorescence intensity of the detected internalized FasL in the cells . SW480 Cells ( 2 . 5 x 105 cells/well ) were seeded in RPMI+10% FBS on coverslip in 24-well plate for 24 h . Cells were then cooled down to 0°C in a refrigerating chamber for 45 min in RPMI+10% FBS+10 mM Hepes . The medium was then replaced with ice-cold RPMI+0 . 1% BSA+10 mM Hepes ( internalization medium ) containing 200 ng/ml FasL+1μg/ml mouse anti-FLAG ( M2 ) +1 μg/ml Alexa Fluor-647 anti-mouse antibody , and cells were incubated at 0°C in a refrigerating chamber for 1 h to allow FasL binding . The medium containing excess FasL was then removed , and internalization medium at 0°C was added to the well . The coverslips containing cells for control condition were kept at 0°C in the refrigerating chamber throughout the experiment , while coverslips containing cells destined for activation were rapidly transferred to another 24-well plate containing 0 . 25 ml of internalization medium per well ( maintained at 37°C in an incubator ) to trigger the internalization of Fas/FasL complexes . After incubation at 37°C for indicated times , the plate was rapidly transferred from the incubator to the refrigerating chamber , and 1 ml of PBS at 0°C was added to each well to stop the activation . Cells were then rapidly fixed with ice-cold 4% paraformaldehyde for 20 min on ice and counter-stained with DAPI for nuclear detection before mounting on a slide in the presence of mounting medium ( Fluoromount , Sigma-Aldrich ) . Fluorescence images were taken using a spinning disk confocal microscope ( Olympus/Andor/Yokogawa system ) with a 100×oil/1 . 4 numerical aperture objective lens . Images were deconvolved with Huygens software ( Scientific Volume Imaging ) . Phosphate affinity SDS-PAGE was performed using 7 . 5% polyacrylamide gels containing 10 μM acrylamide-pendant Phos-Tag ( Wako ) , according to the manufacturer's instructions . Highly phosphorylated proteins migrate more slowly through the gel than less phosphorylated proteins , allowing protein separation based on phosphorylation states .
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The versatility of the tumor necrosis factor receptor superfamily members in cell fate regulation is well illustrated by the dual signaling generated by one of the most extensively studied members of the family , Fas ( CD95/TNFSFR6 ) . Upon binding its ligand , Fas is able to elicit both pro-death and pro-survival signals . Until now , we have lacked mechanistic knowledge about when and how one signaling output of Fas is favored over the other . We demonstrate here that the outcome of Fas signaling is determined by the phosphorylation status of two tyrosine residues ( Y232 and Y291 ) within the death domain . Dephosphorylation of Fas tyrosines by SHP-1 tyrosine phosphatase turns on the pro-apoptotic signal whereas the tyrosine phosphorylation by Src family kinases ( SFKs ) turns off the pro-apoptotic signal and turns on the pro-survival signal . Furthermore , we provide evidence that Fas tyrosine phosphorylation status may vary among different cancer types and influence the response to anti-cancer treatments . This information reveals an opportunity to use the screening of Fas tyrosine phosphorylation , a newly discovered direct molecular indicator of Fas functional output , to aid the design of Fas-related cancer therapies .
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2016
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An Evolution-Guided Analysis Reveals a Multi-Signaling Regulation of Fas by Tyrosine Phosphorylation and its Implication in Human Cancers
|
Neutrophils are the first line of defense at the site of an infection . They encounter and kill microbes intracellularly upon phagocytosis or extracellularly by degranulation of antimicrobial proteins and the release of Neutrophil Extracellular Traps ( NETs ) . NETs were shown to ensnare and kill microbes . However , their complete protein composition and the antimicrobial mechanism are not well understood . Using a proteomic approach , we identified 24 NET-associated proteins . Quantitative analysis of these proteins and high resolution electron microscopy showed that NETs consist of modified nucleosomes and a stringent selection of other proteins . In contrast to previous results , we found several NET proteins that are cytoplasmic in unstimulated neutrophils . We demonstrated that of those proteins , the antimicrobial heterodimer calprotectin is released in NETs as the major antifungal component . Absence of calprotectin in NETs resulted in complete loss of antifungal activity in vitro . Analysis of three different Candida albicans in vivo infection models indicated that NET formation is a hitherto unrecognized route of calprotectin release . By comparing wild-type and calprotectin-deficient animals we found that calprotectin is crucial for the clearance of infection . Taken together , the present investigations confirmed the antifungal activity of calprotectin in vitro and , moreover , demonstrated that it contributes to effective host defense against C . albicans in vivo . We showed for the first time that a proportion of calprotectin is bound to NETs in vitro and in vivo .
Neutrophils are an essential component of the innate immune response since neutropenia or impairment of neutrophil function results in microbial infections that are often fatal [1] . Microbes engulfed by neutrophils are efficiently killed by reactive oxygen species ( ROS ) and antimicrobial proteins within vacuoles [2] . Additionally , neutrophils [3] and two other granulocytes , mast cells [4] and eosinophils [5] , release web-like extracellular traps that ensnare and kill microbes . Neutrophil Extracellular Traps ( NETs ) are released during a novel form of cell death that requires ROS produced by the NADPH-oxidase complex [6] . During this process , the nucleus decondenses and intracellular membranes disintegrate allowing the mixing of nuclear and cytoplasmic components . Eventually , the plasma membrane ruptures to release NETs , structures that contain chromatin and granule proteins . The overall composition of NETs has not been explored . Neutrophils of several species make NETs [7] , [8] , [9] and they might be important in the immune defense against bacteria and fungi [10] , [11] , [12] , [13] . Whereas bacteria [3] and parasites [14] probably are killed by histones in NETs , in a previous study we found that purified histones did affect Candida albicans in vitro only poorly [13] . Thus , it remains to be determined whether histones or other antifungal effectors in NETs kill or inhibit fungi . This seems to be particularly of importance since previous reports have demonstrated that histones and histone peptides kill different fungal species such as Cryptococcus neoformans and Candida tropicalis [15] , [16] , [17] . Fungal pathogens , in particular C . albicans , cause an increasing number of severe infections with high mortality rates [18] . C . albicans is an opportunistic pathogen that can be part of the normal microbial flora of humans . In immunosuppressed patients the microbe can use a variety of virulence factors that enables it to exploit various host niches and to cause different diseases ranging from cutaneous to systemic infections [19] . A key characteristic of C . albicans is the ability to change growth morphology from budding yeast to filamentous forms: pseudohyphae and true hyphae [20] . A variety of external stimuli have been shown to induce the yeast-to-hyphae transition , such as serum , alkaline pH and temperatures above 37°C [21] . The ability to reversibly switch between different morphologies upon external stimuli appears to be essential for the virulence of C . albicans [22] , [23] . Using a proteomic approach , we analyzed the qualitative and quantitative protein composition of NETs . We identified 24 different proteins , including the cytoplasmic calprotectin protein complex ( also called Mrp8/14-complex or S100A8/A9 ) that has been shown previously by several groups to have potent antimicrobial properties [24] , [25] , [26] . S100A8 and S100A9 belong to the large group of S100 calcium-binding proteins and form a heterodimer , calprotectin , which is abundant in neutrophils , monocytes and early differentiation stages of macrophages [27] . In other cell types , such as keratinocytes and epithelial cells , the expression can be induced under inflammatory conditions [28] . The antibacterial and antifungal activity of the complex is reversible by Zn2+ [29] and does not require direct contact to the microbe [30] , [31] . Therefore , it is thought that calprotectin chelates divalent metal ions that are required for microbial growth . This defense mechanism has been termed nutritional immunity [32] . Recently , Sroussi et al . proposed that the antifungal activity of calprotectin may be increased by oxidative stress [33] . Calprotectin is elevated in the extracellular fluids of patients with inflammatory disorders such as rheumatoid arthritis and vasculitis . Indeed , this complex is now used as a marker for inflammation [34] . Recently , calprotectin was described as a potential endogenous Toll-like receptor 4 ( TLR-4 ) activator that promotes lethal endotoxic shock [35] . Despite all these important extracellular functions the complex lacks a secretion signal . A non-classical and tubulin-dependent secretion mechanism was shown in monocytes activated by inflammatory cytokines [36] . The mechanism by which this cytoplasmic dimer derived from neutrophils is able to interact with extracellular microbes in vivo is not completely understood . Here we show that calprotectin is released as the major antifungal protein in NETs . Our data indicate that at infection sites NET formation is a mechanism which ensures the interaction between cytoplasmic calprotectin and extracellular microbes at high local concentrations .
We isolated neutrophils from healthy donors and induced them to form NETs using phorbol myristate acetate ( PMA ) . After gently washing the NETs twice to remove unbound proteins , we solubilized NET-bound proteins with DNase-1 ( Figure S1 ) . NET proteins were digested with trypsin and analyzed by nano-scale liquid chromatography coupled matrix-assisted laser desorption/ionization mass spectrometry ( nano LC-MALDI-MS ) . The identification quality is represented by the MS/MS spectrum of S100A9 , a subunit of calprotectin found with this approach ( Figure S2 A ) . A protein was considered to be associated to NETs when the identification criteria ( see Methods ) were fulfilled in at least two from a total of three independent samples . We identified 24 different proteins ( Table 1 , NET Database: http://web . mpiib-berlin . mpg . de/cgi-bin/pdbs/lc/index . cgi ) , nine of which were previously shown to localize in NETs by immunofluorescence microscopy [3] , [37] , which correlates well with the results of our approach . We identified proteins that have a nuclear , granular or cytoplasmic localization in unstimulated neutrophils . Among the nuclear components , we confirmed the presence of all four subtypes of core histones and newly found the myeloid cell differentiation antigen ( MNDA ) . We also identified eight granular proteins , five of which ( neutrophil elastase ( NE ) , lactotransferrin ( LTF ) , cathepsin G ( CG ) , myeloperoxidase ( MPO ) [3] and more recently proteinase 3 ( PR3 ) [37] ) were previously found associated with NETs . Azurocidin , lysozyme C ( LysC ) and α-defensins were not known to be NET components . Notably , we found eleven cytoplasmic proteins; two glycolytic enzymes , catalase , five cytoskeletal proteins and three S100 proteins . We confirmed NET association of twelve proteins found in this study by indirect immunofluorescence . Notably , the linker histone H1 , bactericidal/permeability increasing protein ( BPI ) , pentraxin 3 ( PTX-3 ) and cathelicidin ( CAP-18 ) , were described as NET-associated [3] , [38] , [39] , but we did not find them with this approach . Immunoblot analysis , however , confirmed the presence of BPI , but not of PTX-3 or CAP-18 ( Figure S2 B–D ) . To evaluate the specificity of our approach , we analyzed all purification steps . As previously described , activated neutrophils release many unbound proteins into the supernatant [40] ( Figure 1A and B , lane 2 ) which were removed by two washes with culture medium ( Figure 1A and B , lane 3 and 4 ) . NET-associated proteins were specifically released by Dnase-1 ( Figure 1A and B ) with or without protease inhibitor cocktail ( lane 7 and 8 respectively ) . As an additional control for the specificity of our approach we demonstrated that two cytoplasmic proteins , glyceraldehyde-3-phosphate dehydrogenase ( GAPDH ) and lactate dehydrogenase ( LDH ) , were present in the supernatant ( Figure 1B , lane 2 ) , but not in the nuclease-digested NETs ( Figure 1B , lanes 7 and 8 ) . To control the efficiency of the washing steps we increased the number of washes from two to nine ( 1 ml/wash , Text S1 ) in similar experiments as described above and determined the amount of calprotectin , a very abundant protein in neutrophils , in each fraction with ELISA . After NET formation calprotectin was present in similar amounts in culture supernatants and in wash 1 ( Figure S2 E ) . The calprotectin concentration dropped in wash 2 . These data agreed well with the immunoblot data presented in Figure 1B . However , we still detected minor amounts of calprotectin in wash 3 ( 55 ng/ml ) and 4 ( 21 ng/ml ) . The amount decreased further in washes 5–7 until the calprotectin concentration reached the detection limit ( 1 . 6 ng/ml ) of the assay in wash 8 and 9 . Notably , from digestion of these intensely washed NETs with 1 ml RPMI containing 5 U/ml MNase we obtained approximately 170 ng/ml calprotectin which equals 23% of the total amount ( 720 ng/ml ) according to our determination with the same ELISA under the same conditions . This is 20-fold more calprotectin than in wash 7 suggesting that calprotectin is bound to NETs . Taken together , this indicates that we purified and selectively enriched NET-associated proteins and that the NETs contain at least 24 proteins 15 of which were identified as NETs component for the first time ( Table 1 , marked in blue , and NET Database ) . To determine the relative amounts of NET proteins we quantified 15 of the 24 NET-associated proteins by immunoblotting ( Table 2 , Figure S3 , NET Database ) . We purified NET proteins from neutrophils isolated from ten healthy donors . On average , NETs derived from 1012 neutrophils contained 3 . 58+/−0 . 28 g of protein and 2 . 24+/−0 . 51 g of DNA . This indicates a ratio of 1 . 67+/−0 . 26 g of protein per gram of DNA . The core histones H2A , H2B , H3 and H4 , were the most abundant proteins and account for 70% of all NET-associated proteins . The molecular mass of these proteins is decreased by approximately 2–5 kDa , when compared to histones present in the nucleus ( Figure 2A ) . This modification is specific , since the masses of non-nuclear proteins do not change upon association with NETs . ( Figure 2A , S100A8 and NET Database ) . Moreover , the stoichiometry of the four core histones is different in NETs as compared to intact nuclei . In unstimulated neutrophils , the core histones are present in similar amounts ( Figure 2A , lane 2 ) . In contrast , on NETs , H3 and H4 are found in lower amounts than H2A and H2B ( Figure 2A , lane 7 ) and their molarity per gram NET-derived DNA is different ( Table 2 , NET Database ) . These observations correlated to high resolution Field Emission Scanning Electron Microscope ( FESEM ) analyses ( Figure 2B and C ) . NETs consist of “smooth” stretches , probably only composed of histones and DNA ( Figure 2B , white box ) , interspersed with globular domains that contain granular proteins [3] . The smooth stretches showed periodical signal intensities that are approximately 5 nm thick and 10 nm wide ( Figure 2C ) . These dimensions are similar to those of nucleosomes [41] suggesting that smooth NET stretches are composed of stacked cylindrical nucleosomes . On different areas we found intensities with a similar horizontal periodicity but a lower vertical dimension consistent with the notion that the histone composition was changed during NET formation ( Figure 2A ) . The most abundant non-histone NET protein was NE and the least abundant protein detected was catalase ( 5 . 24 and 0 . 02% of total protein , respectively ) . The 15 proteins we quantified represent 90 . 3% of the total protein in NETs . Thus , the remaining nine proteins and those that were potentially not identified in our approach represent 9 . 7% of the total NET proteins . NETs contained 39 ng of S100A8 and 17 ng of S100A9 per µg of DNA after 2 washes ( Table 2 and NET database ) . Since each well yielded in average 3 . 8 µg DNA , we calculated 148 ng S100A8 and 65 ng S100A9 per well . Both proteins add up to a total of 213 ng/well . This correlates well with the quantification of calprotectin by ELISA ( Figure S4 E ) which yielded 170 ng/well . It is possible that the difference observed is due to the different number of washes in the two experiments . Taken together , the different quantification methods are comparable . The association of calprotectin to NETs suggests that the complex is released during a specific form of holocrine secretion [42] referred to as NETosis [43] . Using indirect immunofluorescence we verified that calprotectin is released through NET formation in vitro ( Figure 3A–F ) . A calprotectin-heteroduplex-specific antibody [44] confirmed that the dimer was cytoplasmic ( red ) , and it overlapped with granular MPO ( green ) in the compact cytoplasm of unstimulated cells ( Figure 3A ) . There was also a faint calprotectin signal within the nucleus ( DNA stain , blue ) consistent with the fact that cytoplasmic proteins with a mass below 30 kDa can diffuse through the nuclear pore complex . Thirty minutes after stimulation the neutrophils flattened as a sign of activation revealing a granular staining for MPO and a more dispersed cytoplasmic staining for calprotectin ( Figure 3B ) . After stimulation for 1 hour , we observed partial colocalization of MPO and calprotectin in the cytoplasm ( Figure 3C ) . This increased two hours after activation , when the chromatin decondensed and the nuclear membrane disassembled . At this time point calprotectin , MPO and DNA colocalized ( Figure 3D ) . A proportion of the MPO signal remained in the area between the plasma membrane and the decondensed nucleus . Three and 4 hours post-activation , when the plasma membrane ruptured , calprotectin , DNA and MPO colocalized on NETs ( Figure 3E–F ) . To determine whether calprotectin is released during NET cell death or before the plasma membrane ruptures , we compared neutrophils that were treated either with PMA to stimulate NET formation [3] or with the microbial peptide formyl-met-leu-phe ( f-MLP ) to stimulate degranulation [40] . At the indicated time points we monitored neutrophil cell death by quantifying extracellular LDH ( Figure 3G ) and solubilized NET-bound proteins by adding Dnase-1 ( Figure 3H ) . We subsequently collected the supernatants containing both unbound and NET-bound proteins to determine the total release of the indicated proteins during degranulation and NET formation . We analyzed the samples by immunoblotting and probed for the calprotectin subunits S100A8 and S100A9 as well as for two granular proteins , MPO and LTF ( Figure 3H ) . Thirty minutes after f-MLP treatment , neutrophils released MPO and LTF , but not S100A8 nor S100A9 . Four hours after f-MLP treatment , minor amounts of S100A8 and S100A9 appear in the supernatants and less than 10% of the cells were dead . During NET formation , MPO and LTF were secreted in low amounts before S100A8 and S100A9 were released confirming that neutrophils also degranulate upon PMA-activation [40] . Significant amounts of extracellular calprotectin were only found 3 to 4 hours after stimulation , when 40 and 70% of the neutrophils respectively were dead . Furthermore , we determined how much of the total calprotectin amount present in the neutrophil actually binds to NETs . After stimulation we quantified S100A9 released into the supernatant , bound to NETs and remaining in the cell remnants by quantitative immunoblots . Approximately 30% of the total S100A9 was bound to NETs ( Figure 3I ) . Equivalent amounts of S100A9 were found in the supernatants and in the nuclease-resistant cell remnants ( Figure 3I ) . This agrees with a previous report showing that after nuclease treatment , a proportion of chromatin remains within the debris of the neutrophil [6] . We obtained similar results for S100A8 ( data not shown ) . Moreover , a calprotectin specific ELISA ( Figure S2 E ) confirmed the finding that similar amounts of calprotectin are in NETs ( 170 ng/ml ) compared to unbound in the culture supernatant ( 200 ng/ml ) and that this corresponds to 23% and 28% of the total amount of calprotectin respectively . Taken together , this demonstrates that approximately one third of calprotectin in vitro is released from neutrophils in NETs . As we have reported previously NETs can control the growth of C . albicans yeast and hyphal forms [13] and more recently this has been demonstrated for Aspergillus nidulans conidia and hyphae as well [45] . Additionally , we have now added Cryptococcus neoformans to this list of NET inhibited fungi ( Figure S4 A ) . However , the mechanism behind the antifungal activity remained unclear . In this study we confirmed the previous results by exposing C . albicans to NETs at different multiplicities of infection ( MOI ) and at different temperatures to induce yeast-form and hyphal growth ( Figure S4 B and C ) . Under all conditions the antifungal activity of NETs was very similar reducing CFU counts approximately 100-fold , but not when the NETs were degraded with nucleases ( Figure S4 D ) . We confirmed the antifungal activity of NETs against hyphae macro- and microscopically ( Figure S4 E and F ) . As expected , hyphae grew in media but this growth was inhibited when they were incubated with NETs . We corroborated the anti-candidal activity of NETs and showed similar results measuring the viability of C . albicans hyphae with the tetrazolium dye XTT ( Figure S4 G–I ) . This method , unlike CFU enumeration , does not require the dispersion of the hyphae [46] . We confirmed that NETs made by neutrophils stimulated with C . albicans contain calprotectin , lactotransferrin and catalase ( Figure S4 J ) . This suggests that NETs have a qualitatively similar composition regardless of the stimulus used to activate the neutrophil . Calprotectin chelates essential metal ions , such as Zn2+ and Mn2+ resulting in reduced microbial growth [30] , [31] . Consistent with this mechanism of action the antifungal activity of NETs was inhibited by increasing concentrations of Zn2+ ( Figure 4A ) or Mn2+ ( Figure 4B ) . The direct role of calprotectin in NETs was tested with immunodepletion experiments . We purified NET proteins by DNase-1 treatment and concentrated the samples 10-fold with 3 . 5 kDa cut-off membranes . The samples were incubated with a mixture of immobilized antibodies directed against the individual subunits S100A8 and S100A9 ( Figure 4C ) . Immunodepletion of calprotectin , but not treatment of NET preparations with isotype-matched controls , completely abrogated the growth-inhibitory activity of NET proteins showing that this dimer is a major antifungal component of NETs . It is important to note that immunoblotting of the supernatant showed that S100A9 , but not lactotransferrin , was depleted ( Figure 4C ) . A complementary assay where C . albicans viability was measured using XTT confirmed the result of the CFU based assay ( Figure S4 H ) . Furthermore , NETs made by calprotectin-deficient mice inhibited C . albicans growth less efficiently than wild-type mouse NETs ( Figure 4D ) which was confirmed with an XTT assay as well ( Figure S4 I ) . Notably , neutrophils from both genotypes made NETs with similar efficiency ( Figure S5 A–F ) . The antimicrobial activity of calprotectin does not require direct contact between microbe and protein , which is consistent with its inability to bind microbial surfaces [30] , [31] . Regardless , at high concentration this chelator is more efficient . Therefore , we tested whether NET-bound calprotectin binds to microbes . Seeded neutrophils were induced to release NETs . We collected the supernatant after NET formation that contained unbound and soluble calprotectin . The NETs were subsequently washed twice and digested with MNase , a non-processive nuclease , to generate NET fragments . We exposed the indicated fungi to these NET fragments or supernatants containing soluble calprotectin and pelleted the microbes afterwards . The microbial pellets were washed three times . Immunoblotting in Figure 4E shows that NET-associated , but not soluble S100A9 , binds to the fungi . We obtained similar results for S100A8 ( data not shown ) . Taken together , these data indicate that calprotectin is a major antifungal NET component . We conclude that the presentation of this dimer in NETs provides a high local concentration on the surface of microbes . We addressed the role of calprotectin in C . albicans infections comparing wild-type to S100A9 knockout mice . These animals transcribe the mRNA for S100A8 but are deficient in S100A8 and S100A9 protein [47] . C . albicans exploits different host niches and we investigated three of them: ( i ) subcutaneous inoculation , which leads to confined abscesses; ( ii ) intranasal infection , which causes pulmonary candidiasis and ( iii ) intravenous challenge , which mimics disseminated systemic candidiasis . The dimensions of the abscess lesions were measured at indicated time points . On average , the area of the lesions in calprotectin –deficient mice were twice as large ( 200 mm2 ) as compared to those of wild-type animals ( 100 mm2 ) measured at two , four and six days after inoculation ( Figure 5A–E ) . At day four , 60% of the abscesses in calprotectin-deficient mice ulcerated as indicated by extensive necrosis which also included the epidermis ( Figure 5A ) . Consistent with a less severe progression , abscesses of wild-type animals were restricted to subcutaneous areas without involvement of outer skin layers ( Figure 5D ) . Eight days after inoculation , the size of the abscesses in calprotectin-deficient and wild-type mice was similar , indicating that calprotectin is essential for the initial phase of the disease but that eventually other mechanisms clear the infection . Interestingly , in about 30% of the infected knockout mice , but not in wild-type controls , infection spread from the original abscess to surrounding areas of the skin ( Figure S6 ) . In our experimental settings neutrophil recruitment ( Figure S5 G ) and NET formation ( Figure S5 A–F ) was similar in knockout and wild-type animals . In pulmonary candidiasis , wild-type mice show disease symptoms within three days but recover and survive . In contrast , calprotectin-deficient mice carried a significantly higher fungal load than wild-type controls ( Figure 5F ) and succumbed to C . albicans ( Figure 5G ) . Intravenous challenge is lethal in both calprotectin-deficient and wild-type mice . However , the knockout animals died significantly earlier ( Figure 5H ) , suggesting that also in deep-seeded infection sites , the antimicrobial activity of calprotectin contributes to containment of fungal growth . This is consistent with a previous report that calprotectin reduces bacterial load in systemic staphylococcal infection [31] . We conclude that calprotectin is required for an effective acute antifungal response . We tested whether NET formation is a route of calprotectin release in vivo during subcutaneous ( Figure 6A–F ) and pulmonary C . albicans infection ( Figure 6G–M ) . Histological analysis of 6 day old abscesses showed fungal foci surrounded by neutrophils ( Figure 6A ) . Using staining with hematoxylin and eosin ( H &E ) , we observed abundant extracellular DNA in web-like structures in these areas ( Figure 6B ) . To confirm that these structures were NETs , the samples were labeled with antibodies directed against MPO ( green ) and histones ( blue ) and analyzed by immunofluorescence microscopy . Labeling with anti-S100A9 antibodies demonstrated that these structures also contain calprotectin ( Figure 6 , C–F ) . In infected lungs , there was a strong neutrophil infiltration into C . albicans colonized bronchioles one day after challenge . These areas showed web-like structures of extracellular DNA ( Figure 6 G and H ) . MPO and histones ( Figure 6M , arrow ) , as well as calprotectin ( Figure 6I–l ) colocalized in these structures that unfold into the lumen of the bronchioles . To demonstrate that C . albicans and NETs interact in the lumen of bronchioles we analyzed these sections by Scanning Electron Microscopy ( SEM ) ( Figure 7A ) . Areas colonized by C . albicans show web-like structures that cover fungal surfaces ( Figure 7B and C ) . These structures have a very similar morphology and dimension to those observed for NETs in vitro . Taken together these observations demonstrate that NETs and C . albicans interact in vivo and that these NETs contain calprotectin . We propose that release and NET-association of calprotectin from neutrophils could contribute to contain fungal infections .
The analysis presented here identified 24 NET-associated proteins . Nine of the 24 proteins were described previously as NET-associated which correlates well to our approach . Fifteen proteins were hitherto unknown to be NET-bound and their association to NETs was confirmed by immunoblotting and indirect immunofluorescence ( NET Database ) . The data underscore the specificity and reproducibility of the analysis . Moreover , the composition of the identified NET proteins was very similar among different neutrophil donors ( NET Database ) . The small number of identified NET proteins is surprising since the neutrophil cell membrane ruptures during the process of NET release . Additionally , we used mild washing conditions for the isolation . Therefore , the protein incorporation into NETs appears to be selective . We found that , in addition to five nuclear proteins , the NETs contain eight cationic granule proteins . Furthermore , we identified eleven cytoplasmic proteins which have neutral to acidic isoelectric points . This suggests that charge is not an exclusive requirement for binding to NETs . In previous reports histone H1 [3] , bactericidal/permeability increasing protein ( BPI ) [3] , cathelicidin ( CAP-18 ) [37] , [39] and pentraxin 3 ( PTX-3 ) [38] have been described as NET-associated proteins . In contrast , we did not find these proteins in our MS approach . We further investigated these proteins by immunoblot ( Figure S2B–C ) . We did not obtain clear results for histone H1 because commercially available anti H1-antibodies are cross-reactive with other histones ( data not shown ) . This remains to be clarified . We could , however , detect the 25 kDa cleavage product of BPI in both neutrophil lysates and NET extracts . The failure of MS analysis to detect BPI remains unclear ( Figure S1B ) . Notably , we detected CAP-18 and PTX-3 in neutrophil lysates but not in NET extracts . Therefore the presence of these two proteins in NETs should be further investigated . It is possible that these proteins are very loosely attached to NETs , that they are present in very low amounts , that they were lost during the isolation procedure or that the MS analysis failed to detect them . Notably , neither by immunoblotting nor by MS did we find GAPDH , a very abundant cytoplasmic and highly cationic protein . This supports the assumption that NET binding is not exclusively mediated by charge . Quantification by immunoblots showed that 15 NET proteins comprised 90% of the total protein amount in NETs , indicating good coverage . Detection of 1 . 25 ng of catalase per µg NET-DNA underscored the sensitivity of the approach . As the amounts of NET proteins were similar in different donors the approach was also reproducible ( NET Database ) . More importantly this suggests that NETs do not assemble randomly and are similar in many individuals . The core histones are the major protein components in NETs as shown by the quantitative analysis . However , we found a reduction of the molecular mass of NET-associated compared to chromatin histones , possibly caused by post-translational modifications . Indeed , histone H3 is deiminated during NET formation in HL60 cells but this remains to be confirmed in primary neutrophils [48] . Additionally , the stoichiometry of the four histones is different in NETs when compared to chromatin . There is less H3 and less H4 in NETs than in chromatin , but the significance of these differences remain unclear . High resolution FESEM correlated with this finding . We determined a transverse periodicity in individual NET-fibers that was similar to the actual dimensions of intact and partially degraded nucleosomes . The role of some of the identified cytoplasmic proteins in NETs is unknown but there are indications for potential functions . Enolase , for example , has been found to be a plasminogen activator on leukocyte surfaces , although the secretion mechanism is unknown [49] . NET-association could explain how enolase is released allowing it to participate in tissue remodeling at inflammatory sites . We identified S100A8 and S100A9 that form the heterodimer calprotectin as NET-associated proteins by MS analysis and by indirect immunofluorescence . The dimer lacks a secretion signal and localizes to the cytoplasm as well as partially to the nucleus of unstimulated neutrophils . Therefore , the proteins must be released in order to function as an antimicrobial protein against extracellular pathogens . The release of calprotectin by neutrophils [50] and other cells [36] in response to specific stimuli was previously reported . Our data , however , establish NET formation as a hitherto unrecognized mechanism of calprotectin release in neutrophils . Interestingly , incubation of C . albicans with neutrophils was reported to increase extracellular calprotectin and , simultaneously decrease neutrophil viability [51] . The release mechanism , however , remained unknown . Consistent with these data we reported that C . albicans induces neutrophils to form NETs and die in the process [13] , [52] . Here , we demonstrate that NET formation induced by C . albicans during infection is a novel route for release and presentation of calprotectin in vivo . We additionally showed that calprotectin tightly binds to NETs as several intense washes cannot remove calprotectin from NETs ( Figure S2 E ) or NET-bound calprotectin from fungal surfaces ( Figure 4E ) . In contrast the unbound proportion of calprotectin released during NET formation does not adhere to fungal surfaces . These findings suggest that calprotectin , although it is abundant in unstimulated neutrophils , specifically interacts with NETs . We determined that 30 and 23% of the total calprotectin was NET-associated by immunoblot densitometry ( Figure 3I ) and ELISA ( Figure S2E respectively ) . These values indicate that a significant proportion of the protein complex associates with NETs . Additionally , approximately the same amount of calprotectin is released to the supernatant . Thus , probably 50–60% of total cellular calprotectin might become extracellular during NET formation . It is important to note that with other methods previous publications determined the total amount of calprotectin to be approximately 5 µg/106 human neutrophils [53] , [54] . Here we report approximately a ten-fold lower amount of calprotectin ( 0 . 43 µg/106 ) . This difference could be due to the methodology , donor variation and the technique to count cells . Our functional assays on the inhibition of C . albicans growth by NETs suggest that calprotectin in NETs might have a biological implication due to the high local concentration and close contact to the microbe . In agreement with the findings discussed above we showed that calprotectin is a major antifungal protein in NETs affecting C . albicans and C . neoformans . Addition of Zn2+ or Mn2+ and immunodepletion of calprotectin abrogated the antifungal activity of NETs . This is consistent with previous reports showing that calprotectin is microbiostatic in vitro because it chelates ions [24] , [25] , [26] , [30] . Notably , an enhancement of calprotectin's antifungal activity by oxidative stress has been reported recently [33] . This could as well be the case in NETs , since the release requires a robust oxidative burst to occur before [6] . Other NET proteins were not affected by calprotectin depletion ( Figure 4C ) , but nonetheless NETs contain more antifungal proteins , such as azurocidin and LTF . Interestingly , addition of Fe2+ did not have an impact on C . albicans in our experimental settings ( data not shown ) , suggesting that calprotectin , rather than LTF , controls C . albicans under these conditions . This is probably also true for C . neoformans . Fractionations of neutrophil compartments revealed previously that azurophilic granule extract inhibited cryptococcal growth to 80% and cytoplasmic extract to 70% [55] . The group identified calprotectin as the one single effector in the cytoplasm responsible for the strong inhibition correlating well with our findings . Whether other proteins contribute to the antifungal activity of NETs still remains to be determined ( Figure 4D ) . Moreover , our data demonstrate for the first time that calprotectin is required for the innate immune defense to C . albicans infections . We detected abundant NETs in subcutaneous abscesses ( Figure 6A–F ) , lungs after pulmonary challenge ( Figure 6 G–M ) and kidneys from systemic candidiasis ( data not shown ) , and we propose that they prevent the spread of C . albicans . This might be in particular important for C . albicans hyphae that are too large to be engulfed and is consistent with our previous finding that hyphae are more potent NET inducers as yeasts [52] . Thus , we conclude that NET formation could serve as an additional neutrophil-mediated anti-hyphal mechanism complementing the previously described damage of hyphae by oxidative products and the myeloperoxidase system [56] , [57] . The NET-released calprotectin reduces C . albicans growth correlating well with the more severe disease symptoms we observed in calprotectin-deficient mice as compared to wild type: increased abscess sizes upon subcutaneous challenge and higher fungal loads in lungs upon intranasal challenge . Indeed , the importance of NETs in reducing dissemination seems likely to be similar to that observed in streptococcal pneumonia [10] . This is consistent with a stronger survival phenotype of calprotectin in pulmonary compared to systemic infection , where C . albicans was disseminated from the outset . We and others determined neutrophils as a major source of calprotectin in infected tissue under the given conditions ( data not shown , [31] ) . However , calprotectin has been reported to be expressed by other myeloid and epithelial cells and to be involved in numerous other immune-related functions , such as chemotaxis , regulation of the NADPH oxidase complex , activation of Toll-like receptor 4 and induction of apoptosis [35] , [58] , [59] , [60] . Any of these functions may contribute to the phenotypes in C . albicans infection we report here . So far we have not identified a component that is exclusively required for NET formation but not other neutrophil functions . Therefore , we do not have a tool to determine the actual contribution of NET-associated calprotectin in the described infection phenotypes . We propose that differential localization of calprotectin , e . g . unbound and NET-bound , may enable the protein to be multi-functional . Our analysis revealed a simple , stable and reproducible composition of NETs . These data are likely to help investigate the functions of NETs in infections and in other disorders such as autoimmune diseases .
S100A9−/− mice [47] that are deficient in both calprotectin subunits S100A8 and S100A9 protein were backcrossed 7 times into C57 BL/6 . These mice and C57 BL/6 controls were bred in our animal facility . All animal experiments were in compliance with the German animal protection law in a protocol approved by the Landesamt fur Gesundheit und Soziales , Berlin . Human peripheral blood neutrophils and mouse bone marrow derived neutrophils were isolated as described [6] , [61] . We used C . albicans clinical isolate SC 5314 [62] . C . neoformans strain DSM 11959 was obtained from the German Collection of Microorganisms and Cell Cultures ( DSMZ ) . C . albicans was cultured overnight in YPD ( 1% yeast extract , 2% bacto peptone and 2% glucose ) at 30°C and C . neoformans in the same medium at 37°C . Cells were washed 3 times in PBS prior to the experiments . Cell numbers were calculated by OD600 correlation ( C . albicans: 1 OD600 = 3×107 cells/ml; C . neoformans: 1 OD600 = 6×107 cells/ml ) . For experiments with neutrophils and NETs C . albicans was either incubated at 37°C to induce hyphae or at 30°C to preserve yeast-form growth . C . neoformans was always incubated at 37°C . All infection assays were performed in RPMI medium . Human neutrophils were seeded in 12-well tissue culture plates to a density of 1 . 7×106 ml−1 ( RPMI 1640 without phenol red ) . 1 . 7×106–3 . 4×106 ( according to 1–2 wells ) and 1 . 7×107 ( according to 10 wells ) neutrophils were used for analytical and MS identification respectively . Neutrophils were activated with 20 nM PMA for 4 h at 37°C in a 5% CO2 atmosphere . Each well was carefully washed twice after removing the supernatant by pipetting 1 ml of fresh and pre-warmed RPMI into the well along the wall of the well . Each wash was incubated for 10 min at 37°C . Subsequently the NETs were digested for 20 min in 1 ml RPMI with 10 U/ml DNase-1 ( Worthington ) . DNase-1 was stopped with 5 mM EDTA ( final concentration ) . The samples were centrifuged at 300×g to remove whole cells and then at 16 , 000×g to remove debris . Proteins were acetone precipitated . Four matched samples from different wells were pooled together and transferred to a 30 ml glass corex tube and 16 ml of ice-cold acetone ( −20°C ) was added . For precipitation samples were incubated overnight at −20°C and then centrifuged at 10 000×g for 30 min at 4°C . The protein pellet was washed with 1 ml 80% acetone buffered in 20 mM Tris-HCl pH 8 . 0 and solubilized in 120 µl SDS loading buffer or prepared for MS identification as described below . For verification of our purification procedure we followed each step of the isolation procedure by analysis of the respective sample using silver stained SDS electrophoresis and immunoblotting . We harvested all steps including the supernatant after 4 h incubation , the washes 1 and 2 , and the NET digests . As controls ( i ) NETs were mock-digested with nuclease-free RPMI and ( ii ) unstimulated neutrophils that did not release NETs were washed twice and incubated with RPMI containing 10 U/ml Dnase-1 for 20 min at 37°C . Each sample was derived from one well containing 1 . 7×106 neutrophils in a volume of 1 ml . Four samples out of 4 wells were pooled , acetone precipitated , solubilized in 120 µl SDS loading buffer and boiled for 3 min . To account for potential protein loss due to proteolytic activity in the samples a complete purification procedure was performed in the presence of protease inhibitor cocktail ( Sigma P1860; 1∶200 ) added to the wells 2 h after stimulation start as described above . Protease inhibitor cocktail was additionally present in all media used for washing and digestion . For analysis of the purification steps we loaded 30 µl of each pooled sample ( equaling 1 well and 1 . 7×106 neutrophils ) on SDS protein gels ( Tris-HCl 10–20% ) . The gels were either silver stained as described elsewhere [63] or immunoblotted by transferring to a PVDF membrane ( Immobilon 40; Millipore ) . The immunoblots were performed as described in detail for the quantitative blots below . The following primary antibodies were used: α-elastase ( Calbiochem 481001 , 2 µg/ml ) , α-azurocidin ( Sigma N5662 , 0 . 5 µg/ml ) , α-histone H2B ( Upstate 07-371 , 1 µg/ml ) , α-S100A8 combined with α-S100A9 ( Acris BM4029 and BM4027 , both at 2 . 5 µg/ml ) , α-catalase ( Sigma C0979 , 1 µg/ml ) , α-GAPDH ( Labfrontier LF-PA0018 , 0 . 5 µg/ml ) , α-LDH ( Abcam ab7639 , 1 µg/ml ) . As secondary antibodies horseradish-peroxidase-conjugated F ( ab' ) 2 fragments ( Jackson ImmunoResearch ) were used in 1∶20 000 dilutions . For MS analysis NET proteins from 3 different donors were purified independently in the absence ( donor 1–3 , named sample 1–3 ) or presence of the protease inhibitor cocktail indicated above ( donor 1–2 , named sample 4 and 5 ) . DNA-concentration , neutrophil elastase activity and reactive oxygen species ( ROS ) were measured as described previously [6] . Samples 1–5 ( acetone precipitates from NET-purifications ) were solubilized in 40 µl of 500 mM triethylammonium bicarbonate buffer pH 8 . 5 ( TEAB ) and reduced with 2 µl of 50 mM tris- ( 2-carboxyethyl ) phosphine ( TCEP ) for 60 min at 60°C . After alkylation with 1 µl of 200 mM methyl methanethiosulfonate ( MMTS ) at RT for 10 min each sample was incubated overnight at 37°C with 10 µl of a 200 µg/ml trypsin solution , solubilized in 500 mM TEAB . The reaction was stopped with 1 µl of a 10% TFA solution to obtain a final concentration of TFA of approximately 0 . 2% . The sample was centrifuged for 10 min at 13800×g and the supernatant used for LC/MS analysis . The samples were analyzed by bottom-up nano-LC/MALDI-MS as described in detail in the NET Database . Proteins were digested with trypsin and the resulting peptides separated by nano-LC ( Dionex ) . Peptides were fractionated ( Probot microfraction collector , Dionex ) and analyzed with a 4700 Proteomics Analyzer ( Applied Biosystems ) MALDI-TOF/TOF instrument . The criterion for the identification of a protein was a minimum number of 3 peptides fulfilling the Mascot homology criteria . Candidates with two peptides fulfilling these criteria were verified by checking the fragmentation rules , such as hypercleavage sites ( Asp , Glu , Pro ) , the appearance of common immonium masses and mass losses [64] . A protein was considered as localizing to NETs only when found in at least 2 independent samples from different donors . Exceptions are MNDA , actinin and lysozyme C . MNDA and actinin were identified with one peptide in independent samples only , however the peptide is unique to both proteins within the IPI-database . Presence of lysozyme C in NETs was verified by immunoblotting . The MS analysis is described in more detail on the NET database and in the supporting materials . NET-associated proteins were quantified by immunoblot as described [65] . Neutrophils were purified from 10 different healthy donors , proteins were quantified using the DC assay ( Biorad ) and DNA was quantified with Pico Green™ ( Invitrogen ) [6] . We isolated NET proteins as described in ‘Purification of NET-proteins’ . From each donor we prepared 12 times 1 . 7×106 neutrophils each seeded in 1 ml RPMI per well in a 12 well tissue culture plate . NETs from 10 wells were digested with 5 U/ml MNase ( Fermentas ) , a non-processive nuclease that cuts DNA at linker sites . 2 wells were mock-digested with nuclease-free medium as controls . We measured 3 independent pools made from 3 different donors each to average samples . For each quantification , the 3 averaged samples and 6 different concentrations of the respective purified protein , used as a standard , were loaded on the same SDS-protein gel ( Tris-HCl 10–20% ) . The gel was transferred to a PVDF-membrane ( Immobilon 40; Millipore ) in a liquid transfer system for 2 h at 80 V . The membrane was blocked for 1 h in PBS with 5% skim milk powder , washed 3 times in PBS with 0 . 05% Tween-20 ( PBST ) . Primary antibodies ( listed in the NET Database ) were diluted in PBST with 1% BSA ( PBST-BSA ) and incubated with the membrane for 1h at room temperature or at 4°C over night . After 3 washes in PBST horseradish-peroxidase-conjugated F ( ab' ) 2 fragments ( Jackson ) were used as secondary antibodies and incubated for 30 min . The blots were developed with chemiluminescence and detected in a LAS 3000 camera ( Fujifilm Europe ) . The signal intensities of the bands were analyzed by 2D densitometry ( array imager software 4 . 15 , Raytest ) and the concentration calculated based on the purified protein standards in a linear range of analysis . The amount of sample was adjusted to be within this range . To confirm the reproducibility of our quantification approach , we prepared a separate batch of pooled NET proteins from additional 5 donors and quantified again 7 randomly chosen NET proteins producing comparable results ( NET Database ) . Recombinant human S100A8 and S100A9 for quantification were purified as previously described [66] . cDNA of human S100A8 and S100A9 in the plasmid pQE32 ( C . Kerkhoff ) were amplified by standard PCR using primers with HindIII and NdeI restriction sites at either end ( S100A8: AGTCCTAAGCTTCTACTCTTTGTGGCTTTCTT , ATTACACATATGATGTTGACCGAGCTGGA; S100A9: ATCTAACATATGATGACTTGCAAAATGTCGCAGC , ATCTTCAAGCTTTTAGGGGGTGCCCTCCC ) . The PCR products were cloned into the expression vector pET28a+ ( Invitrogen ) and confirmed by sequencing . We purchased recombinant core histones from Upstate , catalase purified from human erythrocytes ( Sigma-Aldrich ) and all other proteins purified from human neutrophils ( Athens Research and Technology ) . Human neutrophils were seeded on 12-mm cover slips and stimulated with PMA for 4 hours . After fixation ( 2 . 5% glutardialdehyde ) , specimens were contrasted using repeated changes of 0 . 5% OsO4 and 0 . 05% tannic acid . Specimens were then dehydrated in a graded ethanol series and subjected to critical point drying . After coating with platinum/carbon , specimens were analyzed in a Leo 1550 field emission scanning electron microscope ( FESEM , Zeiss SMT ) . Peripheral NET areas with individual NET fibers were recorded at high magnification and the obtained images were analyzed using the SmartSEM software ( Zeiss SMT ) . 1 . 7×106 ml−1 human neutrophils were seeded in 1 ml RPMI medium containing 10 U/ml DNase-1 in 12 well tissue culture plates . For each condition to be tested three wells were seeded with neutrophils ( n = 3 ) . They were stimulated to form NETs using 20 nM PMA , or to degranulate using 5 µM f-MLP at 37°C in a 5% CO2 atmosphere similar to [40] . Dnase-1 was present to collect all released proteins , also the ones that are bound to NETs . At the indicated time points the supernatants were collected . Hundred µl aliquots were used to quantify LDH activity in the samples using the Cytotoxicity Assay™ ( Promega ) as a marker for cell death [67] , [68] . As 100% control for LDH we lysed the same amount of neutrophils in 1 ml RPMI with 0 . 1% Triton X-100 and measured 100 µl . The rest of the samples were acetone precipitated . Precipitates were boiled in 30 µl SDS sample buffer and analyzed by immunoblotting using anti-S100A8 ( Acris BM4029 , 2 . 5 µg/ml ) , anti-S100A9 ( Acris BM4027 , 2 . 5 µg/ml ) , anti-LTF ( Sigma-Aldrich L3262 , 2 µg/ml ) and anti-MPO ( DAKO A0398 , 2 µg/ml ) antibodies . To determine the relative amounts of calprotectin in the supernatant after NET formation and in NETs we seeded 1 . 7×106 neutrophils per well and induced them to make NETs for 4 hours . We collected the supernatant and washed the cells twice . The washes were discarded . Then we digested the NETs with nuclease in the same volume and removed the supernatant again after 20 min . Subsequently , we added 200 µl reducing SDS protein sample buffer to the remaining debris in the wells and scratched them thoroughly and boiled for 3 minutes . As 100% control we lysed the same amount of neutrophils in 400 µl protein sample buffer and boiled as well . The supernatant and the NET digest were lyophilized overnight and also resuspended in 200 µl sample buffer . 40 µl of each fraction was loaded and subjected to SDS PAGE and immunoblotting with an anti-S100A9 antibody . The signal intensities of the bands were analyzed by 2D densitometry as described under “Quantification of NET proteins” . The relative amounts were calculated . 5×105 ml−1 human or murine neutrophils were stimulated with 20 nM PMA for 4 h at 37°C in a 5% CO2 atmosphere to form NETs in 24 well tissue culture plates . Supernatants were then removed , NETs were washed twice with 1 ml RPMI and microbes were added at a multiplicity of infection ( MOI ) of 0 . 01 if not stated differently in 500 µl of RPMI per well . Samples C . albicans were incubated overnight at 30°C to induce yeast-form growth or at 37°C to induce hyphal growth . NETs showed to be similarly antifungal at both temperatures ( Figure S4 ) and purified calprotectin as well ( data not shown ) . Assays with C . neoformans were incubated only at 37°C . After incubation overnight the microbes were plated on YPD agar plates to determine colony forming units ( CFU ) or an XTT assay was performed as shown in Figure S4 . For assays in Figure 4 A & B C . albicans and NETs were incubated at 30°C . Twenty times 5×105 human neutrophils were seeded into 24 well tissue culture plates each in 500 µl RPMI and stimulated with 20 nM PMA for 4h at 37°C in a 5% CO2 atmosphere to form NETs . Supernatants were removed , NETs were washed twice with 1 ml RPMI and digested with 500 µl 10 U/ml DNase-1 each . The digested NET proteins were pooled into two 5 ml fractions and concentrated 10-fold on filter columns with a 3 . 5 kDa cut-off to a volume of 500 µl . The concentrated samples were incubated for 2 h either with a combination of anti-S100A8 and anti-S100A9 ( Acris BM4029 and BM4027 , 10 µg each ) , or isotype mouse IgG1 as a control ( Sigma M5284 ) , immobilized on 100 µl magnetic sepharose beads ( Pierce ) . After incubation , the samples were diluted back to the original volume using fresh medium to complement metal ions . CFU were determined after incubation of these samples overnight with C . albicans . Viability of C . albicans was also monitored using the XTT assay as stated in supporting material Figure S4 . The immunodepletion was evaluated by immunoblotting . Samples were tested for the presence of calprotectin ( Acris BM4027 , 2 . 5 µg/ml ) and lactotransferrin ( Sigma-Aldrich L3262 , 2 µg/ml ) as a control . 1 . 7×106 ml−1 human neutrophils were stimulated with 20 nM PMA for 4h at 37°C in a 5% CO2 atmosphere to form NETs in 1 ml RPMI . NET-free supernatants after incubation were removed and kept . NETs were washed twice with 1 ml RPMI and digested in 1 ml RPMI with 5 U/ml MNase for 20 min to obtain fragments of NETs . NET-free supernatants and digested NET fragments were incubated on a rolling wheel with 3×107 C . albicans yeasts and C . neoformans from YPD overnight cultures previously washed 3 times in PBS . After 30 min the microbes were pelleted , the supernatants removed and washed three times in fresh medium . These new supernatants were acetone-precipitated . Precipitates from supernatants and microbial pellets were boiled in SDS sample buffer and analyzed by immunoblotting using anti-S100A8 and anti-S100A9 antibodies ( Acris BM4029 and BM4027 , both at 2 . 5 µg/ml ) as described above . For analysis of abscess formation 10 anesthetized animals per group were shaved on the back and subcutaneously infected with 5×107 C . albicans . Abscesses were measured with a caliper at the indicated time points . Pulmonary candidiasis was induced by intranasal challenge of anesthetized animals with 5×107 C . albicans and systemic candidiasis by intravenous injection to the tail vein with 5×105 C . albicans in 10 animals per group . Inoculation doses were determined by CFU counts after plating . For intranasal and intravenous challenge , survival was monitored daily . For fungal load , lungs from 11 mice per group were macerated and CFU determined 3 days after intranasal challenge . For histology of pulmonary infections lungs were removed 24 h after intranasal challenge and 6 days after subcutaneous challenge abscesses were removed . Tissue samples were fixed in 2% formalin , dehydrated , embedded in paraffin , sliced to 5 µm , rehydrated and stained with hematoxylin and eosin ( H & E ) . For immunostainings , samples were rehydrated , subjected to antigen retrieval and incubated with primary antibodies directed against calprotectin subunits S100A8 and S100A9 ( produced in house ) , MPO ( DAKO A0398 ) and histone ( Santa Cruz 8030 ) . These were detected with secondary antibodies coupled to Cy2 , Cy3 or Cy5 . DNA was detected with DRAQ5™ ( Biostatus ) . For fine structural analysis of paraffin-embedded tissue samples , 5 µm sections were rehydrated , postfixed with glutaraldehyde , contrasted using repeated changes of 0 . 5% OsO4 and 0 . 05% tannic acid , dehydrated in a graded ethanol series , critical-point dried and coated with 5 nm platinum/carbon . For immunostainings with human neutrophils , 1×105 cells were seeded on 13 mm glass cover slips , stimulated with 20 nM PMA and fixed in 2% formalin at the indicated time points . Specimens were blocked with 3% cold water fish gelatin , 5% donkey serum , 1% BSA , 0 . 25% TWEEN 20 in PBS , incubated with primary antibodies directed against S100A8/A9 complex ( Acris BM4025 ) and MPO ( DAKO A0398 ) and then washed . Primary antibodies were detected with species-specific secondary antibodies and DNA with DRAQ5™ . Specimens were analyzed using a SP5 confocal microscope ( Leica ) . One-way analysis of variance ( ANOVA ) with Bonferroni post-tests was applied when multiple groups were compared and two-tailed Student's t-test was used for analysis of two groups . For non-parametrically distributed data , the two-tailed Mann-Whitney test was used . Survivals of infected mice were determined by the log-rank test . Differences were considered statistically significant at P<0 . 05 . All statistical tests were performed using GraphPad Prism version 4 . 02 .
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Neutrophils are phagocytes that disarm and kill microbes by engulfing them . Less well characterized than their phagocytic killing mechanisms is how neutrophils cope with microbes that are too large to be internalized . Notably , neutrophils may also kill or inhibit extracellularly by releasing Neutrophil Extracellular Traps ( NETs ) . NETs are fibers made of chromatin ( histones and DNA ) decorated with antimicrobial proteins . NETs ensnare and kill microbes , such as bacteria , fungi and parasites . We wanted to find out if and how NETs control pathogenic fungi that can form large filaments such as Candida albicans . We purified all NET-bound proteins and identified 24 of them . We found that calprotectin is the major antifungal NET-bound protein . Calprotectin was known to be antimicrobial but here we demonstrate that NET formation is a novel release mechanism for this cytoplasmic protein . The NET matrix comes in close contact with the fungi and the high local concentration of calprotectin in the NETs supports the antifungal activity . Furthermore , in mice calprotectin is essential for an efficient antifungal response to Candida albicans in skin , lung and systemic infections . In tissue sections from these animals we detected NETs and NET-associated calprotectin . Thus , our study gives more insights into mechanisms how the immune system copes with fungal pathogens .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"infectious",
"diseases/fungal",
"infections",
"immunology/immunity",
"to",
"infections",
"immunology/innate",
"immunity"
] |
2009
|
Neutrophil Extracellular Traps Contain Calprotectin, a Cytosolic Protein Complex Involved in Host Defense against Candida albicans
|
Genetically identical cells frequently display substantial heterogeneity in gene expression , cellular morphology and physiology . It has been suggested that by rapidly generating a subpopulation with novel phenotypic traits , phenotypic heterogeneity ( or plasticity ) accelerates the rate of adaptive evolution in populations facing extreme environmental challenges . This issue is important as cell-to-cell phenotypic heterogeneity may initiate key steps in microbial evolution of drug resistance and cancer progression . Here , we study how stochastic transitions between cellular states influence evolutionary adaptation to a stressful environment in yeast Saccharomyces cerevisiae . We developed inducible synthetic gene circuits that generate varying degrees of expression stochasticity of an antifungal resistance gene . We initiated laboratory evolutionary experiments with genotypes carrying different versions of the genetic circuit by exposing the corresponding populations to gradually increasing antifungal stress . Phenotypic heterogeneity altered the evolutionary dynamics by transforming the adaptive landscape that relates genotype to fitness . Specifically , it enhanced the adaptive value of beneficial mutations through synergism between cell-to-cell variability and genetic variation . Our work demonstrates that phenotypic heterogeneity is an evolving trait when populations face a chronic selection pressure . It shapes evolutionary trajectories at the genomic level and facilitates evolutionary rescue from a deteriorating environmental stress .
According to classical evolutionary theory , genetic variation provides the major source of heritable variation on which natural selection acts . However , many scholars argue that phenotypic heterogeneity can accelerate adaptive evolution [1–3] . Phenotypic heterogeneity can take many forms , including stochastic gene expression variability [4] , alternative protein conformations [5] , morphological plasticity [3] , cellular age-correlated phenotypic plasticity [6] , and learning throughout the lifetime of the organism [7 , 8] . The literature goes back to Baldwin’s suggestion in 1896 [7] and has recently been discussed extensively [3 , 4 , 9 , 10] . Phenotypic heterogeneity has been shown to have a beneficial effect in fluctuating environments [11–14] . However , its role in long-term adaptation has remained disputed [10 , 15 , 16] , partly due to the shortage of experimental evolutionary studies ( but see [17] ) . This issue is important , not least because phenotypic heterogeneity may facilitate evolution of microbial drug resistance [18] and cancer progression [19] . One prior study claims that the direction of phenotypic plasticity in gene expression is generally opposite to the direction of adaptive evolution [20] ( see also [15] ) . Based on prior theoretical work [21] , we hypothesized that phenotypic heterogeneity has a long-lasting impact on adaptive evolution for two possible reasons . Phenotypic heterogeneity may increase population size and hence the chance of occurrence of adaptive mutations . Alternatively , by generating individuals with exceptionally high trait values , phenotypic heterogeneity may increase the net adaptive value of beneficial mutations at an early stage of adaptation . In this work , we study phenotypic heterogeneity that arises from stochastic fluctuation in cellular states and focus on the impact of such nongenetic cellular variation under permanent challenges in a novel stressful environment . Specifically , we developed two versions of an inducible synthetic gene circuit that generate varying degrees of expression stochasticity of an antifungal resistance gene . We tested the costs and benefits of phenotypic heterogeneity under stress and nonstress conditions alike . Next , we investigated how these two genetic circuits influence evolutionary adaptation towards antifungal stress and what the underlying molecular mechanisms of adaptation might be . Specifically , we asked how the level of nongenetic cellular variation shapes mutational effects . We provide several lines of evidence that phenotypic heterogeneity promotes evolvability , partly by modulating the adaptive value of beneficial mutations .
We first developed and characterized two versions of a chromosomally integrated , inducible synthetic gene circuit that generate varying degrees of expression stochasticity of an antifungal resistance gene . The positive feedback ( PF ) gene circuit consisted of a modified version of the reverse-tetR trans-activator coding gene ( rtTA-MF ) [22 , 23] , which is under the control of a synthetic tet-inducible tetreg promoter ( PTetreg2 ) . When bound by doxycycline ( a tetracycline analog ) , the reverse-tetR trans-activator protein ( rtTAp ) activates the expression of its own gene as well as the C-terminally GFP-tagged PDR5 target gene ( PDR5-GFP; also controlled by PTetreg2 ) [22 , 24] . Many similar positive regulatory feedback loops , leading to alternative heritable phenotypes , have been described in microbes [1] . The second gene circuit ( no positive feedback [noPF] ) has a constitutively expressed rtTA-MF ( controlled by glyceraldehyde-3-phosphate dehydrogenase promoter [PGPD] ) , as it lacks the positive feedback loop ( Fig 1A ) . In both gene circuits , the rtTAp controls the expression of the bifunctional C-terminally GFP-tagged Pdr5p protein ( Pdr5p-GFP ) in S . cerevisiae . Pdr5p is a natural multidrug transporter involved in the efflux of several major antifungal drugs [25 , 26] . This bifunctional protein served as a fluorescent reporter , while simultaneously protecting cells from the antifungal agent fluconazole . Therefore , we could relate gene expression stochasticity to the corresponding variation in fitness . The native PDR5 gene was eliminated in the genomes of both strains . Evolution of resistance mechanisms in the laboratory is contingent upon PDR5 expression [25] . Flow cytometry was used to establish the distribution of the steady-state expression level of the target protein across cells from isogenic yeast cell populations carrying the PF or the noPF gene circuits , respectively . For both genotypes , the mean and the coefficient of variation ( CV ) of PDR5-GFP expressions were calculated . In agreement with a prior study [22] , genotypes carrying the PF gene circuit showed bimodal expression ( Fig 1B ) . This pattern reflects stochastic transitions between two cellular states that form distinct subpopulations , and it arises from the positive regulatory loop along with the nonlinear promoter response [22] . By contrast , the noPF gene circuit displayed unimodal expression distribution ( Fig 1B ) . By varying the level of inducer concentration ( S1 Fig ) , we found a regime where gene expression variability ( estimated by the CV of the distribution ) of the PF circuit was 150% larger ( Fig 1B ) . In contrast , the difference in the mean target gene expression level was only 5% , and it was actually higher in the noPF-carrying strain ( Fig 1B ) . Therefore , any potential evolutionary advantage of the PF gene circuit cannot be attributed to differences in mean PDR5-GFP expression between the two strains . Accordingly , the only main difference between the PF and noPF strains was the CV of the PDR5-GFP expression , making it possible to directly test the physiological and evolutionary impacts of gene expression stochasticity . The PF and noPF strains were induced by 0 . 3 μg/ml and 0 . 015 μg/ml doxycycline , respectively . Unless otherwise indicated , these inducer concentrations were used throughout the study . The switching rate between the expression states is between 10−1–10−3 per hour [22] . The PF and noPF genetic circuits were appropriately induced , and the corresponding growth patterns were investigated . The PF genotype shows enlarged expression variability , and thereby , it generated individuals with exceptionally high and low PDR5-GFP expression . Individuals with high PDR5-GFP expression are better able to cope with fluconazole stress , leading to the survival of a small subset of the population carrying the PF circuit [22] . On the other hand , production of an excess level of Pdr5p-GFP is expected to be costly in the absence of stress . In agreement with expectation , when cells were exposed to fluconazole at low dosages , the genotype carrying the PF genetic circuit was better able to propagate ( Fig 1C ) , presumably due to a subset of the cell population with exceptionally high Pdr5p-GFP level [27] . These results indicate that differences in the level of cell-to-cell variability have an impact on fitness under environmental stress [27] . In contrast , PF cells showed somewhat reduced fitness in stress-free medium ( Fig 1D ) . Along with a prior paper [28] , these results indicate a trade-off between gene expression costs and survival under stress conditions , which may shape phenotypic heterogeneity in nature . Does phenotypic heterogeneity influence genetic adaptation ? We initiated laboratory evolutionary experiments with genotypes carrying either the PF or the noPF genetic circuits ( Experiment A ) . Populations were cultivated in parallel ( 42 replicate populations per genotype ) : 10% of each culture was diluted into fresh medium every 72 h , and populations were allowed to evolve to progressively higher fluconazole stress for approximately 120 generations . Starting with a subinhibitory fluconazole concentration ( 8 μg/ml ) , the dosage was increased gradually ( 1 . 5 times the previous dosage ) at every second transfer ( S2A Fig ) . Several populations became extinct ( i . e . , showed no detectable growth after transfer ) , while others reached clinically significant levels of fluconazole resistance ( up to 224 μg/ml ) during the course of laboratory evolution ( Fig 2A ) . Evolution of resistance caused an 11%–16% fitness deficit in drug-free medium ( Fig 1D ) . Prior work [25] suggests that stepwise increase of fluconazole concentrations promotes the evolution of resistance mechanisms , which are contingent upon PDR5 expression . Therefore , stochastic expression variation in this gene is expected to influence evolutionary processes . This was indeed the case ( Fig 2A ) : 32% of the noPF populations died out during the course of laboratory evolution , while the same figure was as low as 12% for PF populations . These results indicate that phenotypic heterogeneity had a major impact on extinction patterns in evolving populations facing fluconazole stress . To confirm that phenotypic heterogeneity accelerates evolutionary adaptation , we initiated a new round of laboratory evolution with slightly modified experimental settings ( Experiment B ) . Instead of measuring extinction rate , this protocol aimed to maximize the level of fluconazole resistance in the evolving population reached during a fixed time period ( see Materials and methods , S2B Fig ) . Fig 2B shows the distribution of minimum inhibitory concentration ( MIC ) values in ten parallel evolved noPF and ten parallel evolved PF strains , respectively . We found that PF populations reached significantly higher fluconazole resistance during the course of laboratory evolution ( Fig 2B and S3 Fig ) . This result does not simply reflect the initial difference in fluconazole susceptibilities between the PF and noPF genotypes , as the rate of adaptation ( measured by relative increase in MIC levels ) also differed: after only 110 generations , the PF populations reached an average 12-fold increase in MIC level relative to their ancestor , while the same figure was 8-fold in the case of noPF populations ( Fig 2B ) . Phenotypic heterogeneity may increase the rate of adaptation in two fundamentally different ways . It may elevate the rate by which beneficial mutations arise in the population ( mutational supply theory ) . Indeed , the product of beneficial mutation rate and effective population size determines mutation supply rate in the population . A second potential mechanism to increase adaptation leaves population size and mutation rate unchanged but increases the beneficial effects of mutant alleles ( mutational effect theory ) . Here , we investigate the first possibility . One may argue that by increasing fitness , phenotypic heterogeneity increases population size and thereby the probability that a beneficial mutation will arise in the population . However , there was no significant difference in population size between evolving PF and noPF lineages ( S4 Fig ) . Moreover , we failed to find evidence that the specific promoters underlying the PF genetic circuit would generate a local mutational hot spot ( S1 Table ) , not least because the evolved PF strains did not accumulate an especially high number of mutations in the genetic circuit , in the Pdr5p coding region , or elsewhere in the genome ( S2 Table ) . Similarly , there is no evidence that PF strains would have an especially high genomic mutation rate ( S5 Fig ) . These results indicate that despite similar population densities and mutation rate , PF populations evolve more rapidly to fluconazole stress ( Fig 2B ) . We finally asked how elevated mutational supply affects the outcome of laboratory evolution . As controlling population size is cumbersome and can potentially alter selection pressure , we manipulated genomic mutation rate . Briefly , a mismatch-repair gene ( MSH2 ) was inactivated in the PF and noPF strains , respectively , leading to an approximately 10-fold increase in genomic mutation rate , in accordance with previous works [29 , 30] . We initiated laboratory evolution with the mutator and nonmutator strains , as described previously ( Experiment B , Fig 2B ) . As expected [31] , mutator strains ( Δmsh2 ) reached higher levels of fluconazole resistance than the corresponding nonmutators that carried the same genetic circuit ( PF or noPF , respectively ) . More surprisingly , the level of resistance in the evolved noPF mutator strains was consistently lower than that in the evolved PF nonmutator strains ( Fig 2B ) . This suggests that despite massive increase in mutational supply , the genotype with low phenotypic heterogeneity has an intrinsic disadvantage during evolutionary adaptation . We conclude that the observed low adaptation rate under low phenotypic heterogeneity cannot be explained by shortage of mutational supply only . The above results demonstrated that phenotypic heterogeneity promotes adaptation rate under prolonged exposure to fluconazole stress . Under the assumption that evolution favors individuals with exceptionally high trait values , one might also expect evolution of elevated phenotypic heterogeneity as a secondary response . Specifically , the selection pressure towards broader Pdr5p-GFP distribution should be especially strong when the level of phenotypic heterogeneity is initially low , such as in noPF strains . Moreover , the mean PDR5-GFP expression should also increase . This was indeed so . We isolated single clones from 27 independently evolved noPF and 27 independently evolved PF strains , respectively , each from the final day of the evolutionary experiments ( Experiment A ) . The distribution of the target protein expression level was estimated in these strains and their corresponding ancestors . The CV showed an average 54% increment of the initial value in the evolved noPF strains , while only a very modest 7% change was found in the evolved PF strains ( Fig 2C ) . Similar trends were observed for changes in the mean expression level: no significant changes were detected in the evolved PF strains , while the noPF strains showed an average 17% increment ( Fig 2C ) . In the evolved noPF strains , the distribution of Pdr5p-GFP fluorescence deviated significantly from normal distribution ( Fig 2D and S6B Fig ) , and , in at least some cases , it appeared to be bimodal ( S6A Fig ) . These results indicate that under strong directional selection for exceptionally high Pdr5p-GFP level , both the mean and the breadth of expression evolve . For molecular underpinnings of these alterations , see S1 Table . Phenotypic heterogeneity may enlarge the phenotypic effects of mutations [32] and consequently increase the set of adaptive mutations that provide resistance above a critical fluconazole dosage . This prediction was first investigated by eliminating the positive regulatory feedback loop in three randomly selected , independently evolved strains from the final day of the experiments , all of which carried the PF genetic circuit ( Fig 3A ) . Specifically , the promoter controlling the rtTA-MF gene was swapped for the corresponding promoter present in the ancestor noPF strain . The same procedure ( promoter swap ) was done with the corresponding ancestor PF strain . Elimination of the positive feedback loop left the mean PDR5-GFP expression unchanged but substantially reduced expression variability ( i . e . , the CV of expression , Fig 3A ) . As a result , fluconazole resistance level in the PF ancestor—estimated by minimum inhibitory concentrations—showed a relatively minor 2-fold reduction . By contrast , the same promoter-swap procedure in the evolved PF strains caused a 4 . 2–11-fold reduction in resistance level ( Fig 3B ) . As the drop of resistance due to promoter swap was especially high in the evolved strains , we conclude that phenotypic heterogeneity has a large impact on the fitness effects of adaptive mutations that accumulated during the course of laboratory evolution . Next , we tested whether the advantage of phenotypic heterogeneity remains after controlling for differences in the initial fluconazole susceptibilities between PF and noPF . Careful adjustment of the inducer concentration ( Fig 4A ) in the noPF strain ensured that fluconazole resistance levels in the ancestor PF ( high phenotypic heterogeneity [HH] ) and noPF ( adjusted expression level [AE] ) strains are the same . ( Fig 4B ) . In the evolved ( EV ) strains ( EV-1 , EV-2 , and EV-3; Fig 4B ) , however , resistance level of the HH setting was somewhat higher than that of AE . This indicates that the advantage of high phenotypic heterogeneity remains when the PF and noPF have the same starting fluconazole susceptibilities . Finally , we note that high phenotypic heterogeneity is beneficial over low heterogeneity , high expression level ( HE ) setting in certain evolved strains ( for details , see Fig 4B ) . To gain insight into the molecular mechanisms of evolved resistance and its dependence on phenotypic heterogeneity , we randomly selected four independently evolved noPF and four independently evolved PF strains , respectively , from the final day of the Experiment A . Single clones of these strains were subjected to whole-genome sequencing using the Illumina Nextera XT protocol . As no large-scale genomic rearrangements were detected , we focused on analyzing point mutations only . In total , we observed 38 mutational events , 95% of which occurred in protein-coding regions ( Fig 5A and S2 Table ) . Several observations indicate that the mutation accumulation in the protein-coding regions was largely driven by selection . More than eighty percent ( 83 . 3% ) of these mutations were nonsynonymous , and many of them were found in genes with established links to fluconazole resistance ( Fig 5B ) . Consistent with the hypothesis that fungicidal drugs ( including azoles ) induce oxidative damage [33] , proteins with mitochondria- and respiration-related functions were frequently mutated . More generally , we found significant gene-level convergent evolution . As high as 22% of the mutated genes were shared by at least two strains , and some were shared extensively ( Fig 5B ) . However , none of the mutations in different strains were identical at the nucleotide level , confirming that they accumulated independently of each other during the course of laboratory evolution . Repeatedly mutated genes include the natural multidrug transporter ( PDR5 , carried on the synthetic genetic circuit ) , sterol oxidase ( ERG25 ) , and a transcriptional regulator ( ROX1 ) of ergosterol biosynthesis and respiration . The rarity of mutations in the molecular target of fluconazole ( ERG11 ) under fluconazole stress has been observed previously [25] and may reflect high associated fitness cost of ERG11 mutations or the efficiency of alternative resistance mechanisms . To further investigate the impact of phenotypic heterogeneity on mutational effects , we focused on the multidrug transporter PDR5 , not least because this gene was mutated in all of the sequenced strains . A randomly selected nonsynonymous mutation—observed in one of the evolved PF strains—was inserted individually into the ancestor strains ( see Materials and methods ) with PF and noPF genetic backgrounds , respectively ( Fig 5C ) . The results confirmed the outcome of the promoter-swap experiments ( Fig 3B ) . The mutation conferred a highly significant decline in fluconazole susceptibility when phenotypic heterogeneity was high , but its beneficial effect was substantially reduced otherwise ( Fig 5C ) . Additional analyses confirmed that the effects of genomic mutations on resistance level were contingent on phenotypic heterogeneity ( Figs 4B and 5D ) . The above analysis leaves open the question of what the long-term advantage of phenotypic heterogeneity might be over mutations that simply provide a shift towards higher mean expression level . The key to this problem lies in the observation that high expression of a drug-resistance gene provides resistance , but it induces an especially high fitness cost in nonstressed conditions [28] . We hypothesized that the ultimate fate of elevated phenotypic heterogeneity should reflect a fundamental trade-off between the level of resistance and the fitness cost of resistance: compared to constitutively high expression level , phenotypic heterogeneity may dampen fitness costs when the level of fluconazole stress is relatively mild . To investigate this issue experimentally , we focused on the ancestor and three randomly selected , independently evolved strains , all of which were carrying the PF genetic circuit ( EV-1 , EV-2 and EV-3 ) . Pdr5p-GFP fluorescence distribution in all three strains remained unchanged during the course of laboratory evolution ( S7 Fig ) . We investigated how modulation of PDR5-GFP mean expression level and simultaneous removal of gene expression stochasticity affect fitness under a wide range of fluconazole dosages . Careful adjustment of the inducer level allowed us to generate expression settings with low phenotypic heterogeneity but exceptionally high and low mean PDR5-GFP expression levels , respectively . The derived expression patterns were comparable to the low and high peaks of the PF strain , respectively ( S8 Fig ) . We compared the fitness of each strain under low expression level ( LE ) , high expression level ( HE ) , and the original HH settings . This setup controls for the differences in the mutations that accumulated during the course of evolution and ensures direct comparison of the impact of PDR5-GFP expression patterns . In the absence of fluconazole , the fitness in the HH setting was 28%–40% higher than the HE fitness , but 8%–52% lower than the LE fitness ( Fig 6A ) . This indicates that selection ultimately favors low PDR5-GFP expression level ( i . e . , LE ) in the absence of stress . The situation was more complex under fluconazole stress . Under a wide-range of fluconazole dosages , fitness in HH setting was higher than the corresponding HE and the LE fitnesses ( Fig 6B ) . Remarkably , HH fitness was statistically higher or equal to the fitness in HE , even at the fluconazole dosage deployed during the final stage of laboratory evolution ( 224 μg/ml ) . As expected , fitness in LE was generally very low under most fluconazole dosages investigated ( Fig 6B ) . Taken together , phenotypic heterogeneity appears to be favorable under a wide range of antifungal stress level compared to HE and LE settings . We suspect that this reflects an intricate balance between the level of resistance conferred and the fitness cost of resistance-bearing mutations . We will address this important issue in more detail in a future work .
Several studies indicate that phenotypic heterogeneity facilitates survival in dynamically changing environments , and promotes interactions and division of labor between individual cells [34] . It has also been suggested that stochastically generated phenotypes precede genetic changes and thereby facilitate rapid evolution of complex phenotypes [3 , 35] . This issue remained controversial due to the shortage of comprehensive tests from natural and experimental populations [35] . In this work , we combined synthetic biology , single-cell monitoring , and experimental evolution to explore the impact of phenotypic heterogeneity on evolvability . We developed two versions of an inducible synthetic gene circuit that generate varying degrees of expression stochasticity of an antifungal resistance gene . The following main conclusions were reached . First , elevated phenotypic heterogeneity increased population survival under mild antifungal stress ( Fig 1C ) , but , at the same time , it substantially reduced fitness in stress-free medium ( Fig 1D ) . These results indicate a trade-off between gene expression costs and survival under stress conditions , which may shape phenotypic heterogeneity in nature . Second , phenotypic heterogeneity promoted adaptation towards increasing levels of antifungal stress . Populations with high phenotypic heterogeneity reached a higher level of resistance ( Fig 2B ) and were less likely to become extinct during the course of laboratory evolution ( Fig 2A ) . Most importantly , we found no evidence that higher adaptation would reflect elevated local ( S1 Table ) or global ( Fig 5A and S5 Fig ) mutation rate associated with the PF genetic circuit . Third , the phenotypic effects of the mutations that accumulated during the course of laboratory evolution were contingent on phenotypic heterogeneity ( Figs 3B and 5C ) . This result suggests that phenotypic heterogeneity may enlarge the set of adaptive mutations that provide resistance above a critical stress level . Finally , compared to constitutively high expression , phenotypic heterogeneity alleviated the fitness costs of target protein expression under a wide range of stress conditions . In sum , our study demonstrates that phenotypic heterogeneity promotes evolvability , partly by modulating the adaptive value of beneficial mutations . Several important predictions emerged from our study . First , phenotypic heterogeneity should be especially favorable in deteriorating conditions . Indeed , the extent of phenotypic heterogeneity in wild-type yeast isolates is highest in stressful environments [36] , indicating that heterogeneity facilitates adaptation to adverse conditions in the wild . Previous studies also indicate that genes are expressed when they are not needed for fitness [37] . Our work gives credit to the idea that many genes expressed in a certain fraction of the microbial populations are in a “standby mode” , and thereby help survival upon environmental change [38] . Second , our work focused on the evolutionary consequences of phenotypic heterogeneity and left open the issue of why phenotypic heterogeneity exists in nature . Phenotypic heterogeneity could generally be a by-product of the unavoidable imprecision of molecular processes . We note , however , that phenotypic heterogeneity can readily change in the laboratory ( Fig 2C , see also [17] ) . Therefore , different forms of phenotypic heterogeneity in nature may evolve as a direct response to novel and extreme environmental challenges [39] . Our study also suggests that cell-to-cell phenotypic heterogeneity could initiate key steps of microbial drug resistance , for example by promoting fluctuations of protein concentrations in efflux pumps [40] . An important unresolved issue concerns the molecular mechanisms whereby gene expression noise shapes the beneficial effects of mutations in the protein ( Fig 5C and 5D ) . This pattern may reflect a trade-off between protein stability and improved protein activity , as suggested previously [41] . Finally , future studies should reveal the extent by which phenotypic heterogeneity facilitates adaptive search for rare combinations of beneficial mutations , as seen in the case of key evolutionary innovations [10] .
To detect the level of Pdr5p ( YOR153W , S000005679 ) , the C-terminally GFP-tagged PDR5 region ( PDR5-GFP ) was obtained from the available GFP collection [42] ( Open BioSystem ) . The GFP-tagged Pdr5p remained fully functional . The PDR5-GFP was amplified by PCR using PDR5-BamHI-f and neGFP-XhoI-r primers . The resulting fragment was digested in two sequential reactions , as follows . First , it was divided into short upstream and long downstream fragments by using AflII and XhoI restriction enzymes . In the second step , the short upstream fragment was also digested with BamHI enzyme . Afterwards , both the short upstream and the long downstream fragments were inserted into a BamHI-XhoI digested pDN-T2dGZmxh plasmid [22] . This resulted in the pDN-T2dPGxh reporter plasmid that contains the PDR5-GFP gene downstream of PTetreg2 , a modified version of PCYC1 containing two tetO2 sites . All cloning procedures were performed in Escherichia coli XL-10 Gold strain ( Stratagene , La Jolla , CA ) , using ampicillin as selection marker ( Sigma , St . Louis , MO ) . The inserted regions were verified by sequencing with double coverage . Sequences and description of the oligonucleotides used for strain construction can be found in S3 Table . The regulatory plasmids ( pDN-T2dMFot and pDN-GPMFot , for more details , see [22] ) carry a modified version of the rtTA-MF that contains three minimal VP16 activator F domains . The expression of the rtTA-MF is either controlled by a synthetic tet-inducible tetreg promoter ( PTetreg2 in pDN-T2dMFot ) or by a constitutive glyceraldehyde-3-phosphate dehydrogenase promoter ( PGPD in pDN-GPMFot ) . When the rtTAp is bound by an externally added inducer ( doxycycline , Biochemica ) , it activates transcription by binding to the tetO2 sites in PTetreg2 . All strains used in this study were derived from the YPH500 S . cerevisiae parental strain ( α , ura3-52 , lys2-801 , ade2-101 , trp1Δ63 , his3Δ200 , leu2Δ1 ) and were generated by yeast transformation using the standard lithium acetate procedure [43] . First , the pDN-T2dPGxh reporter plasmid ( containing the HIS3 selectable marker ) was linearized with AfeI restriction enzyme and integrated into the his3Δ200 locus of the YPH500 strain . As a result , an intermediate yeast strain , YDN-T2dPGxh was constructed . Transformants were selected on histidine drop-out synthetic medium ( SC-His , 5 g/l ammonium sulfate , 1 . 7 g/l Yeast Nitrogen Base , supplemented by an amino acid mix without histidine ) . Second , the regulatory plasmids ( pDN-T2dMFot and pDN-GPMFot , for more details , see [22] ) , containing the TRP1 selectable marker , were linearized with AhdI restriction enzyme and integrated into the genome of the intermediate YDN-T2dPGxh yeast strain by using the ampR locus of the reporter construct for targeting . Transformants were selected on tryptophan drop-out synthetic medium ( SC-Trp ) . As a result , YDN-T2dPGxh-T2dMFot ( PF ) and YDN-T2dPGxh-GPMFot ( noPF ) yeast strains were generated , respectively . Only strains with single genomic integrations of the constructs ( verified by PCR ) were used in this study . In the PF strain , the rtTA-MF is expressed from the same PTetreg2 as the PDR5-GFP gene . Therefore , the produced rtTAp induces the transcription of the PDR5-GFP and its own transcription , alike . This positive feedback loop generates high expression stochasticity of the PDR5-GFP . In the noPF strain , the same rtTA-MF is controlled by PGPD . Owing to absence of the positive feedback loop , the PDR5-GFP has low gene expression stochasticity . Finally , the native PDR5 gene was deleted from the genome of constructed PF and noPF strains as follows . The PDR5::KanMX deletion cassette was amplified with longer than 0 . 3 kb flanking region using the genomic DNA of the Δpdr5 strain from the YKO Mat a collection [44] ( Open Biosystem ) . This deletion cassette was transformed into the parental PF and noPF strains , and the correct transformants were selected on histidine and tryptophan drop-out synthetic medium ( SC , 5 g/l ammonium sulfate , 1 . 7 g/l Yeast Nitrogen Base , supplemented by amino acid mix ) , containing G418 ( Roche ) selection drug at 200 mg/l final concentration . Mutator strains were generated by inactivating a mismatch-repair gene ( MSH2 , S000005450 ) in PF and noPF strains , respectively . Briefly , the MSH2::NatMX deletion cassette was amplified using the genomic DNA of the Δmsh2 strain from the SGA ( Synthetic Genetic Array ) query collection [45] . This deletion cassette was transformed into the PF and noPF strains , and the correct transformants were selected on SC–His/Trp containing nourseothricin ( clonNAT , WERNER BioAgents ) selection drug at 100 mg/l final concentration . An established flow cytometry protocol [22] was used to measure the distribution of steady-state gene expression level across cells from isogenic yeast cell populations . Briefly , single yeast colonies were picked from agar plates and incubated in synthetic drop-out medium supplemented with 2% glucose at 30°C . After reaching stationary phase , 1% of the population were serially transferred into fresh synthetic drop-out medium containing galactose as carbon source , supplemented with appropriate inducer concentrations: PF and noPF strains were induced by 0 . 3 μg/ml and 0 . 015 μg/ml doxycycline , respectively . After reaching stationary phase , 1% of the populations were serially transferred to fresh induction medium ( synthetic drop-out medium with 2% galactose and inducer ) . The populations were grown until the stabilization of gene expression distributions ( approximately 20 h ) . Cell suspensions were diluted to approximately 5 × 102 cells/ml , and fluorescence intensity values ( logarithmic scale ) were estimated by a Guava flow cytometer . Gating was applied based on forward scatter data ( logarithmic scale ) , to exclude extrinsic noise . During each run , a minimum of 5 , 000 events were recorded . The log10-scale fluorescence level of the cells was normalized to log10-scale forward scatter data ( i . e . , cell size ) . Phenotypic heterogeneity ( CV ) was computed for each population as the standard deviation normalized by the mean of fluorescence . Two complementary experiments were used to study the impact of phenotypic heterogeneity on adaptive evolution . Experiment A measured the extinction rate of the evolving populations as a function of gradually increasing fluconazole ( Molekula ) dosage , while experiment B aimed to maximize fluconazole resistance increment during a fixed time period . Experiment A was conducted in 96-well plates , using 42 independent populations of noPF and 42 independent populations of PF strains , respectively ( S2A Fig ) . The populations were subjected to parallel laboratory evolution in histidine and tryptophan drop-out synthetic medium ( SC-Trp/His ) containing doxycycline to induce the synthetic genetic circuits in 96-well deep-well plates ( 0 . 5 ml , polypropylene , V-bottom ) . Plates were covered with sandwich covers ( Enzyscreeen BV ) to ensure an optimal oxygen exchange rate and limit evaporation , shaken at 280 rpm , and incubated at 30°C . Ten percent of the populations were serially transferred into 350 μl of fresh medium every 72 h . The relatively long time period between transfers and the increased medium volume ( i . e . , increased population size ) was necessary , as growth rates of the evolving populations were low at high fluconazole dosages . Starting at a subinhibitory fluconazole concentration ( 8 μg/ml ) , fluconazole dosage was increased gradually at every second transfer . The applied dosages were as follows: 0 , 8 , 16 , 24 , 32 , 64 , 96 , 128 , 160 , 192 , and finally 224 μg/ml . Samples from each time interval , including time-zero , were frozen in 15% glycerol and kept at −80°C until fitness measurement or further analysis . Cross-contamination events were regularly checked by visual inspection of blank wells containing only medium . Doxycycline and fluconazole stock solutions were made fresh before the transfer by dissolving powder stocks in specified solvents by the manufacturer’s instructions . Population extinction was defined by a cutoff OD600 = 0 . 15 after 72 h of cultivation . Evolved strains from the final day of experiment A were used for further genomic and functional analyses . Experiment B followed the protocol of an established automated evolution experiment [46] . Ten independent populations each of both noPF and PF strains were propagated in parallel , in the same medium and culturing conditions as in Experiment A . A checker board layout was used on the plate to monitor cross-contamination events ( S2B Fig ) . Starting with the subinhibitory drug concentration , each culture was allowed to grow for 72 h . Twenty μl of culture was transferred to four independent wells containing fresh medium and increasing dosages of fluconazole ( 0 . 5x , 1x , 1 . 5x , and 2 . 5x the concentration of the previous step ) . At each transfer , cell growth was monitored by measuring the optical density at 600 nm ( OD600 value , Biotek Synergy 2 microplate reader was used for this purpose ) . Only populations with ( a ) vigorous growth ( i . e . , OD600 > 0 . 2 ) and ( b ) the highest drug concentration were selected for further evolution . Accordingly , only one of the four populations was retained for each independently evolving strain . This protocol was designed to avoid population extinction and to ensure that populations with the highest level of resistance were propagated further during evolution . To estimate population size from raw OD600 values , an established protocol [47] was used to correct for the nonlinearity of OD measurements in high-density cultures . Corrected OD values were then converted to cell number according to established yeast protocols ( i . e . , OD600 = 1 is equal to 3 × 107 cells/ml , [48] ) . CanR ( canavanine resistance ) spontaneous mutation rate was estimated by performing a standard fluctuation assay , as previously described [49] . Briefly , a stationary culture inoculated from a single colony was diluted to an approximately 102-cells/ml density and separated into six independent cultures . The independent cultures were incubated until early stationary phase then appropriate dilutions were spread onto nonselective YPD solid medium as well as SC arginine-dropout solid medium containing 60 mg/liter canavanine . After 3 days of incubation at 30°C , colonies were counted . The mutation rate was calculated using the Lea-Coulson Method of the Median [50] . The calculations were performed using the FALCOR web tool [51] . MIC values were determined using a standard linear broth dilution technique [52] in 96‐well microtiter plates ( Sarstedt ) . Approximately 104 to 105 yeast cells were inoculated into each well of the 96-well microtiter plates ( containing varying concentrations of fluconazole ) with a 96‐pin replicator ( VP407 , V&P Scientific ) and were propagated at 30°C shaken at 280 rpm . After 72 h of incubation , raw OD600 values were measured in a Biotek Synergy 2 microplate reader ( BioTek Instruments ) . MIC was defined by a cutoff OD600 value ( 0 . 15 ) . Established protocols [53] were used to measure the fitness of the yeast populations without fluconazole stress . Growth was assayed by monitoring the OD600 value of liquid cultures of each strain using 384-well density microtiter plates . The prestarter cultures were inoculated from frozen samples into a medium containing 2% glucose using a VP407 replicator ( V&P Scientific ) . The prestarter plates were incubated at 30°C in a rotary shaker . After reaching stationary phase , the prestarter plates were replicated into a medium containing 2% galactose and appropriate concentrations of doxycycline ( Biochemica ) . After reaching the stationary phase , 384-well density plates—filled with 60 μl inducing SC–His/Trp medium per well—were inoculated for growth curve recording using a pin tool with 1 . 58 mm floating pins . The pin tool was moved by a Microlab Starlet liquid handling workstation ( Hamilton Bonaduz AG ) to provide uniform inocula across all samples . Cultures were incubated at 30°C in an STX44 19 ( LiCONiC AG ) automated incubator with shaking speed alternating every minute between 1 , 000 rpm and 1 , 200 rpm . Plates were transferred by a Microlab Swap 420 robotic arm ( Hamilton Bonaduz AG ) to Powerwave XS2 plate readers ( BioTek Instruments ) every 20 min , and cell growth was followed by recording the OD600 value . To estimate fitness , the increment of the OD600 was calculated from the obtained growth curves , following established procedures [54 , 55]: the average of the initial five OD600 values was subtracted from the last OD600 value of the corresponding curve . All values were blank corrected , OD calibrated , and smoothed by averaging and by removing negative slopes . To determine the impact of gene expression stochasticity on the acquired resistance of the evolved PF strains , the promoter that controls the expression of PDR5-GFP was cross-swapped with the promoter of the noPF strain . A double-joint PCR method [56] was applied for this purpose . First , a selectable auxotrophic marker ( CaUra3 , component M ) was amplified by PCR with chimeric primers that contain complementary overhangs to the flanking regions of the promoter . The 5′ and 3′ flanking regions of the rtTA-MF transactivator gene promoters ( component 5′ flanking and component 3′ flanking , respectively ) were amplified and joined to the selectable marker by nested PCR . The joint cassettes were transformed into the evolved strains , and transformants were selected on uracil drop-out synthetic medium . The correct transformants were confirmed by PCR using primers designed to the flanking regions . The consequent change in gene expression stochasticity was confirmed by flow cytometry , as described above . Sequences and descriptions of the oligonucleotides used for promoter-swap can be found in S4 Table . To reveal the underlying molecular mechanisms of acquired resistance , four independently evolved PF and four independently evolved noPF strains were subjected to whole-genome sequencing , respectively . Single clones were picked and isolated by streaking the evolved populations onto solid medium . Fitness increase and Pdr5p-GFP fluorescence distribution of the clones were retested after isolation . Only confirmed representatives of the corresponding evolved populations were further investigated . Genomic DNA was prepared using a glass bead lysis protocol: clones were inoculated into 2 ml SC–His/Trp and grown to saturation at 30°C . Cells were pelleted and resuspended in 500 μl of lysis buffer ( 1% SDS , 50 mM EDTA , 100 mM Tris pH 8 ) . Cells were mechanically disrupted by vortexing for 3 min at high speed with 500 μl glass bead ( 500 μm , acid washed ) . The proteins and other contaminants are precipitated from the crude cell lysate using high concentrations of ammonium acetate ( 275 μl 7 M ammonium acetate , final concentration: 2 . 5 M ) . This salting-out step was performed at 65°C for 5 min . To separate the nucleic acids from the contaminants , an organic extraction method was used . After cooling the samples for 5 min , an equivalent volume of chloroform:isoamyl-alcohol ( 24:1 ) was added . After centrifugation for 10 min , the nucleic acid containing aqueous layer was transferred into a new tube and precipitated with an equivalent volume of 1 ml isopropanol . The nucleic acid was pelleted and washed with 70% ethanol and resuspended in 500 μl RNaseA solution ( 50 ng/ml ) . After 30 min RNaseA treatment at room temperature , samples were chloroform:isoamyl-alcohol ( 24:1 ) extracted , precipitated with 50 μl sodium acetate ( 3 M pH 5 . 2 ) and 1 , 250 μl ethanol , pelleted , and washed with 70% ethanol . Finally , the genomic DNA ( gDNA ) was dissolved in 10 mM Tris-HCl ( pH 8–8 . 5 ) . Genomic DNA was quantified using a Qubit ( Invitrogen ) 2 . 0 fluorometer with a Qubit dsDNA HS Assay . Starting from the recommended 1 ng of gDNA , the samples were processed according to Illumina Nextera XT protocol , with the exception that the bead-normalization step was replaced by library quantification using a qPCR assay KAPA Library Quantification kit . Library fragment distribution was analyzed by Bioanalyzer ( Agilent ) using a High Sensitivity DNA chip . All libraries were normalized to 4 nM and pooled . The resulting final library was denatured and diluted before loading on a MiSeq cartridge ( Illumina MiSeq Reagent Kit v3 ) for a paired-end 2 x 300 bp run ( 90x coverage ) . Raw sequencing data can be obtained from the European Nucleotide Archive ( ENA ) with the following accession number: PRJEB9425 ( URL: http://www . ebi . ac . uk/ena/data/view/PRJEB9425 ) . Raw sequence data of paired end reads were filtered according to length and read quality . The reads were trimmed by removing consecutive bases on both 5′ and 3′ flanks with base quality less than 20 . Thereafter , reads were filtered for length ( minimum of 50 bp ) , ambiguity ( N bases content <5% ) , and Average Quality Filter ( minimum of 20 ) . All reads not complying with these criteria were discarded . Remaining paired reads were then mapped to the reference S . cerevisiae genome obtained from the Saccharomyces Genome Database , using the BWA Read Mapper application [57] . All mapping options of BWA were set using default values . The GATK pipeline [58] , with default parameters , was used for indel calling and to realign reads around indels . After mapping , another stage of reads filtering was performed to remove reads that interfered with the accuracy of the final results . Thus , unmapped , improperly aligned , and duplicated reads were removed using SAMtools [59] . In addition , reads with low mapping quality ( less than 20 ) and reads with less than 95% of sequence identity to the reference were also discarded . Finally , reads with low or high insert size were filtered as well . To define both the lower and higher thresholds for which the value of an insert size should stand , the mean and standard deviation of that value for all the remaining read pairs was calculated , and reads with lower or higher insert size than the mean minus/plus the standard deviation were discarded , respectively . This process was repeated for each sample . After the reads mapping and filtering process , variant calling was performed to identify single-nucleotide polymorphisms ( SNPs ) and copy-number variations ( CNVs ) . Identification of potential CNVs was detected using the CNV-seq application and default parameters [60]: no large-scale CNV was observed in the evolved strains relative to their corresponding ancestors . For the identification of SNPs , the SAMtools Variant Calling application was used [59] . All the Genotype Likelihood options and filter options provided by the application were set using the default values . SAMtools computes the likelihood of having a SNP in each genomic position . After SNP calling , genomic regions not well covered by the reference assembly were identified , and SNPs detected in those regions were discarded ( e . g . , fewer than ten reads ) . Additionally , SNPs positioned in repeated genomic regions , as determined by RepeatMasker [61] using default parameters and the reference genome , were also discarded from the final dataset . Up to this point , the SNP calling process was performed in order to find variations regarding the genome reference used in the mapping process . Last , the SNP list of the evolved and ancestor strains were compared to identify mutations that accumulated during the course of laboratory evolution . Genomic single-nucleotide polymorphisms with less than 100 variant quality score ( Phred-scaled ) or lower than 0 . 9 mutant/reference ratio were ignored . A mutation in PDR5 conferring His595Asp amino acid change was generated by CRISPR-Cas9–mediated genome engineering , using an established workflow [62] . The targeting CRISPR plasmid was constructed by replacing the CAN1 targeting gRNA construct on p426-SNR52p-gRNA . CAN1 . Y-SUP4t with the corresponding PDR5-specific gRNA sequence by whole-plasmid amplification and subsequent ligation . Briefly , primers carrying the target region of His595 of the PDR5 gene were used to PCR amplify the CRISPR plasmid . Subsequent recircularization of the PCR product produced the desired PDR5-targeting construct , containing a yeast selectable marker ( URA3 ) . Correct clones were verified by colony-PCR and sequencing with pYEST_frw and pYEST_rev primers . dsDNA donor cassette , carrying the desired SNV ( single nucleotide variation ) , was generated by annealing equimolar amounts of single-stranded oligonucleotides ( H595DF and H595DR , all purchased from Integrated DNA Technologies ) by first denaturing the mixture of the two complementary strands in 50 mM NaCl at 95°C for 5 min and then allowing the samples to cool down to room temperature for 2 h . Introduction of the desired SNP was carried out in two steps . First , a Cas9 expression plasmid ( p415-GalL-Cas9-CYC1t ) was transformed into the corresponding ancestral strains using a standard transformation method [43] . Transformants were selected on leucine drop-out synthetic complete medium . To induce Cas9 expression , cells carrying p415-GalL-Cas9-CYC1t were grown until saturation in 10 ml selection medium , containing 1% raffinose as carbon source and 2% galactose as inducer , then diluted to OD600 = 0 . 3 in the same medium . Cells from the exponential phase were used for electrocompetent cell preparation [63] for the second step . The CRISPR-Cas9–stimulated homologous recombination was carried out by the coelectroporation of the corresponding p426-SNR52p-gRNA-SUP4t plasmid ( 300 ng ) and SNV-incorporating donor dsDNA ( 50 μg ) into electrocompetent cells expressing Cas9 . Electroporated cells were then transferred from each cuvette into 8 ml of a 1:1 mix of 1M sorbitol/YPD medium and allowed to recover at 30°C for 1 h . The cells were then diluted 100-fold and inoculated into selection medium ( leucine and uracil drop-out SC medium ) containing 1% raffinose and 2% galactose and incubated at 30°C on a rotary shaker ( 280 rpm ) for 48 h . Upon reaching stationary phase , cells were subcultured twice in the same medium as before . Cells were then diluted and plated onto solid selection plates and allowed to grow at 30°C until colonies appeared . Correct clones were verified by yeast allele-specific colony-PCR . PCR products were assayed by agarose gel electrophoresis . Mutations were also confirmed by capillary sequencing of the corresponding PDR5 genomic region . Sequences and descriptions of the oligonucleotides used in in CRISPR-Cas9–mediated allele replacement can be found in S5 Table .
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Phenotypic heterogeneity of genetically identical cells can generate nonheritable variation in a population . Is this heterogeneity favorable for microbes ? In a changing environment , the answer is a definite yes . While scholars have argued that stochastically generated variation precedes genetic changes and thereby facilitate the evolution of complex traits , this idea has remained disputed , not least because of the shortage of experimental studies . We address this long-standing and controversial issue by integrating synthetic biology , laboratory experimental evolution , and genomic analyses . We explicitly tested the mechanisms whereby phenotypic heterogeneity may promote evolvability . Our work demonstrates that phenotypic heterogeneity facilitates evolutionary rescue from deteriorating environmental stress by generating individuals with exceptionally high fitness . Remarkably , elevated phenotypic heterogeneity evolves as a direct response to stress and thereby it promotes evolution of rare combinations of mutations . These results indicate that phenotypic heterogeneity might have an important role in the evolution of key innovations .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"methods"
] |
[
"organismal",
"evolution",
"medicine",
"and",
"health",
"sciences",
"microbiology",
"mutation",
"fungal",
"evolution",
"microbial",
"evolution",
"evolutionary",
"adaptation",
"pharmacology",
"mycology",
"antimicrobial",
"resistance",
"gene",
"expression",
"evolutionary",
"genetics",
"phenotypes",
"genetics",
"microbial",
"control",
"biology",
"and",
"life",
"sciences",
"evolutionary",
"biology",
"evolutionary",
"processes"
] |
2017
|
Phenotypic heterogeneity promotes adaptive evolution
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Most cartilaginous tumors are formed during skeletal development in locations adjacent to growth plates , suggesting that they arise from disordered endochondral bone growth . Fibroblast growth factor receptor ( FGFR ) 3 signaling plays essential roles in this process; however , the role of FGFR3 in cartilaginous tumorigenesis is not known . In this study , we found that postnatal chondrocyte-specific Fgfr3 deletion induced multiple chondroma-like lesions , including enchondromas and osteochondromas , adjacent to disordered growth plates . The lesions showed decreased extracellular signal-regulated kinase ( ERK ) activity and increased Indian hedgehog ( IHH ) expression . The same was observed in Fgfr3-deficient primary chondrocytes , in which treatment with a mitogen-activated protein kinase ( MEK ) inhibitor increased Ihh expression . Importantly , treatment with an inhibitor of IHH signaling reduced the occurrence of chondroma-like lesions in Fgfr3-deficient mice . This is the first study reporting that the loss of Fgfr3 function leads to the formation of chondroma-like lesions via downregulation of MEK/ERK signaling and upregulation of IHH , suggesting that FGFR3 has a tumor suppressor-like function in chondrogenesis .
Enchondromas and osteochondromas are the most frequently occurring benign cartilaginous tumors affecting the skeleton [1 , 2] . The former develop as ectopic cartilaginous tissue within the bone marrow , while the latter manifest as cartilage-covered bony lesions arising on the bone surface [3] . Both tumor types can potentially undergo malignant transformation to become central or peripheral chondrosarcomas , respectively [4] . Since cartilaginous tumors are resistant to conventional chemo- and radiotherapy , surgical excision is the only treatment option [1] . A deeper understanding of the pathogenic mechanisms underlying cartilaginous tumor development is essential for the development of effective therapeutic strategies . Cartilaginous tumors arise as a result of mutations in several genes . Hereditary multiple exostoses syndrome ( HME , also called hereditary multiple osteochondromas , OMIM 133700 ) is associated with heterozygous loss-of-function mutations in exostosin ( Ext ) 1 or 2 , which encode Golgi-associated glycosyltransferases that mediate the polymerization of heparan sulphate ( HS ) chains [5 , 6] . Cells deficient in Ext1 or 2 fail to synthesize sufficient amounts of HS-rich proteoglycan ( HSPG ) , which is required for the regulation of cell surface and matrix-associated signaling pathways such as Indian hedgehog ( IHH ) , Wnt/β-catenin , fibroblast growth factor ( FGF ) , and bone morphogenetic protein [6] . Although Ext1/2-deficient mice develop multiple bony lesions with features similar to osteochondromas in HME patients [7–10] , the underlying mechanisms remain unclear . In addition , patients with enchondromatosis ( Ollier disease and Maffucci syndrome , OMIM 166000 ) carry mutations in parathyroid hormone-related protein ( PTHrP ) receptor ( Pthr ) 1 [11] , while mice with the Pthr1 R150C mutation exhibit constitutive activation of HH signaling and develop multiple enchondroma-like lesions [11 , 12] . A somatic gain-of-function mutation in the isocitrate dehydrogenase ( Idh ) 1/2 gene was also found to induce enchondroma formation in patients with Ollier disease and Maffucci syndrome [13–17] . Recently , the autosomal dominant disease metachondromatosis ( MC , OMIM 156250 ) was found to be associated with heterozygous inactivating mutations in tyrosine protein phosphatase non-receptor type ( Ptpn ) 11 [18 , 19] , which encodes the tyrosine phosphatase SHP2 , a downstream effector of receptor tyrosine kinases that activates the Ras/extracellular signal-regulated kinase ( ERK ) pathway [20] . MC is a rare disease characterized by enchondromas and osteochondromas [19] . The role of Ptpn11 in skeletal development and cartilaginous tumor formation has been investigated in mice [21–24]; interestingly , different mechanisms underlie the development of chondroma-like lesions in Shp2-deficient mice , enchondroma-like lesions in Pthr1 R150C mutant mice , and osteochondroma-like lesions in Ext1-deficient mice [24] . Most cartilaginous tumors are formed during skeletal development in a location adjacent to growth plates , suggesting that they arise as a result of dysregulated endochondral bone growth [1]; this is supported by the fact that the majority of the above-mentioned genes are involved in the regulation of growth plate development [24–26] . FGF receptor ( FGFR ) 3 is a transmembrane receptor that regulates skeletal development [27] . Patients with activating FGFR3 mutations exhibit skeletal dysplasias characterized by short stature including achondroplasia ( ACH , OMIM 100800 ) , thanatophoric dysplasia I/ II ( TD I , OMIM 187600 and TD II , OMIM 187601 ) , hypochondroplasia ( OMIM 146000 ) , and severe ACH with developmental delay with acanthosis nigricans ( SADDAN , OMIM 187600 ) [27] . Activated FGFR3 leads to impaired growth plate chondrocyte proliferation and differentiation , resulting in disordered endochondral bone growth and skeletal dysplasia in ACH/TD [28–30] . In contrast , a loss-of-function mutation in Fgfr3 in humans causes camptodactyly , tall stature , and hearing loss ( CATSHL ) syndrome ( OMIM 610474 ) [31 , 32] , and Fgfr3 deletion in mice leads to skeletal overgrowth due to enhanced proliferation of growth plate chondrocytes [33 , 34] . These data indicate that FGFR3 negatively regulates endochondral bone growth , and that FGFR3 mutations may involve in the development of cartilaginous tumors . In cartilage , FGFs such as FGF18 require HSPG as a cofactor to bind to the extracellular domain of FGFR3 [35 , 36] . Intracellular receptor tyrosine kinase domains then recruit the SHP2–growth factor receptor-bound protein 2–son of sevenless 1 signaling complex to the cell membrane via adapter proteins , resulting in the activation of downstream effectors such as mitogen-associated protein kinase ( MAPK ) , AKT , and signal transducer and activator of transcription [27 , 37 , 38] . FGFR3 signaling can also act as an inhibitor to regulate a negative feedback loop involving IHH and PTHrP [27] . Thus , most genes implicated in the formation of benign cartilaginous tumors are associated with FGF/FGFR3 signaling . Moreover , Colvin et al previously found that some global Fgfr3 knockout ( Fgfr3-/- ) mice develop ectopic pockets of hypertrophic chondrocytes below growth plates [34] , and Toydemir et al reported that osteochondroma has been detected in the long bones of several members of a family with CATSHL syndrome [31] . However , the precise role of FGFR3 signaling in cartilaginous tumorigenesis is not known . In this study , we demonstrate that conditional Fgfr3 knockout ( Fgfr3 cKO ) mice lacking FGFR3 in chondrocytes exhibit severe enchondroma- and osteochondroma-like lesions in the skeleton , indicating that FGFR3 is critically involved in the formation of cartilaginous tumors .
To investigate the role of FGFR3 in postnatal skeletal growth , we monitored the overall growth of Fgfr3 cKO relative to Fgfr3f/f Cre-negative control mice after tamoxifen administration . Fgfr3 cKO mice exhibited an increase in body length compared to Cre-negative mice ( Fig 1A ) . Similarly , femur and tibia lengths increased by 13 . 79% and 9 . 32% , respectively , in mutants as compared to that in controls . The increased stature of Fgfr3 cKO mice is consistent previous findings in conventional Fgfr3 knockout mice and patients with CATSHL syndrome [31–34] . Lesions were observed around the knee joints of 12-week-old Fgfr3 cKO mice ( Fig 1C , upper panels ) . Since these lesions appeared to be connected with bony tissue , we performed X-ray and micro-computed tomography ( CT ) analyses of undecalcified bone samples . The X-ray analysis showed dramatically expanded growth plates in the deformed knee joints ( Fig 1C , middle panels ) The broadly based sessile lesion was located near the growth plate and contained a medulla that was contiguous with the underlying bone ( Fig 1C , middle panels ) . Micro-CT 3-dimensional ( 3-D ) images of Fgfr3 cKO knee joints showed that the lesion surface formed a non-calcified cap ( Fig 1C , lower panels ) . X-ray and micro-CT scans of 25-week-old Fgfr3 cKO mice revealed the progression of bony lesions ( S1 Fig ) . We also detected multiple lesions in the wrist , an arch-like deformation of the radius , and subluxation/dislocation of the radial head in mutants ( Fig 1B ) , which also showed heterogeneous radiodensities of costochondral junctions by X-ray ( Fig 1D ) and multiple bony lesions around the costal cartilage surface ( Fig 1E ) . There were no significant lesions observed in bones with fused growth plates such as digits in these mice during the 12-month observation period ( S2A Fig ) . Histological analyses were carried out in order to investigate the morphological and cellular changes underlying the skeletal abnormalities in Fgfr3 cKO mice . Sections of wrist and knee joints from 12-week-old mutant mice showed multiple osteochondroma- and enchondroma-like lesions around the growth plate of the tibia ( Fig 2A–2P ) , femur ( Table 1 ) , ulna , and radius ( Fig 2Q–2X ) . Enchondroma-like lesions consisted of cartilaginous lobules with irregular architecture and cellular pleomorphism ( Fig 2I–2L and 2U and 2V ) . Osteochondroma-like lesions that were structurally similar to growth plate cartilage were observed at the bone surface ( Fig 2M–2P and 2W and 2X ) . Notably , growth plates in mutants were expanded and the columnar morphology of chondrocytes was lost ( Fig 2E–2H ) . Multiple hypertrophic chondrocyte clusters were present in the costal cartilage of Fgfr3 cKO mice ( Fig 3A and 3B ) . In some cases , the expansion of the chondrocyte cluster territory disrupted the integrity of the perichondrium . We also found cartilaginous islands within the trabecular bone of the ribs in mutants . Enchondroma- and osteochondroma-like lesions were observed in several Fgfr3-deficient mouse models ( Table 1 ) . Although the incidence of chondroma-like lesions in other Fgfr3-deficient mice was relatively low , their locations and histological features were similar to what we observed in Fgfr3 cKO mice ( S2B Fig ) . It is worth noting that those mice with conditional Fgfr3 deletion in chondrocytes had a higher incidence and greater severity of chondroma-like lesions . To determine whether wild-type chondrocytes contribute to cartilaginous tumorigenesis , cartilage tissues from chondroma-like lesions of Fgfr3 cKO mice were isolated by laser-capture microdissection ( S3A Fig ) and analyzed by allele-specific PCR . The lesions contained a mixture of wild-type and Fgfr3-deficient cells ( S3B Fig ) ; the fraction of Fgfr3-deficient cells ranged from 54 . 94% to 83 . 9% ( S3B Fig ) . These results suggest that Fgfr3-null and wild-type cells interact to promote the formation of cartilaginous tumors , which can explain differences in the incidence and severity of chondroma-like lesions between conditional and global Fgfr3 knockout mice . A histological analysis revealed ectopic cartilage at the bone-ligament attachment site between the menisco-tibial ligament and meniscus in Fgfr3−/− mice ( S4E–S4H Fig ) . This phenotype was similar to that of SHP2 cKO mice in which the protein tyrosine phosphatase non-receptor type 11 gene was deleted in fibroblast-specific protein 1-Cre-expressing fibroblasts [24] . In our study , ectopic cartilage was also observed adjacent to osteochondroma-like lesions connected to the side of growth plate cartilage in Fgfr3−/− mice ( S4I–S4P Fig ) ; however , we were unable to determine the precise anatomical location due to structural changes caused by the presence of chondroma-like lesions . The observed chondroma-like lesions suggest that loss of Fgfr3 in chondrocytes at the postnatal stage affects the normal development and maintenance of growth plate cartilage by disrupting the coordination of chondrocyte proliferation , differentiation , and apoptosis [27] . The lesions were associated with the dysregulation of growth plate chondrocytes ( Fig 2B and 2D ) ; thus , to determine how Fgfr3 deficiency alters growth plate development and homeostasis , we performed histological and immunohistochemical analyses on the long bones of 8-week-old mice . As expected , Cre-negative mice exhibited normal growth plate organization in which chondrocytes formed distinct resting , proliferative , and hypertrophic zones ( Fig 4A , left panels ) . In contrast , growth plates in the mutants had disorganized columns of chondrocytes stacked within the proliferative zone , with cell clusters in the resting zone and epiphysis ( Fig 4A , right panels ) , suggesting impaired growth plate polarity . To investigate this possibility , primary cilia were detected using an anti-α-tubulin antibody . A virtual axis oriented parallel to the vertical axis of growth plates was formed by the alignment of primary cilia crossing the center of the chondrocyte column in the proliferative zone of Cre-negative mice ( S5A Fig ) . In contrast , in Fgfr3 mutants , primary cilia of the aligned axis of chondrocytes in the growth plate were disoriented ( S5B Fig ) . Furthermore , although most chondrocytes in chondroma-like lesions had disrupted polarity , a few formed well-organized columns with an orientation perpendicular to the boundary between the lesion and marrow cavity ( S5C and S5D Fig ) . These data suggest that disruption of chondrocyte polarity may be responsible for the disorganization of growth plates and the formation of chondroma-like lesions in mice with absence of Fgfr3 . To determine whether chondrocyte proliferation was altered in growth plates of Fgfr3 cKO mice , proliferating cell nuclear antigen ( PCNA ) and Ki-67 expression was detected by immunohistochemistry . PCNA-positive cells were evenly distributed in the proliferative zone of growth plates in control mice ( Fig 4B , upper left panel ) . In contrast , PCNA- ( Fig 4B , upper right panel ) and Ki67-positive cells ( Fig 4B , lower panels ) showed an irregular distribution in the proliferative and hypertrophic zones in mutants and had higher rates of proliferation ( Fig 4B ) , consistent with observations from the growth plates of Fgfr3−/− mice [33] . Given the expansion of the hypertrophic zone in Fgfr3-deficient growth plates ( Fig 2E–2H ) , the expression of collagen 10 and matrix metalloproteinase ( MMP ) 13 was evaluated by immunohistochemistry to determine whether growth plate chondrocyte differentiation was induced in mutants . Immunoreactivity for these markers was increased in Fgfr3 cKO relative to control mice ( Fig 4C ) . Furthermore , the expression of the terminal differentiation markers Mmp13 , a distintegrin and metalloproteinase with thrombospondin motifs ( Adamts ) 5 , and secreted phosphoprotein ( Spp ) 1 [24] was upregulated in Fgfr3-deficient chondrocytes ( Fig 4D ) . These data suggest that FGFR3 negatively regulates both proliferation and early/terminal differentiation of chondrocytes in postnatal growth plates . The role of FGFR3 in chondrocyte apoptosis remains controversial [27]; although FGFR3 overexpression was found to promote chondrocyte apoptosis in vitro [39–42] , there is little in vivo evidence to support this observation [43] . We performed terminal deoxynucleotidyl transferase dUTP nick end labeling ( TUNEL ) to determine whether Fgfr3 deficiency promotes growth plate chondrocyte apoptosis . In primary chondrocytes isolated from Fgfr3 cKO mice , the proportion of TUNEL-positive cells was similar to control levels ( S7 Fig ) . In contrast , an in vivo examination found that the proportion of TUNEL-positive cells relative to the total number of hypertrophic chondrocytes was significantly reduced in mutants as compared to control mice ( Fig 4E ) . In addition , the osteoclast activity was considered to be involved in endochondral bone growth [44] . To investigate the potential role of osteoclast activity in pathogenesis of the skeletal phenotypes of Fgfr3 cKO mice , we performed tartrate-resistant acid phosphatase staining of bone tissue , but found no differences in growth plate osteoclast recruitment between mutant and control mice ( S6A Fig ) . Furthermore , there were no bony lesions detected in osteoclast progenitor-specific Fgfr3 knockout mice ( Fgfr3f/f; lysozymeM-Cre ) by radiography ( S6B Fig ) . To further assess the tumorigenic properties of Fgfr3-deficient chondrocytes , we transplanted chondrocytes from Fgfr3 cKO and control mice subcutaneously into 5-week-old athymic mice . Two of five Cre-negative and four of five Fgfr3-deficient cartilage masses were detected and harvested 4 weeks after transplantation , and the Fgfr3-deficient cartilage masses were significantly larger after 4 weeks than those derived from Cre-negative cells ( S8A and S8B and S8G Fig ) and a histological analysis revealed larger areas of matrix-enriched cartilage in the former ( S8C–S8F Fig ) . These data indicate that FGFR3 plays a key role in the postnatal development and homeostasis of growth plate chondrocytes , and that FGFR3 deficiency leads to increased number of disordered chondrocytes , resulting in the formation of chondroma-like lesions . IHH and PTHrP signaling are important for the development and maintenance of growth plate cartilage [45] . Moreover , previous studies have shown that Pthr1 R150C mutant mice and those overexpressing Gli2 , a downstream effector of IHH signaling develop enchondroma-like lesions [11] , and we and others have observed that constitutively activated FGFR3 lowers the expression of Ihh mRNA in growth plate chondrocytes [28 , 29 , 46 , 47] . We therefore investigated whether IHH signaling is perturbed in the growth plates of Fgfr3-deficient mice by immunohistochemistry . In growth plates of Cre-negative mice , IHH expression was mainly detected within the pre-hypertrophic zone and in a few cells in the hypertrophic zone ( Fig 5A , left panel ) . In contrast , the growth plate of Fgfr3 cKO mice consisted of disorganized chondrocytes in which IHH expression was markedly upregulated ( Fig 5A , middle panel ) . Furthermore , strong IHH expression was observed in chondroma-like lesions in mutants ( Fig 5A , right panel ) , implying that the dysregulation of IHH signaling is associated with cartilaginous tumorigenesis caused by Fgfr3 deficiency . It was recently reported that SHP2 deletion in chondrocytes and chondroprogenitor cells within the groove of Ranvier results in ectopic chondrogenesis and the formation of chondroma-like lesions via MAPK-induced upregulation of IHH signaling [23 , 48] . Given that SHP2 acts as a downstream effector of FGFR3 signaling that modulates the MAPK pathway [27 , 38] , we speculated that cartilaginous tumorigenesis in Fgfr3 cKO mice was caused by a mechanism similar to that of MC , which results from SHP2 deficiency . To investigate whether MAPK signaling is impaired in chondrocytes of Fgfr3 cKO mice , we evaluated MAPK activation by immunohistochemistry using an antibody against phosphorylated extracellular signal-regulated kinase ( ERK ) . MAPK activity was detected within hypertrophic zones in Cre-negative growth plates ( Fig 5B , left panel ) , consistent with previous observations [48] . In contrast , MAPK activity was reduced in hypertrophic zones of Fgfr3 cKO mice ( Fig 5B , right panel ) , which was confirmed by western blot analysis of isolated chondrocytes . MAPK activation in response to FGF18 was also reduced in Fgfr3-deficient chondrocytes ( Fig 5C ) . To determine whether the upregulation of IHH is a consequence of impaired MAPK signaling in Fgfr3 mutants , we examined the expression of genes regulating chondrogenesis by quantitative real-time ( qRT- ) PCR . Ihh and Pthrp mRNA levels were increased in Fgfr3-deficient chondrocytes ( Fig 5D ) , while treatment with MAPK inhibitor ( 250 nM U0126 or 10 nM PD98059 ) also increased Ihh and Pthrp levels ( Fig 5D ) . These results indicate that the enhanced expression of IHH in growth plates of Fgfr3 mutants is due at least in part to the suppression of MAPK signaling . In the canonical IHH signaling pathway , Smoothened ( Smo ) dissociates from the Patched ( Ptch ) receptor following the binding of IHH to Ptch [49] and activates downstream effectors such as Gli transcription factors [49] . A Smo inhibitor ( SMOi ) such as GDC-0449 ( Vismodegib/Erivedge ) developed to treat basal cell carcinoma and medulloblastoma [49 , 50] can therefore be used to inhibit IHH signaling . Since upregulated IHH signaling is a potential mechanism underlying the cartilaginous tumorigenesis observed in Fgfr3-deficient mice , we investigated whether the process could be blocked by inhibiting IHH signaling using GDC-0449 . Skeletal phenotypes after SMOi treatment were assessed by X-ray , micro-CT , and histological examination . The lower limb length of all mice was significantly reduced by SMOi treatment ( Fig 6Q ) . X-ray analysis and micro-CT revealed joint deformation and growth plate expansion was detected by histological analyses of Fgfr3 cKO mice; however , these were alleviated by SMOi treatment ( Fig 6A–6P and 6R ) . Importantly , there were no chondroma-like lesions in the mutants after 4 weeks of SMOi treatment ( Fig 6L and 6S ) . The fusion of growth plates was readily observed in 8-week-old SMOi-treated Cre-negative and Fgfr3 cKO mice , but not in those receiving vehicle treatment ( Fig 6I–6P ) . Furthermore , SMOi treatment promoted chondrocyte apoptosis in primary chondrocytes isolated from wild-type and mutant mice ( S7 Fig ) . Chondrocytes were evenly distributed at the center of the costal cartilage and were separated from the surrounding muscle by the perichondrium in Cre-negative mice treated either with vehicle or SMOi ( S9 Fig ) . In contrast , hypertrophic-like chondrocyte clusters were observed in the cartilage of Fgfr3 cKO mice , which were alleviated by SMOi treatment ( S9 Fig ) . These results suggest that enhanced IHH signaling is involved in the pathogenesis of cartilaginous tumorigenesis caused by FGFR3 deficiency .
Cartilaginous tumors are the most common primary bone tumors , with osteochondroma and enchondroma being the most prevalent benign cartilaginous lesions in humans [1] . Mutations in several genes , such as Ext1/2 , Ptpn11 , Pthr1 , and Idh1/2 lead to cartilaginous tumorigenesis , and these genes are important in the normal development and maintenance of growth plates . Although FGFR3 plays a key role in chondrogenesis , its role in the pathogenesis of cartilaginous tumors is poorly understood; indeed , although osteochondroma is observed in several members of a family with CATSHL syndrome , there have been almost no studies examining this phenotype [31] . One reason for this is that activation of FGFR3 signaling enhances proliferation in most cell types—including fibroblasts , keratinocytes , melanocytes , epithelial cells , lymphocytes and spermatocytes—and consequently lead to cancer [51]; as such , the loss-of-function phenotype of FGFR3—which positively regulates cartilage development and may be responsible for cartilaginous tumorigenesis—may have been previously overlooked . In this study , we found that Fgfr3 deficiency impaired the normal development and homeostasis of growth plates and induced the formation of chondroma-like lesions via downregulation of MAPK and consequent upregulation of IHH signaling , ( Fig 7 ) . Typical osteochondroma- and enchondroma-like lesions were observed in the femur , tibia , and the distal radius/ulna of Fgfr3 mutant mice . However , these MC-like phenotypes differ from those of patients with CATSHL syndrome , who exhibit only osteochondroma [31] . A more detailed radiographic examination of these patients is needed in order to determine whether they also have enchondroma lesions . It remains unclear how chondroma-forming cells are derived . In our study , enchondroma-like lesions in Fgfr3 cKO mice appeared to originate from disordered growth plates , which is consistent with the enchondroma formation observed in mice with Shp2 deficiency or overexpression of GLI2 or PTHR1 R150C [11 , 24 , 52] . This evidence suggests that enchondroma-forming cells arise from growth plate chondrocytes undergoing abnormal endochondral ossification . The origin of osteochondroma-forming cells is also unknown [6 , 53]; one possibility is that they result from dysregulated growth plate chondrocytes . Evidence in support of this comes from previous studies showing that Ext1 deficiency leads to the formation of osteochondroma-like lesions adjacent to growth plate cartilage [7–9 , 26] . The periosteum and perichondrium both contain chondroprogenitor cells [54 , 55] and are therefore also considered as potential sites of osteochondroma [10 , 48] . Other possibilities are that osteochondroma-forming cells are derived from the groove of Ranvier [6 , 23] , or else the periosteum or perichondrium , which showed Cre recombinase activity in the collagen type II α1 ( Col2a1 ) -CreERT2 mice used in this study [48] . However , since no ectopic chondrogenesis or chondroma-like lesions was observed in bones with fused growth plates ( e . g . , adult digits ) , we speculate that these lesions are mainly induced by the abnormal development and maintenance of growth plate cartilage in Fgfr3-deficient mice . We investigated the roles of FGFR3 in the development and maintenance of growth plate chondrocytes during the postnatal stage to clarify the mechanism by which loss of Fgfr3 leads to the formation of chondroma-like growth plate lesions . Data from a recent study suggests that Shp2 deficiency induces the transition from proliferating chondrocytes to pre- or early-hypertrophic chondrocytes but delays the switch from pre- or early hypertrophic to terminal hypertrophic chondrocytes , which is thought to result in chondroma-like lesions in an MC mouse model [24] . In our study , Fgfr3 deficiency induced both early and terminal differentiation of growth plate chondrocytes in postnatal stages . We therefore propose that different mechanisms are responsible for the formation of chondroma-like lesions in Fgfr3- and Shp2-deficient mice . The chondroma-like lesions in the former are composed of small chondrocytes surrounded by large hypertrophic chondrocytes and are located in the marrow cavity and at the bone surface adjacent to growth plates . We also observed dysregulated polarity , increased proliferation , and decreased apoptosis of growth plate chondrocytes in Fgfr3 mutants . Based on these findings , we speculate that Fgfr3 deficiency in growth plates disrupts the coordination of chondrocyte proliferation , differentiation , and apoptosis , resulting in the accumulation of abnormal chondrocytes that form lesions . Additionally , decreased osteoclast activity and vascular invasion are thought to underlie cartilaginous tumorigenesis [56] . We found that osteoclast/chondroclast recruitment around growth plates and chondroma-like lesions was unaffected in Fgfr3 cKO mice and there were no chondroma-like lesions in Fgfr3f/f; lysozyme ( lys ) M-Cre mice during 6 months of observation . Moreover , it was reported that despite the downregulation of vascular endothelial growth factor—a molecule required for hypertrophic chondrocyte apoptosis [57]—vascular invasion is only slightly impaired in the growth plate cartilage of Fgfr3−/− mice [43] . Therefore , changes in osteoclast/chondroclast and vascular invasion may not be involved in chondroma-like lesion formation resulting from loss of Fgfr3 . The results of this and previous studies suggest that FGFR3 signaling promotes early chondrocyte hypertrophy through SHP2 and MAPK signaling [24 , 27] . However , the roles of FGFR3 and ERK1/2 in terminal hypertrophy are different from that of SHP2 in growth plate chondrocytes during the postnatal stage . Although the downregulation of FGFR3 , SHP2 , and ERK1/2 observed in vivo induced the expansion of the growth plate hypertrophic zone , an increase in bone length was only observed in Fgfr3- and Erk1/2-deficient mice [22 , 24 , 33 , 34 , 58] . Moreover , loss of ERK1/2 strongly inhibited chondrocyte proliferation , implying that enhancement of chondrocyte terminal hypertrophy is a major determinant of longitudinal bone overgrowth in Erk1/2-deficient mice [58 , 59] . These studies suggest that ERK1/2 negatively regulates growth plate chondrocyte terminal differentiation during the postnatal stage . Moreover , expression of constitutively active mitoggen-activated protein kinase kinase ( MEK ) 1 can rescue the overgrowth of bones in Fgfr3-deficient mice by inhibiting chondrocyte differentiation [60] , indicating that the MAPK pathway is activated downstream of FGFR3 signaling to regulate growth plate chondrocyte differentiation . An important remaining question is whether FGFR3 and SHP2 have MAPK-independent and-dependent roles in cartilaginous tumorigenesis . We also speculate that cell autonomous as well as nonautonomous mechanisms are involved in the initiation and progression of cartilaginous tumors . This is supported by the fact that a recombination rate of only 6%–15% is sufficient to induce the formation of osteochondroma-like lesions in Ext1f/f mice , in which osteochondroma-like lesions contain both Ext1-null and wild-type chondrocytes [9] . A similar phenomenon was observed in patients with active Idh1 mutations and in Shp2-deficient mice [16 , 24] , suggesting that altered paracrine signaling from mutant growth plate chondrocytes may alter homeostasis in neighboring wild-type cells . In Fgfr3−/− mice , FGFR3 is deleted in all cells , and each zone within the growth plate—especially the hypertrophic zone—is expanded [33 , 34]; however , the phenotype is milder than that of Fgfr3 cKO mice with mosaic Fgfr3 deletion . Moreover , the incidence of chondroma-like lesions is much lower in Fgfr3−/− than in Fgfr3 cKO mice ( Table 1 ) . One explanation for this is that chondroma formation can be initiated by Fgfr3-deficient cells in both mutants , but will be enhanced in mosaic Fgfr3 cKO mice owing to crosstalk between mutant and adjacent wild-type cells , which are not passively incorporated into lesions but may interact with mutant cells to actively promote cartilaginous tumor formation . Previous studies have suggested that IHH upregulation contributes to metachondromatosis and that inhibiting IHH signaling suppresses cartilaginous tumorigenesis in Shp2-deficient mice [23 , 48] . Although we found that treatment with an IHH signaling inhibitor reduced the formation of chondroma-like lesions in Fgfr3 cKO mice , adverse secondary effects were induced , including premature growth plate fusion leading to a shorter long bone , which is similar to what was observed in mice with chondrocyte-specific deletion of IHH [61] . Other types of SMOi such as HhAntag had similar effects on the skeleton [62] . Thus , given that cartilaginous tumors occur mostly in children , caution is required when using SMOi to treat young patients with enchondroma and osteochondroma . PF-04449913 was shown to alleviate cartilaginous tumors in Shp2-deficient mice without significantly affecting bone development [23] . However , the optimal timing and dosage of IHH signaling inhibitors such as PF-04449913 must be established in order to improve treatment outcome and minimize the deleterious effects on the developing growth plate . In conclusion , this first is the first study to demonstrate that loss of Fgfr3 leads to the downregulation of MAPK signaling and enhanced IHH expression , resulting in the formation of chondroma-like lesions , including enchondromas and osteochondromas . Based on these findings , we propose that FGFR3 has a tumor suppressor-like function in cartilage development . Fgfr3 cKO mice can thus serve as a model to dissect the roles and mechanisms of action of FGFR3 in cartilaginous tumorigenesis , which can facilitate the development of effective therapies .
Fgfr3f/f mice were previously generated by our group [63] . The Col2a1-CreERT2 [64] , Col2al-Cre [65] , lysM-Cre ( Jackson Laboratories , Bar Harbor , ME , USA ) , cytomegalovirus ( CMV ) -Cre [66] , and Fgfr3−/− [33] mice were genotyped as previously described . For inducible deletion of Fgfr3 in chondrocytes , Fgfr3f/f mice were crossed with Col2a1-CreERT2 mice to obtain Fgfr3f/+; Col2a1-CreERT2 mice , which were crossed with Fgfr3f/f mice to obtain Fgfr3f/f and Fgfr3f/+; Col2a1-CreERT2 mice . Fgfr3f/f mice were crossed with Fgfr3f/+; Col2a1-CreERT2 mice to obtain Fgfr3f/+; Col2a1-CreERT2 ( Fgfr3 cKO ) and Fgfr3f/f ( Cre-negative ) mice . Tamoxifen ( 1 mg/10 g body weight ) was administered by intraperitoneal ( i . p . ) injection twice weekly for 8 weeks ( for a total of 16 injections ) starting 4 weeks after birth . Cre recombinase activity in the postnatal growth plate was evaluated as previously described [48] . As for Fgfr3f/+; Col2a1-CreERT2 mice , Fgfr3f/f mice were crossed with Col2al-Cre and lysM-Cre mice to generate Fgfr3f/f; Col2al-Cre and Fgfr3f/f; lysM-Cre mice , respectively . Wild-type mice were mated with the Fgfr3 f/f; CMV-Cre mice to generate Fgfr3 heterozygous mice which are expected to give 50% recombined Fgfr3 allele . Fgfr3 +/- mice were mated with the Fgfr3 +/- mice to generate Fgfr3 +/+ and Fgfr3 -/- mice . All mice were of the C3H/HeJ background . Animal experiments were performed according to protocols approved by the Laboratory Animal Welfare and Ethics Committee of the Third Military Medical University ( Chongqing , China ) . X-ray images of bony tissue were obtained using an MX-20 Cabinet X-ray system ( Faxitron X-Ray , Tucson , AZ , USA ) . Each undecalcified specimen was scanned using a vivaCT 40 micro-CT system ( Scanco Medical , Brüttisellen , Switzerland ) . Serial 12 . 5-μm 2-D and 3-D images were acquired at 70 kV and 113 mA . Constant thresholds ( 200 ) were applied to grayscale images to distinguish bone from soft tissue . Samples were fixed in 4% paraformaldehyde in 0 . 1 M phosphate buffer overnight , decalcified in 15% EDTA-phosphate buffered saline for 2 weeks and embedded in paraffin . Sections ( 5-μm thick ) were stained with Safranin O/Fast Green and hematoxylin and eosin ( H & E ) . For histomorphometric analysis , the Safranin O-positive cartilage area in 5-μm serial sections from the midsagittal region of growth plates was quantified using Image-Pro Plus 5 . 1 ( Leeds Precision Instruments , Minneapolis , MN , USA ) . Decalcified bone sections were deparaffinized with xylene , and endogenous peroxidase activity was quenched by treatment with 3% H2O2 for 15 min , followed by antigen retrieval by trypsinization for 10 min . Sections were then blocked with normal goat serum for 30 min and incubated at 4°C overnight with primary antibody followed by the appropriate biotinylated secondary antibody and horseradish peroxidase-conjugated streptavidin-biotin staining . Immunoreactivity was visualized with a 3 , 3'-diaminobenzidine tetrahydrochloride kit ( ZSGB-BIO , Beijing , China ) followed by counterstaining with Methyl Green . Primary antibodies against the following proteins were used: PCNA ( 1:200; BioVision , Milpitas , CA , USA ) , Ki67 ( 1:100; Abcam , Cambridge , MA , USA ) , Col10a1 ( 1:200; Abcam ) , MMP13 ( 1:200; Abcam ) , phospho-ERK ( 1:100; Cell Signaling Technology , Danvers , MA , USA ) , acetylated tubulin ( 1:200; Sigma-Aldrich , St . Louis , MO , USA ) , and IHH ( 1:100; Abcam ) . The number of PCNA- and Ki67-positive nuclei in three central regions of the growth plate was counted in Cre-negative and Fgfr3 cKO mice ( n = 3 each ) . Primary chondrocytes were isolated from knee joint cartilage of 3-day-old mice . Dissected tissues with cartilage were first digested with 0 . 25% trypsinase ( Gibco/Life Technologies , Carlsbad , CA , USA ) at 37°C for 15 min to remove muscles , ligaments , and bone tissue . Chondrocytes were isolated from knee joints by additional digestion with 0 . 1% collagenase II ( Gibco/Life Technologies ) overnight at 37°C in a CO2 incubator . Cells were seeded in 12-well plates at a density of 2 × 105 cells/well and cultured in Dulbecco’s Modified Eagle’s Medium/F12 ( 1:1 ) supplemented with penicillin/streptomycin ( Gibco/Life Technologies ) and 10% fetal bovine serum until they reached sub-confluence . On day 3 of culturing , primary chondrocytes were treated with 1 μM 4OH-tamoxifen ( Sigma-Aldrich ) for 48 h . For MEK inhibitor treatment , chondrocytes were incubated in 250 nM U0126 or 10 nM PD98059 ( both from Merck , Kenilworth , NJ , USA ) for 24 h after treatment with and in the presence of 4OH-tamoxifen . For FGF18 treatment , 50 ng/ml FGF18 ( PeproTech , Rocky Hill , NJ , USA ) were added to cultures 5 and 30 min after 4OH-tamoxifen incubation . Primary chondrocytes ( 2 × 106cells ) were resuspended in 100 μl serum-free medium and mixed with an equal volume of Matrigel ( BD Biosciences , Franklin Lakes , NJ , USA ) . The mixture was injected subcutaneously into the lower flanks of athymic nude mice ( BALB/c-nu , male , 5 weeks old ) . Tamoxifen was administered by i . p . injection twice weekly . Chondrocyte transplants were harvested 4 weeks after transplantation . Total RNA was extracted from primary chondrocytes with TRIzol reagent ( Invitrogen , Carlsbad , CA , USA ) according to the manufacturer’s instructions . All reactions were performed in a Mx3000P thermal cycler ( Stratagene , Santa Clara , CA , USA ) using the Two-Step QuantiTect SYBR Green RT-PCR Kit ( Takara Biotechnology , Otsu , Japan ) and reaction conditions were optimized for each gene by altering the annealing temperature ( 57°C–61°C ) . Each run consisted of samples for genes of interest and cyclophilin A . The forward and reverse primer sequences were as follows: cyclophilin A , 5'-CGA GCT CTG AGC ACT GGA GA-3' and 5'-TGG CGT GTA AAG TCA CCA CC-3'; Col10a1 , 5'-GCA GCA TTA CGA CCC AAG AT-3' and 5'-CAT GAT TGC ACT CCC TGA AG-3'; Mmp13 , 5'-CAG TTG ACA GGC TCC GAG AA-3' and 5'-CGT GTG CCA GAA GAC CAG AA-3'; Adamts5 , 5'-GGA GCG AGG CCA TTT ACA AC-3' and 5'-CGT AGA CAA GGT AGC CCA CTT T-3'; Ihh , 5'-CAA TCC CGA CAT CAT CTT CA-3' and 5'-GCG GCC CTC ATA GTG TAA AG-3'; Spp1 , 5'-TGG CTA TAG GAT CTG GGT GC-3' and 5'-TTG GCA GTA ATT TGC TTT TG-3'; and Pthrp , 5'-CAT CAG CTA CTG CAT GAC AAG G-3' and 5'-GGT GGT TTT TGG TGT TGG GAG-3' . Tissue sections ( 10 μm thick ) were transferred to polyethylene tetraphthalate FrameSlides ( Leica , Wetzlar , Germany ) and stained with H & E . Cartilage tissue in the region of chondroma-like lesions was isolated using a laser-capture microdissection system ( Molecular Machines & Industries , Eching , Germany ) . To avoid contamination from cells of the perichondrium/periosteum or trabecular bone , the tissue was cut far away from the border between the chondroma-like lesion and surrounding tissue . DNA was extracted from cartilage tissue using the EZNA DNA extraction kit ( Omega Bio-Tek , Norcross , GA , USA ) and analyzed by semiquantitative PCR . The p3 and p5 primers were used to detect the unrecombined allele , whereas p1 and p5 were used to detect the recombined allele as previously described [63] . Band intensity was measured using Image Lab software ( Bio-Rad Laboratories , Hercules , CA , USA ) . The ratio of the recombined to unrecombined allele was normalized to the level of Fgfr3 expression in heterozygous mice ( taken as 1:1 ) . Chondrocytes lysates were prepared in ice-cold radioimmunoprecipitation assay buffer containing protease inhibitors ( Roche Applied Science ) . Proteins were resolved by 10% or 12% sodium dodecyl sulfate polyacrylamide gel electrophoresis and transferred to a polyvinylidene difluoride membrane ( Millipore , Billerica , MA , USA ) , which was probed with antibodies against the following proteins: phospho-ERK ( Cell Signaling Technology , Danvers , MA , USA ) , ERK ( Cell Signaling Technology ) , and β-actin ( Sigma-Aldrich ) . Immunoreactivity was detected by enhanced chemiluminescence ( Pierce , Rockford , IL , USA ) . For in vitro experiments , the SMOi GDC-0449 ( Selleck Chemicals , Houston , TX , USA ) was reconstituted in dimethyl sulfoxide ( Sigma-Aldrich ) and applied at a final concentration of 1 μM for 24 h . For in vivo experiments , GDC-0449 was reconstituted in 50% ( w/v ) 2-hydroxypropyl-β-cyclodextrin ( Sigma-Aldrich ) in water . Mice were injected twice weekly for 4 weeks with tamoxifen ( 1 mg/10 g body weight ) starting from 4 weeks of age and daily with GDC-0449 ( 1 mg/10 g body weight ) or vehicle ( 50% 2-hydroxypropyl-β-cyclodextrin ) , except on tamoxifen injection days . At 8 weeks of age , the tibia and femur were dissected for X-ray , micro-CT , and histological analyses . The TUNEL assay was carried out with the In Situ Cell Death Detection kit ( Roche Applied Science , Pleasanton , CA , USA ) according to the manufacturer’s instructions . Results are expressed as mean ± SD . Differences between groups were analyzed with the Student’s t test . p < 0 . 05 was considered significant .
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Benign cartilaginous tumors , especially osteochondromas and enchondromas , are the most common primary bone tumors in humans . Several hereditary diseases are characterized by the development of cartilaginous tumors , including hereditary multiple exostoses , metachondromatosis , and enchondromatosis , which are caused by mutations in genes such as exostosin 1 and 2 , tyrosine protein phosphatase non-receptor type 11 , parathyroid hormone receptor 1 , and isocitrate dehydrogenase 1 and 2 . The proteins encoded by these genes are directly or indirectly linked to fibroblast growth factor ( FGF ) signaling . In addition , osteochondroma was found in several members of a family with camptodactyly , tall stature , and hearing loss syndrome , a rare inherited disorder caused by a heterozygous missense mutation in Fgfr3 . In this study , we found that Fgfr3 deficiency leads to the formation of cartilaginous tumors , including osteochondromas and enchondromas , likely due to dysregulated endochondral ossification in growth plates . We also show that cartilaginous tumorigenesis in Fgfr3-deficient mice results from excessive Indian hedgehog production mediated by the activation of mitogen-associated protein kinase signaling . Based on these results , we propose a model for cartilaginous tumor development in which FGFR3 functions as a tumor suppressor . Our findings also suggest that modulation of FGFR3 and related signaling pathways is a potential therapeutic strategy for treating benign cartilaginous tumors .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
FGFR3 Deficiency Causes Multiple Chondroma-like Lesions by Upregulating Hedgehog Signaling
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Many bacterial pathogens produce extracellular proteases that degrade the extracellular matrix of the host and therefore are involved in disease pathogenesis . Dichelobacter nodosus is the causative agent of ovine footrot , a highly contagious disease that is characterized by the separation of the hoof from the underlying tissue . D . nodosus secretes three subtilisin-like proteases whose analysis forms the basis of diagnostic tests that differentiate between virulent and benign strains and have been postulated to play a role in virulence . We have constructed protease mutants of D . nodosus; their analysis in a sheep virulence model revealed that one of these enzymes , AprV2 , was required for virulence . These studies challenge the previous hypothesis that the elastase activity of AprV2 is important for disease progression , since aprV2 mutants were virulent when complemented with aprB2 , which encodes a variant that has impaired elastase activity . We have determined the crystal structures of both AprV2 and AprB2 and characterized the biological activity of these enzymes . These data reveal that an unusual extended disulphide-tethered loop functions as an exosite , mediating effective enzyme-substrate interactions . The disulphide bond and Tyr92 , which was located at the exposed end of the loop , were functionally important . Bioinformatic analyses suggested that other pathogenic bacteria may have proteases that utilize a similar mechanism . In conclusion , we have used an integrated multidisciplinary combination of bacterial genetics , whole animal virulence trials in the original host , biochemical studies , and comprehensive analysis of crystal structures to provide the first definitive evidence that the extracellular secreted proteases produced by D . nodosus are required for virulence and to elucidate the molecular mechanism by which these proteases bind to their natural substrates . We postulate that this exosite mechanism may be used by proteases produced by other bacterial pathogens of both humans and animals .
Dichelobacter nodosus is a Gram negative , anaerobic rod that is the principal causative agent of ovine footrot , a debilitating disease of the hoof of ruminants . The disease results in significant costs to the worldwide sheep industry due to a reduction in meat and wool production and the expenditure associated with prevention and treatment programs [1] , [2] , [3] . Footrot is characterized by the separation of the keratinous hoof from the underlying tissue , resulting in severe lameness and loss of body condition [4] , [5] . The severity of the disease can vary from benign footrot , which presents as an interdigital dermatitis that does not progress , to virulent footrot , which results in severe under-running of the horn of the hoof and the separation of the hoof from the underlying tissue [1] . Type IV fimbriae are an essential virulence factor [6] , [7]; it also has been suggested that three closely-related secreted subtilisin-like proteases produced by D . nodosus may be required for virulence [8] , [9] . In strains that cause virulent footrot these proteases are called acidic protease isoenzymes 2 and 5 from virulent strains ( AprV2 and AprV5 ) and basic protease from virulent strains ( BprV ) . In benign strains the comparable proteases are termed AprB2 , AprB5 and BprB . All of these proteases are synthesised as inactive precursors with an N-terminal pre-pro-region , a serine protease domain and a C-terminal domain of unknown function . The active protease is produced by cleavage of the N-terminal pre-pro region and the C-terminal domain [10] , [11] , [12] . The protease domains have significant sequence identity to members of the subtilase family of serine proteases ( 54% identity with closest homologue from Dehalococcoides sp . VS ) , but sequence alignments indicate several insertions in the D . nodosus proteases [13] . Previous studies suggested that these proteases may represent the key difference between virulent and benign strains of D . nodosus; proteases secreted by virulent isolates have a greater thermostability and elastase activity ( as monitored on elastin agar plates ) than those of benign strains and it is postulated that this difference may relate to their in vivo activity against host tissue . These features are utilized in diagnostic tests to distinguish between virulent and benign footrot [14] , [15] , [16] . Comparison of the protease sequences from the virulent strain A198 with the benign strain C305 revealed that within the mature protease domain there is a single amino acid difference between AprV2 and AprB2 [12] , and between AprV5 and AprB5 [11] , and 96% sequence identity between BprV and BprB [17] . In this multidisciplinary study we set out to determine the role of these proteases in virulence and to determine the molecular basis for their function . We constructed isogenic protease mutants and characterized their protease activity and virulence . We showed that AprV2 was required for a virulent D . nodosus isolate to cause disease . Determination of the crystal structure of AprV2 revealed the presence of a novel exosite loop . This combined genetic and structural approach has permitted a comprehensive investigation of the secreted protease component of a pathogenic organism , and furthermore provided novel insight into how subtilisin-like proteases may have been hijacked by pathogenic microorganisms to degrade extracellular matrix components .
To assess the contribution of each of the three extracellular proteases to the overall protease activity of the virulent D . nodosus isolate VCS1703A , separate chromosomal mutants of each protease gene were constructed by allelic exchange events that involved double crossovers . To confirm that the observed phenotypes resulted from these mutations , the mutants were complemented by insertion of the wild-type protease genes into the chromosome . Quantitative protease assays of culture supernatants , using azocasein as the substrate , showed that mutation of the aprV5 and aprV2 genes reduced total protease activity by 71% and 39% , respectively ( Figure 1A ) . Complementation with the respective wild-type genes returned total protease activity to wild-type levels; however , it was also observed that the complemented aprV5 strain tended to lose protease activity upon repeated subculture . Since only a 12% reduction ( P<0 . 05 ) was observed in the bprV mutant , BprV does not appear to make a major contribution to total protease activity . These results indicate that AprV5 , either directly or indirectly , makes the major contribution to total extracellular protease activity . To confirm the individual contribution of each protease gene to the overall protease activity of VCS1703A , double and triple mutants were also constructed . Only very low levels of protease activity were observed in the aprV2bprV and aprV5bprV double mutants ( Figure 1B ) . Negligible protease activity was detected in the triple mutant and the aprV2aprV5 double mutant . The reduction in total protease activity for the double mutants was greater than the combined reduction in the total protease activity for the single mutants suggesting that the secreted proteases either act synergistically to degrade target substrates or that one or more of the proteases may be involved in the activation of the other proteases , or both . The ability of D . nodosus to digest insoluble elastin in an agar medium has been used as a diagnostic test to distinguish virulent and benign strains . Virulent isolates digest elastin within seven to ten days , while benign strains show no digestion after >21 days incubation [14] . Analysis of the wild type , the protease mutants and the complemented strains on elastin agar showed that the aprV2 mutant was unable to digest elastin , even after 30 days incubation , whereas both the aprV5 and bprV mutants were able to digest elastin at wild-type levels , showing clearing after 10 days ( Figure S1A ) . Complementation of the aprV2 mutation restored the ability to digest elastin . In addition , the aprV5bprV mutants still had elastase activity . These results provided evidence that the AprV2 protease was responsible for the elastase activity . Note that the levels of elastase activity in culture supernatants were not high enough for detection in quantitative elastase assays . Sequence analysis of the aprV2 and aprB2 genes has shown there is only one amino acid difference ( Y92R ) between the two mature proteases [12] . To see what effect complementing the aprV2 mutant with the aprB2 gene would have on the protease phenotype of the resultant strain we inserted the aprB2 gene from the benign strain CS101 into the site of the disrupted aprV2 gene . Analysis of this strain in the azocasein assay showed that its total protease activity was not significantly different to the wild type ( Figure 1A ) . However , this complemented strain had the in vitro phenotype of a benign strain since it had no elastase activity ( Figure S1A ) , and its protease thermostability profile ( Figure S1B ) was that expected of a benign isolate [16] . The apparent difference in elastase activity between AprV2 and AprB2 was assessed in vitro using an Elastin-Congo Red substrate and purified recombinant proteins . Under these conditions , the ability of AprB2 to degrade the substrate was significantly less than AprV2 ( p<0 . 05 , Figure 2 ) . Both proteases displayed similar activity against the soluble chromogenic elastase substrate , N-Methoxysuccinyl-Ala-Ala-Pro-Val p-nitroanilide ( AAPVn ) , with kinetic parameters that were of the same order of magnitude ( Table 1 ) . These results suggest that the Y92R substitution does not contribute directly to catalysis at the active site of the enzyme . To determine the role in disease of each of the proteases , virulence testing in sheep was carried out on the wild type , the aprV2 , aprV5 and bprV mutants and their corresponding complemented strains , using our standard procedure [6] , [7] . These experiments represented a rigorous test of the ability of these bacteria to cause disease since such pen-based trials often magnify the ability of less virulent isolates to cause disease . Comparative analysis of the footrot scores of sheep infected with the wild-type strain and the aprV2 mutant revealed a significant difference ( P<0 . 0001 ) ; the aprV2 mutant was effectively avirulent ( Figure 3 ) . Complementation of the aprV2 mutant with the wild-type aprV2 gene restored the wild-type virulence profile , fulfilling molecular Koch's postulates . To our surprise , complementation of the aprV2 mutant with aprB2 also restored a virulent phenotype , indicating that AprV2-mediated elastase activity was not required for virulence . Analysis of isolates obtained from infected lesions confirmed that they had the expected phenotypic and genotypic properties . The aprV5 and bprV mutants were also avirulent ( Figure 3 ) , but complementation of these strains with the respective wild-type genes did not restore virulence , which was unexpected . Extensive sequencing of each of the protease genes in these strains showed them to be intact . However , upon subculture , protease secretion and/or elastin digestion by the aprV5 and bprV complemented strains were variable , as was their ability to undergo twitching motility , a property that is essential for virulence [7] . Therefore , we suggest that the complemented derivatives were genetically unstable and that secondary mutations were being selected in these strains . Consequently , no meaningful conclusions can be drawn from the aprV5 and bprV sheep virulence experiments . Since the elastase activity of AprV2 was not required for virulence it was of interest to determine which hoof proteins were degraded by Aprv2 and AprB2 . Fragments of hoof from a disease-free sheep were exposed to recombinant AprV2 and AprB2 and solubilised proteins were identified . AprV2 degraded type I keratin , serum albumin and the beta subunit of haemoglobin ( Figure 4 ) . Importantly , the hoof digestion pattern produced by AprB2 was similar to that produced by AprV2 ( Figure 4 ) , which is consistent with results of the virulence trials . To investigate how the single amino acid difference ( Y92R ) between the active forms of AprV2 and AprB2 alters the substrate specificity of AprB2 we determined the crystal structures of AprV2 and AprB2 to 2 . 0 Å and 1 . 7 Å , respectively ( Table 2; Figure 5A , B ) . The two structures were very similar ( RMSD of 0 . 28 Å for 339 Cα ) ( Figure 5C ) and therefore we will describe the structure with reference to AprV2 . AprV2 adopts a subtilisin-like fold consisting of a curved six-stranded parallel beta sheet sandwiched between two and five alpha helices ( Figure 5A , B ) . A two stranded anti-parallel beta hairpin runs perpendicular to the plane of the central beta sheet . The proteases have two disulphide bonds , Cys89-Cys141 and Cys183-Cys220 and three calcium binding sites ( Figure S2 ) . The proposed catalytic triad ( Asp41 , His105 and Ser277 ) of AprV2 is located at the C-terminal edge of the beta sheet . The most striking feature of the substrate binding site is a large , elongated S1 binding pocket , which is lined by residues 177–180 , 204–208 , 215 and 218 , and appears capable of accommodating bulky side-chains such as phenylalanine ( Figure 5D ) . This finding is consistent with previous studies , which have shown that AprV2 preferentially cleaves after phenylalanine or leucine residues [18] . Comparison with other subtilisin-like proteases reveals several major insertions ( termed I1-I4 ) in the loops that surround the active site cleft ( Figure 6A , B; Figure S3 ) . Most notable is the large well ordered I2 loop ( residues 82–102 ) that is tethered to the subtilisin-like fold by a disulphide bond between Cys141 and Cys89 ( Figure 6 ) . The loop is well defined in the electron density , with low B-factors , suggesting that it has limited mobility in the crystal structure ( Figure 6C ) . However , the apparent stability of the I2 loop is likely to arise from crystal packing and it is uncertain if this conformation would be favoured in solution . Surprisingly , the single amino acid difference between AprV2 and AprB2 ( Y92R ) is located at the tip of this extended loop , ∼27 Å from the active site serine ( S277 ) . A PSI-BLAST search revealed that the additional loops present in AprV2 may be conserved in other extracellular proteases ( Figure S3B ) . We constructed an I2 loop truncation mutant , AprV2Δ83–99 , but the resultant protein was not functional , therefore we investigated the role of the I2 loop using site-directed mutagenesis . We targeted residue 92 as the Y92R substitution reduced the ability of AprB2 to degrade Elastin-Congo Red ( Figure 2 ) , while maintaining its ability to degrade the elastin-like peptide AAPVn ( Table 1 ) . We used site-directed mutagenesis to convert Tyr92 to Asp , Ala , Leu or Phe and examined the ability of the resultant proteins to degrade insoluble Elastin-Congo Red . While the presence of a negative charge ( Asp ) , positive charge ( Arg ) or smaller hydrophobic ( Ala and Leu ) side-chain decreased elastin degradation , the Phe substitution increased the elastase activity of the enzyme ( Figure 2 ) . We also examined the ability of these mutants to degrade AAPVn ( Table 1 ) , fibronectin ( Figure S4 ) or hoof material ( Figure S5 ) . No major differences were discernable . Based on these data we conclude that an aromatic ring is required at position 92 for maximal activity against insoluble elastin , but that this activity is not related to the hoof digestion observed in a footrot lesion . To test the importance of the Cys89-Cys141 disulfide bond for proteolytic activity , we also used site-directed mutagenesis to convert Cys141 to Ser141 . Although this AprV2 . C141S derivative was still active against small peptide substrates ( Table 1 ) , we noted that the ability of this enzyme to degrade fibronectin , insoluble elastin and proteins from sheep hoof was reduced . Notably , wild-type AprV2 was able to break down fibronectin in 48 h , whereas at the corresponding time point AprV2 . C141S-treated fibronectin was still intact ( Figure 7A ) . The elastinolytic activity of the C141S protease was also approximately two-fold lower than wild type ( Figure 2 ) . Finally , the ability of AprV2 . C141S to degrade sheep hoof material was significantly reduced compared to AprV2 and AprB2 ( Figure 4 ) . Together these data suggest that the integrity of the I2 loop and the Cys89-Cys141 disulfide bond is important for maximal AprV2 protease activity . To determine whether the C141S substitution affects the conformation/mobility of the I2 loop we determined the crystal structure of AprV2 . C141S to 2 . 1 Å ( structure refinement statistics in Table 2 ) . The structure of AprV2 . C141S was very similar to that of AprV2 , overlaying with an RMSD of 0 . 21 Å for 339 Cα . The structure confirmed the absence of the Cys89-Cys141 disulfide bond and revealed that while the I2 loop had a slightly different structure to that of AprV2 there were no significant differences in the structure of the active site or primary substrate binding site ( Figure 8A and S6 ) . The average B factors for the I2 loops in AprV2 and AprV2 . C141S were 14 . 6 Å2 and 28 . 4 Å2 , respectively , indicating that the I2 loop is more mobile in the substituted protease ( Figure 8B , C ) . Given that the conformation of the I2 loop is stabilized by crystal packing in all three structures we investigated whether the I2 loop was more mobile in solution in AprV2 . C141S using intrinsic tryptophan fluorescence spectroscopy; the I2 loop contains two tryptophans . Steady state fluorescence quenching data showed that the wild-type protease was more protected from quenching by potassium iodide than the C141S enzyme ( Figure 7B ) , confirming that the conformation of the I2 loop in AprV2 . C141S is different to that in the wild type .
Although the extracellular proteases of D . nodosus have been considered for many years to be potential virulence factors [9] , their importance in the pathogenesis of disease had not been established . The analysis of their role in disease has always been complicated by the fact each isolate produces three very closely related proteases . The genetic approach utilized here has now provided clear evidence that the AprV2 protease is essential for virulence . This conclusion is based on data that showed that an aprV2 mutant was unable to cause footrot in sheep , unlike the isogenic wild-type strain , and that the ability to cause disease was restored to wild-type levels in the complemented derivative . No definitive conclusions could be drawn from the virulence testing of the aprV5 and bprV mutants . However , it is likely that the AprV5 and BprV proteases also play a role in disease , especially since the three secreted proteases appear to act synergistically , with the double protease mutants , aprV2aprV5 , aprV2bprV , and aprV5bprV , showing lower secreted protease activity than that expected based on the secreted protease activity of the individual mutants ( Figure 1B ) . It remains to be elucidated how this synergism arises , although it could occur at the processing , secretion or substrate level . Benign and virulent strains of D . nodosus can be differentiated by phenotypic analysis of their extracellular proteases , including analysis of their elastase activity and thermostability [14] , [16] , [19] , [20] . We now have established that AprV2 is responsible for the elastase activity of the virulent isolate VCS1703A . Purified AprB2 , which differs in sequence from AprV2 only at residue 92 , is less efficient at degrading elastin . Therefore , it was important to examine the effect on virulence of complementing the aprV2 mutant with aprB2 . This AprV2−AprB2+ strain was benign by the standard laboratory tests used to differentiate virulent and benign strains . Unexpectedly , it was virulent in the sheep footrot trial , producing disease that was indistinguishable in footrot severity from that caused by the isogenic wild-type strain . Since this strain still produces both AprV5 and BprV we conclude that the presence of one benign protease ( AprB2 ) in combination with two virulent proteases ( AprV5 and BprV ) is not sufficient to make a strain benign even though in a laboratory diagnostic context the strain would be designated as benign . Therefore , although it appears that the properties of AprV2 and AprB2 are responsible for the differentiation of benign and virulent strains in laboratory tests , there clearly are other virulence factors , such as the other virulent proteases , that contribute to virulent disease . We have shown that AprV2 mediates the degradation of keratin ( Figure 4 ) , a component of the ovine hoof that confers physical protection and tissue integrity . This finding suggests that AprV2 has a direct role in destroying the keratin layer of the ovine hoof , a characteristic feature of virulent footrot . The initial site of D . nodosus attachment during infection is at the epidermal layer of the interdigital skin and degradation of keratin in this area by AprV2 is likely to be required to break through the skin horn junction , allowing the subsequent under-running of the horn by D . nodosus . An intriguing feature identified in the structures of AprV2 and AprB2 is the disulphide-tethered I2 loop . This loop is located next to , and partially occludes , the substrate binding site of the enzyme ( Figure 6 ) . The single amino acid difference between AprV2 and AprB2 is located at the tip of the loop . We have shown that residue 92 is a key determinant of elastase activity . Although , substitution of Tyr92 with Arg reduced the degradation of insoluble elastin , no significant differences in either KM or Vmax were observed for hydrolysis of the elastin-like peptide , AAPVn . This result suggests that residue 92 does not contribute to catalysis at the active site of the enzyme . Instead , the reduced elastinolytic activity of AprB2 is likely to arise from impaired enzyme-substrate interactions at a site distal to the active site . We therefore propose that the I2 loop functions as an exosite , mediating the formation of a stable enzyme-substrate complex . The disulphide bond tethering the I2 loop appears to be important for this function since its disruption alters the mobility of the loop , which significantly reduces the ability of the protease to degrade fibronectin , insoluble elastin and other proteins from the sheep hoof . We have identified several serine proteases that like AprV2 also appear to contain large insertions between the β1 strand and α2 helix ( the location of the I1 and I2 loops ) ( Figure S3 ) . These enzymes include MprA , from Burkholderia pseudomallei ( the causative agent of melioidosis ) , TgSUB1 and TgSUB2 , from Toxoplasma gondii ( the causative agent of toxoplasmosis ) and PfSUB1 from the malaria parasite Plasmodium falciparum . MprA degrades physiologically relevant proteins and may play a role in causing the lung damage associated with melioidosis [21] , [22] , however , it has only a minor role in virulence [23] . PfSUB1 and TgSUB2 appear to be critical for parasite survival [24] , [25] . The presence of the I2-like insertions in these proteins suggests that the mechanism of exosite loop-mediated proteolysis used by the D . nodosus secreted proteases may represent a mechanism of substrate recognition that is utilized by other bacterial proteases . Studies investigating the structure of these proteins along with the function of their I2-like insertions will shed some light on this hypothesis and may lead to the development of improved diagnostic reagents and the identification of novel vaccine and drug targets .
Strains and plasmids are detailed in Table 3 . D . nodosus strains were routinely grown in an anaerobic chamber ( Coy Laboratory Products Inc . ) as described previously [6] . To construct the single mutants , suicide plasmids were inserted into D . nodosus strain VCS1703A by natural transformation [6] . The aprV2 and bprV mutants were complemented by transforming the mutants with the relevant plasmids , which reconstituted the disrupted gene and inserted a different resistance marker . The aprV5 mutant was complemented by inserting an intact copy of aprV5 and a kanamycin resistance marker into one of the three rrnA operons . The aprV2bprV double mutant was constructed by inserting the bprV suicide plasmid into an aprV2 mutant , and the aprV2aprV5 double mutant constructed by inserting the aprV2 suicide plasmid into an aprV5 mutant . An aprV5bprV double mutant was constructed by inserting a suicide plasmid into the wild-type strain , which disrupted both genes when a double crossover event occurred . Finally , a triple aprV2aprV5bprV mutant was constructed by inserting the aprV5bprV suicide plasmid into the aprV2 mutant . All mutants and complemented strains were confirmed by PCR and Southern hybridizations . PCR-RFLP analysis of the omp gene family was used to confirm that mutants were derived from the wild-type strain [26] . Elastase activity and protease thermostability assays for the differentiation of benign and virulent strains of D . nodosus were as described previously [14] , [16] , [27] . Virulence testing in sheep was performed as before [6] , [7] . The sheep were randomly allocated into nine groups of eight sheep and challenged blind with the various strains . A plain agar challenge was used as the negative control . The feet of all animals were examined and scored for footrot lesions at the start of the trial and then at weekly intervals using a standard lesion scoring method [28] , [29] . The total weighted foot score ( TWFS ) was used to provide an unambiguous overall footrot score for each animal [29] . The trial was carried out in a PC2 containment facility at Elizabeth Macarthur Agricultural Institute in accordance with the guidelines of the Australian Government Office of the Gene Technology Regulator and the Elizabeth Macarthur Agricultural Institute Animal Ethics Committee . All assays were performed in 20 mM Tris-HCl pH 8 and 5 mM CaCl2 ( buffer A ) with the exception of the Elastin-Congo Red elastase assay , which was performed in 25 mM Bis-Tris pH6 . 5 , 150 mM NaCl , 5 mM CaCl2 and 5% glycerol . Quantitative determination of total protease activity or elastase activity was carried out using azocasein [26] or Elastin-Congo Red [30] assays , respectively . Degradation of AAPVn by recombinant protease ( 1 µM ) was measured at 25°C as described [31] . KM and Vmax were determined by plotting initial velocities against AAPVn concentration and fitted by non-linear regression ( Prism ) . Fibronectin degradation was measured by incubating human fibronectin ( 1 µM , BD Biosciences ) with recombinant protease ( 0 . 1 µM ) at 25°C . Cleavage products were visualised by SDS-PAGE . Proteolytic degradation of hoof was determined by incubating dissected hoof material ( 2 . 2% ( w/v ) in buffer A ) from a disease-free sheep with recombinant protease ( 110 µg/ml ) at 25°C . Samples were taken over a 16 hour period and degradation products were visualised by SDS-PAGE . Hoof material ( 14% ( w/v ) in buffer A ) from a disease-free sheep was incubated with 100 µg of recombinant protease at 25°C for 18 h . Degradation products were separated by SDS-PAGE . The bands were excised and subjected to in-gel tryptic digestion and the digests analysed by LC-MS/MS using a HCT ULTRA ion trap mass spectrometer ( BrukerDaltonics ) coupled online with a 1200 series capillary HPLC ( Agilent technologies ) . Proteins were identified by searching the LC-MS/MS data against the National Center for Biotechnology Information ( NCBI ) non-redundant and Swiss-Prot databases using the MASCOT search engine ( version 2 . 1 , Matrix Science Inc . ) with all taxonomy selected . AprV2 and AprB2 were purified and crystallised as before [32] . Data collection statistics have been reported [32] . The expression construct for AprV2 . C141S was generated using the Quikchange site-directed mutagenesis kit ( Stratagene ) and pET22b . AprV2 as the template . Expression , purification and crystallisation of AprV2 . C141S were as for AprV2 . Data collection statistics for AprV2 . C141S are in Table S1 . Unless stated otherwise , all programs used for structural and crystallographic analysis were located within the CCP4 interface [33] to the CCP4 suite [34] . Manual building and maximum likelihood refinement were carried out using COOT [35] and REFMAC5 [36] , respectively . The protease structures were solved by molecular replacement using PHASER [37] . A search model for AprB2 was derived from the coordinates of Bacillus Ak . 1 protease ( PDB code 1DBI [38] ) , identified using the FFAS server [39] . The search model was generated using the SCRWL server and consisted of all conserved side-chains with the remaining non-alanine/glycine residues truncated at the Cγ atom [40] . The initial model of AprB2 was subject to several iterations of manual building and refinement . The model was then subjected to automatic building using ARP/wARP [41] before the structure was completed by more cycles of manual building and refinement . The refined AprB2 structure with the I2 loop deleted was used as the MR search model for AprV2 and AprV2 . C141S . The initial models were subjected to simulated annealing using PHENIX [42] , [43] . Successive rounds of manual building and refinement incorporating TLS [44] generated the final models . Water molecules were added to all models using ARP/warp v 5 . 0 [45] , [46] . Structure validation was carried out using MolProbity [47] and COOT [35] . Refinement statistics for the structures determined are presented in Table 2 . The coordinates and structure factors are available from the Protein Data Bank ( 2LPA; 2LPC; 2LPC ) . Raw data and images are available from TARDIS ( www . tardis . edu . au ) [48] . Recombinant protease ( 0 . 5 µM in buffer A ) was incubated with increasing amounts of quenching solution ( 2 M KI and 1 mM Na2S2O3 ) and the change in the fluorescence emission intensity of the tryptophan residues ( λex290 nm/λem340 nm ) was measured using a Perkin-Elmer LS50B spectrofluorometer . The data were analysed as before [49] . The sheep virulence experiments were carried out in a PC2 containment facility at the Elizabeth Macarthur Agricultural Institute in accordance with the guidelines of the Australian Government Office of the Gene Technology Regulator and the Elizabeth Macarthur Agricultural Institute Animal Ethics Committee . These experiments were approved by the Elizabeth Macarthur Agricultural Institute Animal Ethics Committee .
|
Extracellular proteases are produced by many bacterial pathogens and are commonly involved in the degradation of the host extracellular matrix , facilitating invasion and colonization . One such pathogen is Dichelobacter nodosus , the causative agent of ovine footrot , a disease of major economic significance to the international sheep industry . D . nodosus secretes a number of serine proteases , which are thought to cause the tissue damage associated with virulent footrot . Our study showed that a D . nodosus mutant lacking one of the three secreted proteases , AprV2 , failed to cause virulent disease in sheep . We used x-ray crystallography to solve the structure of AprV2 and the closely related protease AprB2 . Our structures revealed an unusual extended disulphide-tethered loop that is located next to , but does not form part of , the primary substrate binding site . Through targeted mutagenesis studies we were able to show that this loop functions as an exosite , mediating effective enzyme-substrate interactions . Bioinformatic analyses suggest that subtilases from other pathogenic bacteria may contain this loop and may therefore utilize a similar mechanism . Our multidisciplinary research approach has provided a comprehensive understanding of the functional role of extracellular proteases in the pathogenesis of ovine footrot .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biochemistry/protein",
"folding",
"biochemistry/protein",
"chemistry",
"microbiology",
"infectious",
"diseases/bacterial",
"infections",
"biochemistry/structural",
"genomics"
] |
2010
|
The Subtilisin-Like Protease AprV2 Is Required for Virulence and Uses a Novel Disulphide-Tethered Exosite to Bind Substrates
|
Lymphatic filariasis ( LF ) is a socio-economically devastating mosquito-borne Neglected Tropical Disease caused by parasitic filarial nematodes . The interaction between the parasite and host , both mosquito and human , during infection , development and persistence is dynamic and delicately balanced . Manipulation of this interface to the detriment of the parasite is a promising potential avenue to develop disease therapies but is prevented by our very limited understanding of the host-parasite relationship . Exosomes are bioactive small vesicles ( 30–120 nm ) secreted by a wide range of cell types and involved in a wide range of physiological processes . Here , we report the identification and partial characterization of exosome-like vesicles ( ELVs ) released from the infective L3 stage of the human filarial parasite Brugia malayi . Exosome-like vesicles were isolated from parasites in culture media and electron microscopy and nanoparticle tracking analysis were used to confirm that vesicles produced by juvenile B . malayi are exosome-like based on size and morphology . We show that loss of parasite viability correlates with a time-dependent decay in vesicle size specificity and rate of release . The protein cargo of these vesicles is shown to include common exosomal protein markers and putative effector proteins . These Brugia-derived vesicles contain small RNA species that include microRNAs with host homology , suggesting a potential role in host manipulation . Confocal microscopy shows J774A . 1 , a murine macrophage cell line , internalize purified ELVs , and we demonstrate that these ELVs effectively stimulate a classically activated macrophage phenotype in J774A . 1 . To our knowledge , this is the first report of exosome-like vesicle release by a human parasitic nematode and our data suggest a novel mechanism by which human parasitic nematodes may actively direct the host responses to infection . Further interrogation of the makeup and function of these bioactive vesicles could seed new therapeutic strategies and unearth stage-specific diagnostic biomarkers .
The parasitic filarial nematodes Wuchereria bancrofti , Brugia malayi and B . timori are etiological agents of Lymphatic filariasis ( LF ) , a chronic and debilitating disease infecting over 120 million people in 73 endemic countries [1] . Adult parasites reside in the lymphatic vasculature of infected individuals and release larvae called microfilariae , which are taken up by vector mosquitoes during the blood meal . Parasites rapidly develop within the mosquito , molting twice to the infective L3 stage [2 , 3] before transmission to the definitive host during a subsequent blood meal . Following penetration of the vertebrate host via the puncture wound left by the mosquito , L3 stage parasites migrate to the lymphatics and undergo further growth and development , molting to the L4 stage and again to adulthood . The longevity of patent infection is remarkable; adults live for at least 8–10 years by general consensus . The ability of larval stages to successfully invade the host , and for adult worms to maintain infection for such an extended period of time , suggest filarial worms have developed strategies to both facilitate the establishment of infection and evade or manipulate the host immune response . Although the immunomodulatory capabilities of infecting larval and adult stage filarial worms have been well documented and reviewed [4–8] , the parasite effector molecules responsible for manipulating host biology and their mechanisms of release have been difficult to define . Actively secreted proteins have historically been considered the principal candidates and several secreted proteins have been identified with demonstrable bioactivity at the host-parasite interface [9–12] . Adding to these , the characterization of parasitic nematode secretomes has revealed a complex array of potential proteinaceous effectors [13–16] . Other types of effector , including molecules expressed on the parasite surface may have a role [17] and the emergence of small noncoding RNAs as cell-to-cell agents of genetic regulation [18–22] hint at exciting alternative mechanisms . Exosomes are a subtype of extracellular vesicle categorized by size ( 30–120 nm diameter ) and defined by a particular biogenic pathway [23]; exosomes are formed by inward budding of vesicles in the late endosomal pathway to create multivesicular endosomes that fuse with the plasma membrane to effect release [24 , 25] . Originally thought to be a means of cellular waste disposal , exosomes are now considered highly bioactive extracellular vesicles that facilitate cell-to-cell communication and are the focus of renewed investigation . The cargo of exosomes is complex and variable , containing bioactive proteins , functional mRNA , miRNA and other small non-coding RNA species [18 , 26] , likely reflecting both source and target environments . Fusion of the exosome to a target cell delivers this heterogeneous bioactive cargo and selectively alters the biology of the target tissue [19 , 21 , 26 , 27]; the isolation of exosomes from circulatory systems and an array of biofluids suggests effector sites can be far from the point of release . Parasites are known to release exosome-like vesicles [27–30] and it is compelling to hypothesize that bioactive molecules secreted by parasitic nematodes , packaged in exosomes , function as cell-to-cell effectors in the host-parasite interaction . Indeed recently , extracellular vesicles secreted by the gastrointestinal nematode Heligmosomoides polygyrus , containing proteins and small RNA species , have been shown to alter gene expression in host cells and suppress innate immune responses in mice [26] . Here we show that larval and adult stage B . malayi secrete prodigious quantities of extracellular vesicles in vitro whose size and morphology are consistent with exosomes . These exosome-like extracellular vesicles ( ELVs ) contain small RNA species , including specific miRNA and are enriched in miRNA that are identical to host miRNAs with known immunomodulatory roles [31–34] . The protein cargo of the vesicles is relatively scant but includes bioactive proteins , proteins with putative RNA binding properties and proteins commonly associated with exosomes [35] . The parasite ELVs are internalized by host macrophages and elicit a classically activated phenotype in these cells . The demonstration that filarial nematodes secrete exosomal RNA and proteins that potentially function at the host-parasite interface is significant . Defining this parasite effector toolkit exposes an array of new molecules that may be exploited in novel LF control strategies .
In order to ascertain whether exosomes are released by B . malayi , extracellular vesicles were isolated from parasites incubated in culture media using a filtration and ultracentrifugation protocol . We focused our initial discovery efforts on larval and adult stage parasites . L3 , adult male , and adult female B . malayi were incubated in vitro for 24 hour periods under standard culture conditions , and purified vesicle preparations were evaluated with electron microscopy ( EM ) . Infectious stage L3 parasites in culture release abundant 50–120 nm microvesicles consistent with the classical “deflated ball” morphology of mammalian and non-mammalian exosomes reported in the literature [36] ( Fig 1A & 1B ) . We refer to these as exosome-like vesicles ( ELVs ) throughout this manuscript , in recognition that they cannot be unequivocally designated as exosomes , rather than another class of extracellular vesicles , because their biogenesis has not been determined . Preparations from adult stage B . malayi were more heterogenous and dilute , not allowing for the definitive categorization of putative exosome-like vesicles ( Fig 1C ) . This , despite the fact a much higher mass of total parasite tissue was used for adult preparations as compared to larval preparations . These data suggest ELV release to be a predominantly larval phenomenon in B . malayi , a working hypothesis supported by analysis of RNA associated with the vesicles . We therefore chose to focus our subsequent experiments on L3 stage parasites . A compelling overall hypothesis for the function of B . malayi ELVs is that they mediate the secretion and trafficking to host cells of effector molecules that facilitate parasitism and the observation that ELV secretion occurs primarily in those parasite stages that infect the host and establish parasitemia is consistent with this narrative . To more accurately resolve the dynamics of ELV release in L3 B . malayi , we used a nanoparticle tracking analysis ( NTA ) system to measure vesicle output in a 72 hr in vitro time course . Media was collected from 300 worms after three successive 24 hr incubation periods , vesicles were purified by ultracentrifugation as before and individual vesicle preparations were analyzed via NanoSight LM10 as shown in Fig 2 ( sample recording: S1 Video ) . Day 1 ( 0–24 hr in culture ) preparations reveal a prolific ELV release rate ( > 9 , 000 ELVs/parasite/min ) with a very narrow size distribution centered at ∼90 nm . Day 2 ( 24–48 hr in culture ) preparations show an essentially equivalent rate of release , but a stark broadening of the size distribution . Day 3 ( 48–72 hr in culture ) preparations are associated with significantly lower levels of release ( <4 , 000 ELVs/parasite/min ) and an even wider multimodal size distribution . These data suggest an overall time-dependent decay in vesicle rate of release and size specificity , which correlates to decreased L3 viability in vitro . The release of considerable quantities of precisely-sized ELVs in viable worms ( Days 1–2 ) is followed by the release of smaller quantities of a broader size range of particles that potentially include larger membrane vesicles and apoptotic blebs ( Days 2–3 ) . This suggests an active and regulated mechanism of ELV release in healthy and viable L3 stage parasites , as opposed to a passive mode of noisy cellular deterioration . The protein content of B . malayi ELVs was determined using nanoscale liquid chromatography coupled to tandem mass spectrometry ( nano LC/MS/MS ) . A total of 32 proteins each containing at least two unique peptides were identified using MASCOT ( Table 1 ) . Specific proteins identified within the pellet included characteristic markers of exosomes including Hsp70 , elongation factor-1α , elongation factor-2 , actin , and Rab-1 . In addition , over 80% of the proteins identified are orthologous to proteins identified in mammalian exosome proteomes , strongly suggesting that these vesicles are exosome-like in nature and supporting our ELV designation here . Interestingly , this set of vesicle-specific proteins is entirely distinct from the proteins previously identified in pre- and post-molt L3 secretions [37] . UniProt-GOA and quickGO were used to sort proteins into functional groups based on assigned gene ontology ( GO ) terms [38 , 39] , as shown in Fig 3 . Based on GO annotations , 20% of the proteins identified are involved in binding of bioactive molecules including nucleic acids and other proteins , 16% function in the transport of various ions and proteins and 14% are ribosomal proteins . In addition , a large fraction of proteins identified ( 21% ) appear to be involved in various metabolic processes including hydrolase and transferase activities while the remaining 29% comprises proteins with translational , cytoskeletal and other functions . Included in the list of Brugia ELV proteins are potential effector molecules . Bm-CPL-1 is a cathepsin L-like cysteine protease robustly expressed across the B . malayi life cycle [40] . Upregulation of Bm-cpl-1 expression coincides with transition between life cycle stages and an important role in the modulation of parasite molting has been confirmed [41–43] . This is the first demonstration that B . malayi secretes CPL-1 although other cathepsin-like cysteine proteases have been identified in the B . malayi secretome [14 , 37] and a cathepsin L-like molecule is secreted by intra-mammalian stage Haemonchus contortus [44] . The exogenous function of exosomal Bm-CPL-1 is not clear but evidence points to some manipulation of the host-parasite interface . In a previous study , we suppressed Bm-cpl-1 expression using in vivo RNAi during the mosquito life stages [42] . Loss-of-function reduced prevalence of infection in mosquitoes by nearly 40% , suggesting Bm-CPL-1 is important for establishing or maintaining parasitemia . In flatworms , an immunomodulatory role for secreted cathepsin L-like proteases is better established [45]; in Fasciola infection cathepsin L contributes to the permissive polarized Th2 > Th1 host response . The proteomic profiles of parasitic helminth exosomes are broad in range; for example , over 350 proteins were identified in the putative exosomes secreted by Heligmosomoides polygyrus [26] whilst 45 and 79 proteins were identified in exosome-like vesicles from Echinostoma caproni and Fasciola hepatica , respectively [46] . The B . malayi L3 stage profile identified here is relatively scant but consistent with this broad distribution . It may be that this is a stage-specific observation and ELV secreted by other B . malayi life stages display a more complex and abundant protein cargo tailored to distinct functional demands . Reflecting the small RNA component of these ELVs ( see later sections ) , it may also be that larval stage Brugia ELVs are primarily vehicles for protected RNA secretion . Replication of the experiments conducted here might add depth to the MS data set and identify further ELV-associated proteins . We probed larval and adult microvesicle preparations for the presence of small RNA species . Exosomes have been found to contain both non-coding RNAs ( ncRNAs ) and messenger RNAs ( mRNAs ) in a diverse range of species and cell types . Of particular interest to us was the potential presence of small non-coding RNAs , including microRNAs ( miRNAs ) , that could potentially mediate parasite-parasite communication or modulate host gene expression . Small RNA species were preferentially isolated from putative ELV-containing pellets and examined with an Agilent Bioanalyzer . The microvesicle fractions of L3 B . malayi ( 24 hr incubations of 300 worms ) revealed an abundance of small RNA species in the 25–200 nt range ( Fig 4 ) . Much less RNA was detected from incubations of adult male and female B . malayi ( 24 hr incubations of 30 adult worms ) , despite the much higher mass of tissue in adult stage culture media . This lack of correlation between total parasite tissue material and RNA yield , coupled to the differential quality of larval and adult microvesicle preparations as evaluated by EM , further indicates that ELV release is primarily a characteristic of larval-stage parasites and perhaps more biologically relevant to early parasite infection . To more fully investigate the nucleic acid contents of these newly discovered vesicles , we carried out RNA-Seq with both L3 ELV and tissue-derived small RNAs . Reads generated by Illumina sequencing were processed and used to seed an miRNA discovery and abundance estimation pipeline using miRDeep2 [47] ( read statistics and raw miRNA abundances can be found in S1 Table ) . To compare ELV and cellular RNA abundance , miRNA expression was normalized to the total miRNA read count within each sample . miRNA discovery and profiling was augmented with data from previously discovered miRNAs in closely related nematode species to help overcome gaps in the B . malayi draft genome assembly ( see Methods ) . Fig 5A compares normalized miRNA expression between ELV and tissue for the 20 most abundant miRNAs in each sample . Although there is considerable conservation in relative miRNAs abundances , there are some notable observations and exceptions . Bma-let-7 is significantly enriched in L3 ELVs as compared to L3 tissue , where it does not appear among the 20 most abundant miRNAs . Bma-let-7 , along with four other B . malayi mature miRNAs found in ELVs ( bma-miR-1 , bma-miR-9 , bma-miR-92 , and bma-miR-100b ) , share perfect sequence identity with host ( Homo sapiens ) mature miRNAs , as shown in Fig 5B . Additionally , bma-miR-34 shares near perfect sequence identity with its H . sapiens homolog . 11 B . malayi miRNAs also share common seed sites with H . sapiens miRNAs ( Fig 5C ) . Brugia ELV miRNA sequences were more broadly clustered by putative seed site and aligned to miRNAs from the soil-transmitted parasitic nematode Ascaris suum , the free living model nematode Caenorhabditis elegans , and mammalian host species H . sapiens and Mus musculus ( Fig 6 and S1 Fig ) . In all cases , Brugia ELV miRNAs that share common seed sites with host miRNAs have one-to-one A . suum orthologs . In some cases , parasite miRNAs are better conserved in mammalian hosts than in C . elegans ( e . g . , bma-miR-9 , bma-miR-993 , and bma-miR-100b/c ) . We examined the complement of the most abundant Brugia ELV-associated miRNAs with respect to very recent investigations of miRNAs released by other parasitic nematode species and found circulating in host biofluids [26 , 48–50] . Common markers include let-7 , lin-4 , miR-34 , miR-71 , miR-92 , and miR-100c ( Fig 7A and 7B ) . While all members of this subset share seed site sequence identity with mammalian host miRNAs , lin-4 , miR-34 , miR-71 , and miR-100c are sufficiently diverged from host miRNAs over their full length mature miRNA sequence and can potentially serve as biomarkers of filarial infection . Additionally , we compared the complements of the 20 most abundant Brugia ELV and H . polygyrus exosomal [26] miRNAs , identifying six miRNAs shared between these vesicles and a large number of miRNAs unique to each species ( Fig 7C ) . Enrichment of bma-let-7 and the high fractional presence of other parasite miRNAs sharing perfect or high homology to host miRNAs , leads us to speculate about a potential ELV-mediated mechanism by which parasite RNAs can be used to efficiently direct aspects of gene expression in host cells . Targets of endogenous let-7 family miRNAs in vertebrates include oncogenes , as well as genes involved in proliferation , apoptosis , and innate immunity [51–53] . Let-7 is intricately involved in macrophage polarization and responses to pathogen challenge [31 , 33 , 54] , and the altering of host let-7 expression therefore represents a potentially advantageous point of intervention for an invading parasite . Live pathogens down-regulate the expression of let-7 family miRNAs , and let-7 miRNAs act on toll-like receptors ( e . g . TLR4 ) that directly mediate macrophage responses [54–56] . Clearly , there is an important association between macrophage response to pathogens and let-7 expression . Our observation that B . malayi secrete let-7 and other potential modulators of host gene expression identifies a mechanism by which this host immune response might be manipulated . Supporting this hypothesis , let-7 and other miRNAs with host conservation have been identified in immunomodulatory H . polygyrus adult stage exosomes [26] . To fully dissect this hypothesis , a broad investigation of the interaction of ELV miRNAs and host immune cells in vivo is needed . Macrophages are critical mediators of the early immune response to invasive Brugia parasites [8] . To test the hypothesis that secreted Brugia ELVs interact with host macrophages , we used fluorescent lipophilic dyes to visualize the interaction between J774A . 1 murine macrophages and ELVs . This cell line was chosen because it is commercially available , can be cultured readily and because it recapitulates the biology of primary macrophages and dendritic cells [57] . ELVs were labeled with PKH67 , a green fluorescent dye , and incubated with J774A . 1 labeled with PKH26 , a red fluorescent dye . Confocal microscopy revealed efficient internalization of the ELVs by this macrophage cell line ( Fig 8 ) . Internalization was observed diffusely throughout the cell cytoplasm with focus around membrane-rich puncta associated with the surface of the macrophages ( Fig 8B ) . This pattern of internalization is consistent with other studies describing a phagocytic route of vesicle internalization [58 , 59] . Macrophages were counterstained with DAPI to determine the efficiency of cell labeling and ELV uptake . PKH26-labeling of J774A . 1 was very efficient and all cells were visualized although intensity of labeling was variable ( Fig 8D ) . Approximately 40–50% of macrophages internalized labeled ELVs to some degree ( Fig 8E ) with approximately 10% of macrophages internalizing ELVs at markedly higher levels ( Fig 8E ) . There was no correlation between strong PKH 26-labelling of macrophages and vesicle uptake indicating internalization is not a factor of receptiveness to labeling . Macrophage activation is dichotomous; classically activated macrophages ( CAMΦ ) are elicited by LPS or IFN-γ and have a generally pro-inflammatory phenotype whereas alternatively activated macrophages ( AAMΦ ) , driven by IL-4 and IL-13 , appear immunosuppressive or anti-inflammatory . Helminth infection is typically associated with the AAMΦ pathway although both CAMΦ and AAMΦ are involved in the immune response to , and immunopathology caused by , Brugia infection . Experiments demonstrate different Brugia preparations can generate both CAMΦ and AAMΦ activation phenotypes; dead and moribund worms and worm lysates produce CAMΦ [60] but live worms and complete excretory/secretory ( ES ) preparations drive AAMΦ [61–63] . To test the hypothesis that ELVs activate host macrophages , J774A . 1 were treated with purified ELV preparations and their cytokine/chemokine responses monitored . J774A . 1 were treated for 48 hrs with approximately 4 × 108 L3 stage vesicles , purified from in vitro culture medium by ultracentrifugation . The macrophage response was assayed using the Milliplex MAP Mouse Cytokine/Chemokine kit ( EDM Millipore ) interfaced with a Bio-Plex System ( Bio-Rad ) utilizing Luminex xMAP technology , a platform capable of simultaneously identifying and quantifying 32 cytokines/chemokines . Vesicle treatment effectively activated J774A . 1 macrophages with significant increases in G-CSF , MCP-1 , IL-6 and MIP-2 levels compared to control macrophages treated with naïve RPMI 1640 culture media , ( p ≤ 0 . 001 ) ( Fig 9A ) . Smaller increases in LIX , RANTES and TNF-α were also noted . Healthy , viable L3 stage parasites produced an almost identical response ( Fig 9A ) , the only difference being a modest but significant enhancement of G-CSF stimulation by the viable parasites ( p < 0 . 001 ) , suggesting that the dominant parasite immunogen ( s ) are found in the vesicle pellet . Finally , parasite culture media from which the ELVs had been removed by centrifugation did not produce this response , nor did live schistosomes ( S . mansoni cercaria ) or their secreted vesicles ( S2 Fig ) suggesting the Brugia-associated activation is specific to this parasite and not a general response to helminths or their secreted vesicles . The activation profile observed would be considered more indicative of a CAMΦ response than AAMΦ; to confirm the response was CAMΦ-like , we compared it to the response elicited by LPS ( 200 ng/mL ) . The only significant differences were that ELV treatment stimulated G-CSF and IL-6 less effectively ( p < 0 . 001 ) and stimulated MCP-1 more effectively ( p < 0 . 001 ) than LPS ( Fig 9B ) . The overall conservation of response , however , indicates these ELVs generate a CAMΦ phenotype . Since Wolbachia , the endosymbiont present in filarial nematodes , lack LPS biosynthetic capacity it seemed unlikely our CAMΦ-like response was driven by LPS-like contamination but to rule this out , endotoxin levels in our vesicle preparation were determined commercially ( Lonza , Walkersville , MD ) . LPS-like activity was present ( 0 . 003 ng/mL ) but at a concentration several orders of magnitude lower than the minimum dose required to stimulate J774A . 1 macrophages [64] . As expected , treatment of macrophages with this low LPS dose was insufficient for activation ( Fig 9C ) indicating that the CAMΦ response we observe is not due to an LPS-like component in our preparation . Since the stimulation of an AAMΦ phenotype by live Brugia and ES preparations thereof in vivo and in vitro has been well established [61–63] it might be expected that Brugia ELV preparations also stimulate a AAMΦ phenotype , especially since complete Brugia ES preparations are likely to include ELVs similar to those examined here , albeit at reduced concentrations . We observed a response consistent with a CAMΦ phenotype , however , although without the acute elevation in IL-β and TNF-α production others have seen in response to LPS [60] . One interpretation is that the CAMΦ > AAMΦ phenotype may be a somewhat artificial function of the homogenous J774A . 1 monoculture used here as other studies describing a AAMΦ phenotype often use PBMC or other heterogeneous primary cell types . It would be instructive to monitor the responses of such mixed cell populations to the ELV preparation . Additionally , although the murine model is regarded as valuable for illuminating both how parasites establish themselves and the early host immune response , J774A . 1 may not be optimal for studying this particular Brugia-host interaction and optimization with other murine or human cells may be required . Another interpretation , however , is that the purified ELVs examined here should be considered a distinct and specific fraction of the highly complex immunogenic facade presented by filarial parasites and may elicit a genuine CAMΦ phenotype when examined in isolation . Supporting this interpretation , exosomes isolated from other biological systems effectively generate a CAMΦ phenotype [59 , 65 , 66] . A key mediator of this pro-inflammatory response is Hsp70 [65] , which was identified in our ELV proteomic profile . In summary , irrespective of the polarity of macrophage activation phenotype , our results unequivocally identify secreted ELVs as distinct parasite-derived structures capable of activating the host immune system . A picture is emerging that parasitic helminths secrete functional exosome-like vesicles . The protein and small RNA cargo of these vesicles have putative effector functions at the host-parasite interface and potentially serve to create conditions favorable to the establishment or maintenance of infection . The identification of these cell-to-cell effector structures is exciting and prompts further investigation of their functional relevance . In particular , it will be important to describe the roles of individual miRNAs and proteins contained within the ELVs , to identify the host molecular targets being manipulated in vivo , and reveal any conserved or stage-specific effectors secreted across the parasite life cycle . Another intriguing question is whether or not there is any specificity or selectivity in host cells or tissues targeted and if so , what molecular mechanisms underscore this specificity . Addressing such questions will illuminate the fundamental interactions that occur between parasite and host , and may open previously unexploited opportunities for parasite control and diagnostics .
Aedes aegypti ( Black eyed Liverpool strain , LVP ) , previously selected for susceptibility to infection with Brugia malayi [67] , were maintained in controlled conditions ( 27°C ± 1°C and 75% ± 5% relative humidity ) with a 16:8 photoperiod . Adult mosquitoes were fed a diet of 10% sucrose . Approximately 4 , 000 and 2 , 600 mosquitos were used for proteomics and RNA sequencing , respectively . For proteomics and transcriptomics , B . malayi microfilaria ( mf ) infected cat blood was obtained from the University of Georgia NIH/NIAID Filariasis Research Reagent Resource Center ( FR3 ) . Blood containing the parasites was diluted with defibrinated sheep’s blood ( Hemostat Laboratories , CA , USA ) to achieve a concentration of 80–100 mf per 20μL . To establish infection , 3- to 5-day-old Ae . Aegypti ( LVP ) were allowed to feed for one hour on a glass membrane feeder . Mosquitoes were sucrose-starved for 24 hrs prior to blood feeding and those that did not take a blood meal were removed . Infected mosquitoes were maintained in the above described conditions for 13–15 days post infection ( dpi ) to allow development of parasites . In exploratory studies , larval ( 300 L3 ) and adult ( 30 male or 30 female ) B . malayi were procured from the FR3 . On arrival , parasites were cultured in 50 mL RPMI 1640 ( Sigma-Aldrich , St . Louis , MO ) at 37°C ( 5% CO2 ) . Cell culture media was collected and replaced at 24 hr intervals for up to 72 hrs to collect secreted ELVs . For downstream sequencing and proteomics , B . malayi ( 13–15 dpi ) were locally collected using methods described by FR3 . Briefly , infected mosquitoes were immobilized by cooling to 4°C for 15 minutes . Immobilized mosquitoes were crushed in a mortar containing 5 ml of chilled Hanks’ balanced salt solution ( HBSS , pH 7 . 0 ) containing pen-strep ( 0 . 4 units penicillin/ml , 0 . 4 mcg streptomycin/ml ) . Mosquitoes were then rinsed onto a 150 mesh sieve contained in a deep well plastic petri dish and washed 3–4 times using fresh chilled HBSS + pen-strep . Sieves were then placed into petri dishes containing warm ( 40°C ) HBSS + pen-strep to allow infective larvae to migrate out . Sieves were transferred to new deep well petri dishes containing fresh warm HBSS every 30 minutes . Collected parasites were washed twice with warm HBSS + pen-strep , placed into 25 mL RPMI 1640 containing pen-strep ( 0 . 4 units penicillin/ml , 0 . 4 mcg streptomycin/ml ) and held at 37°C , 5% CO2 for 24 hrs to collect secreted ELVs . Differential centrifugation was used to isolate ELVs from 25 or 50 mL aliquots of Brugia culture media . Aliquots were collected from 24 hr incubations of larval or adult worms in culture media . Lower speed centrifugation and filtration steps were used to remove contaminating cells ( 300 × g , 10 mins ) and cellular debris ( 10 , 000 × g , 15 mins ) . The resulting supernatants underwent filtration through 0 . 22 μm filters and ultracentrifugation at 105 , 000 × g for 90 mins to pellet ELVs . Pellets were then washed with cold phosphate-buffered saline ( PBS ) and a final spin was carried out at 105 , 000 × g for 90 mins . Supernatants were discarded and pellets were resuspended in small volumes ( 30–250 uL ) of PBS for imaging , sequencing , and proteomics , and RPMI for immunological assays . Samples were kept on ice and centrifugation steps were carried out at 4°C . Resuspended ELVs were stored at −80°C . Small aliquots of ELV suspension ( 3 μl ) were applied to carbon coated 200 mesh copper grids and negatively stained with 2% uranyl acetate . Images were taken using a JEOL 2100 scanning and transmission electron microscope ( Japan Electron Optics Laboratories , Akishima , Japan ) at the Microscopy and NanoImaging Facility ( Iowa State University ) . Nanoparticle tracking analysis was carried out with the NanoSight LM10 ( NanoSight Ltd . , Amesbury , UK ) to ascertain the size and frequency distribution of individual vesicle preparations , assayed in triplicate . The Brownian motion of particles in solution is related back to particle sizes and numbers , allowing better statistical resolution of vesicle size and concentration [68] . Protein was isolated from purified exosome-like vesicles for proteomic analysis ( System Biosciences ) . Briefly , samples were modified with 10% SDS to a final concentration of 2% SDS , heated at 100°C for 15 minutes and clarified by centrifugation . Protein concentration was determined using a Qubit fluorometry assay ( Invitrogen ) . 15 μg of material was processed by SDS-PAGE using a 10% Bis-Tris homogeneous gel and the MES buffer system . In-gel digestion with trypsin was done at 37°C for 4 hrs using a ProGest robot ( DigiLab , Marlborough , MA ) . The digested sample was analyzed by nano LC-MS/MS analysis using a Waters NanoAcquity HPLC system interfaced to a ThermoFisher Q Exactive . Data were searched against a copy of the B . malayi UniProt database ( taxon ID: 6278 ) using a locally running copy of MASCOT ( Matrix Science Ltd . , London , UK ) . The search was restricted using the following parameters; maximum missed cleavages = 2 , fixed modifications = carbamidomethyl ( C ) , variable modifications = Oxidation ( M ) , Acetyl ( N-term ) , Pyro-Glu ( N-term Q ) and Deamidation ( N , Q ) , a peptide mass tolerance of 10 ppm , and a fragment mass tolerance of 0 . 02 Da . Mascot DAT files were parsed into the Scaffold software for validation , filtering and to create a nonredundant list per sample . Data were filtered using a minimum protein value of 90% , a minimum peptide value of 50% ( Prophet scores ) and requiring at least two unique peptides per protein . For detection of RNA species in ELV preparations , small RNAs were preferentially isolated from vesicle-containing pellets using the miRCURY RNA Isolation Kit ( Exiqon , Vedbaek , Denmark ) and RNA samples were examined with an Agilent 2100 Bioanalyzer using the RNA 6000 Nano Kit . For small RNA sequencing ( RNA-Seq ) , total RNA was isolated from ELVs released by ∼5 , 000 L3s over a 24 hr incubation period using the Total RNA and Protein Isolation Kit ( Invitrogen , Carlsbad , CA ) . In parallel , total RNA was isolated from whole worm tissue using a TRIzol ( Invitrogen ) protocol , where a 6 hr precipitation step was carried out at -80°C to improve small RNA recovery . RNA NGS libraries were constructed using modified Illumina adapter methods using SBI’s XRNA Sample Preparation Kit ( System Biosciences , Mountain View , CA ) and indexed with separate bar codes for multiplex sequencing on an Illumina MiSeq v3 instrument using a 2 × 75 bp paired end run setting . Raw reads were trimmed to remove adapter sequences , filtered by quality score , and de-multiplexed using the FASTX-Toolkit [69] ( sequencing data are deposited with the NCBI SRA under project number PRJNA285132 ) . The miRDeep2 pipeline was used to map short RNA reads ( >15 nt ) to the B . malayi genome for miRNA discovery , and to estimate and normalize miRNA abundances with respect to total miRNA read count . Nematode precursor and mature miRNA sequences deposited into miRBase [70] were used in the pipeline , including known B . pahangi , Caenorhabditis elegans , Ascaris suum , Haemonchus contortus , and Strongyloides ratti miRNAs . Non-mapped reads were ranked by abundance , filtered for homology against known miRNAs in the phylum Nematoda using BLASTn [71] , and incorporated for final quantification of abundance with the miRDeep quantifier script , allowing for capture of miRNAs that did not map to the B . malayi assembly due to sequencing gaps . The ggplot2 package [72] of the statistical programming language R was used to organize and visualize comparisons between vesicular and tissue RNA samples . J774A . 1 murine macrophages ( ATCC , Manassas , VA ) were maintained in complete tissue culture medium ( Dulbecco’s modified Eagle’s medium , 25 mM HEPES , pH 7 . 4 supplemented with 2 mM L-glutamine , 100 U/mL penicillin , 100 μg/mL streptomycin , 0 . 05 μM 2-mercaptoethanol , and 10% heat-inactivated fetal bovine serum ) at 37°C and 5% CO2 . 24 hrs prior to assays , 400 μL cells were plated in standard 24-well plates at a density of 5 × 105 cells/well . Exosome-like vesicles were purified from a 24 hr culture of 300 Brugia malayi L3 parasites as described above and labeled with the green fluorescent dye , PKH67 ( Sigma-Aldrich , St Louis , MO , USA ) , according to the manufacturer’s instructions . ELVs were incubated with PKH67 for 5 min at room temperature and the reaction terminated by addition of 1% BSA in PBS . RPMI 1640 media was added , mixed and centrifuged at 105 , 000 × g for 1 hr to separate ELV-bound PKH67 from excess PKH67 . Labeled ELV were washed again then resuspended in an appropriate volume of complete tissue culture medium ( Dulbecco’s modified Eagles medium , 25 mM HEPES , pH 7 . 4 supplemented with 2 mM L-glutamine , 100 U/mL penicillin , 100 μg/mL streptomycin , 0 . 05 μM 2-mercaptoethanol and 10% heat-inactivated fetal bovine serum ) . J774A . 1 were labeled with red fluorescent lipophilic dye , PKH26 ( Sigma-Aldrich , St Louis , MO ) , according to the manufacturer’s instructions . Macrophages were incubated with PKH26 for 5 min at room temperature and the reaction terminated by addition of 1% BSA . To remove excess unbound dye , samples were centrifuged at 400 × g for 10 minutes at room temperature and the supernatant discarded . Centrifugation was repeated three more times using 10 ml of complete media to ensure full removal of unbound dye and the cells were re-suspended in 1 mL of complete medium . Approximately 3 × 105 labeled cells were plated onto sterile coverslips and incubated overnight at 37°C/5% CO2 . Labeled ELV suspension ( approximately 3 × 107 per coverslip ) was added to labeled J774A . 1 and incubated for 6 hrs . Cells were washed 5 times with ice-cold PBS to remove excess labeled ELVs , the cells fixed in 4% paraformaldehyde ( Sigma-Aldrich ) , washed and counterstained with DAPI before mounting and storage at 4°C . Preparations were visualized using a Leica TCS SP5 X Confocal/multiphoton microscope system ( Leica Microsystems Inc . , Buffalo Grove , IL ) . Triplicate wells of adhered J774A . 1 were treated with approximately 4 × 108 purified L3 stage ELVs . The ELVs were purified by ultracentrifugation as previously described , resuspended in RPMI 1640 medium ( Gibco/Life Technologies , Carlsbad , CA ) and quantified by nanoparticle tracking analysis . Other treatments were similar volumes of vesicle depleted L3 culture medium ( supernatant created following pelleting of ELV fraction from spent parasite culture medium ) , live B . malayi L3 parasites ( 10 worms/well ) , lipopolysaccharide ( LPS; final concentration 200 ng/mL ) ( Sigma-Aldrich , St . Louis , MO ) , naïve RPMI 1640 culture medium and various combinations of these conditions . Supernatants from these cell cultures ( 400 μL/well ) were collected 24 or 48 hrs post-treatment and centrifuged briefly ( 2 , 000 × g for 10 min ) to remove non-adhered cells and cell debris before being analyzed for the presence of cytokines/chemokines . The Milliplex MAP Mouse Cytokine/Chemokine kit ( EDM Millipore , Billerica , MA ) interfaced with a Bio-Plex System ( Bio-Rad , Hercules , CA ) utilizing Luminex xMAP technology ( Luminex , Austin , TX ) allowed the simultaneous identification and quantification of the following analytes in the cell culture supernatant: Eotaxin , G-CSF , GM-CSF , IFNγ , IL-1α , M-CSF , IL-1β , IL-2 , IL-3 , IL-4 , IL-5 , IL-6 , IL-7 , IL-10 , IL-12 ( p40 ) , IL-13 , IL-15 , IL-17 , IP-10 , MIP-2 , KC , LIF , LIX , MCP-1 , MIP-1α , MIP-1β , MIG , RANTES , TNFα , IL-12 ( p70 ) , VEGF , IL-9 . Briefly , experimental samples , background , standards and controls were added to a 96-well plate and combined with equal volumes of pre-mixed , antibody coated magnetic beads; the plate was sealed and incubated overnight at 4°C . Following washing , 25 μL of detection antibody was added and the plate incubated for one hour at room temperature with shaking . Streptavidin-Phycoerythrin ( 25 μL ) was added to each well and the plate incubated for a further hour at room temperature before washing . Finally , 150 μL assay buffer was added to all wells and fluorescence immediately recorded . Median fluorescent intensity data were analyzed as recommended using a five-parameter logistic curve-fitting method for calculating cytokine/chemokine concentration . Triplicate wells of adhered J774A . 1 cells , prepared as described above , were treated with LPS ( final concentration 200 ng/mL or 0 . 003 ng/mL ) , approximately 4 × 108 purified L3 stage ELVs as described above , or RPMI 1640 as negative control . Cell culture supernatants were collected 24 hrs after treatment , cleared via centrifugation as described previously and assayed for G-CSF using a Mouse G-CSF Quantikine ELISA kit ( R&D Systems , Minneapolis , MN ) . Standard curves were generated using Prism 6 software ( GraphPad Software , San Diego , CA ) and sample G-CSF concentrations determined by regression analysis . For analysis of Luminex data , Tukey’s test was used to compare overall treatments while multiple t-tests , incorporating the Holm-Sidak method to correct for multiple comparisons , were used to compare individual chemokines/cytokines following treatments . t-tests were used to compare treatment groups following ELISA analysis . All statistical analyses were performed using Prism 6 for Mac ( Graphpad ) .
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Lymphatic filariasis is caused by parasitic nematodes that invade and occupy the host lymphatic system . The extent of lymphatic filariasis is staggering , with over 120 million people infected in 73 endemic countries and an estimated 40 million people suffering from a range of disfiguring and debilitating clinical manifestations of this disease . The mechanisms by which these medically important parasites navigate the host immune response to establish infection are not yet fully understood . In this study , we identify exosome-like vesicles ( ELVs ) that are abundantly released from infective stage L3 Brugia malayi , an etiological agent of human lymphatic filariasis . We show that these vesicles have a narrow size distribution and morphology consistent with classical exosomes , and that they contain common exosomal protein markers , putative effector proteins , as well as small regulatory RNAs . We show that ELVs are enriched with microRNAs that are perfectly conserved between parasite and host , suggesting a potentially novel mechanism by which filarial worms can actively manipulate host gene expression . We demonstrate that parasite ELVs are internalized by macrophages and elicit a classically activated phenotype in these host cells . The discovery of exosome-like vesicle release by human nematode parasites newly enlightens the roadmap to understanding the pathology of LF and related helminthiases . These vesicles also present promising new targets for intervention and diagnostics .
|
[
"Abstract",
"Introduction",
"Results",
"and",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
|
Release of Small RNA-containing Exosome-like Vesicles from the Human Filarial Parasite Brugia malayi
|
Fighting antibiotic resistance requires a deeper understanding of the genetic factors that determine the antibiotic susceptibility of bacteria . Here we describe a chemical-genomic screen in Escherichia coli K-12 that was designed to discover new aspects of antibiotic resistance by focusing on a set of 26 antibiotics and other stresses with poorly characterized mode-of-action and determinants of resistance . We show that the screen identifies new resistance determinants for these antibiotics including a common signature from two antimicrobials , kasugamycin and blasticidin S , used to treat crop diseases like rice blast and fire blight . Following this signature , we further investigated the mechanistic basis for susceptibility to kasugamycin and blasticidin S in E . coli using both genetic and biochemical approaches . We provide evidence that these compounds hijack an overlapping set of peptide ABC-importers to enter the bacterial cell . Loss of uptake may be an underappreciated mechanism for the development of kasugamycin resistance in bacterial plant pathogens .
The emerging threat of antibiotic resistance [1] necessitates new efforts and ideas to control bacterial pathogens . Mapping the determinants of antibiotic resistance in bacteria will be critical for evaluating new antibiotics . In addition to the direct target of the antibiotic , drug efflux , drug permeability , and stress response pathways all contribute to resistance [2] . Global genetic approaches such as chemical-genomic screens , which measure the sensitivities of a large library of mutants to a set of stresses , can be a first-step in discovering resistance determinants and characterizing the mode-of-action of antibiotics . Chemical-genomic screens in the model bacterium Escherichia coli K-12 [3–10] have already provided a critical resource for the bacterial research community and catalyzed insights into molecular systems critical for bacterial viability , stress survival , and resistance to antibiotics [11–15] . Despite these successes , the chemical-genetic space of E . coli remains largely unexplored , as only slightly more than 50% of the genes in E . coli K-12 are “responsive” , defined as having a statistically significant fitness effect for at least one stress [8] . Reasoning that the remaining unresponsive genes may encode resistance determinants to previously untested antibiotics and stresses , we conducted a new chemical-genomic screen of the previously screened library [8 , 10] focusing on antibiotics with unique or unknown modes of action . We integrated the data from this screen with the results of Nichols et al . [8] to create an expanded chemical-genomics dataset that revealed new determinants of antibiotic resistance . From this dataset , we further investigated resistance to the antibiotics kasugamycin ( Ksg ) [16] and blasticidin S ( BcS ) [17] . Both antibiotics were discovered in the mid-20th century as antifungals effective against Magnaporthe oryzae , the causative agent of rice blast . Kasugamycin has a continuing use in the treatment of M . oryzae , bacterial pathogens of rice , and Erwinia amylovora , the bacterial pathogen that causes fire blight . We discovered that both antibiotics enter bacterial cells using illicit transport , the active uptake of non-physiological compounds , through two peptide ABC-importers . We suggest that loss-of-function mutations in homologous peptide ABC-importers are likely to play a role in the development of kasugamycin resistance for E . amylovora and many other pathogens .
We tested the sensitivities of 3975 mutants of E . coli K-12 to 57 stresses , split between new and previously screened conditions . The new stresses included neglected antibacterial compounds and other noxious chemicals with poorly characterized modes of action ( Table 1 ) . We pinned the arrayed mutant library onto agar plates containing each compound , imaged the plates after suitable colony growth , and quantified colony opacity using the image analysis software Iris [10] . We assigned fitness-scores to each mutant , using an in-house software package that built upon previous analyses [8 , 18] by implementing additional filtering and normalization steps to improve data quality ( Methods ) . These fitness-scores represent the statistical significance of a change in colony size for a particular condition , with negative and positive fitness-scores representing sensitivity and resistance , respectively . After reanalyzing the original images from the Nichols et al . [8] screen with our improved workflow , we integrated both datasets . Fitness-scores from stresses present in both screens were significantly correlated ( Fig 1A ) . A responsive gene is defined as having at least one conditional-phenotype in the dataset . We identified more than 5 , 000 conditional-phenotypes for the 26 new stresses , as well as more than 500 additional responsive genes from the 57 stresses tested ( 14% of the library ) ( Fig 1B ) . The conditional-phenotypes that identified additional responsive genes were spread evenly throughout the current screen , ranging from 5 to 54 conditional-phenotypes per condition from within the set of new responsive genes . The integrated dataset also contained more than double the number of statistically significant correlations between genes than each screen considered separately ( Fig 1C ) . This increase in the number of significant correlations came from two factors . First , the integrated dataset captured more conditional-phenotypes that in turn drove higher correlations for some genes . Second , smaller datasets require more stringent cutoffs for statistical significance that exclude a sizable fraction of the correlations ( S1 Fig ) . Because of these factors , integration of chemical genomic screen with a larger dataset was critical for extracting as much information as possible from the new conditions . We use this integrated dataset ( S1 Dataset ) in all further analyses . The pathways that sense and respond to different stresses are nearly as diverse as the types of stress that are encountered . Genes that are involved in drug permeability , drug efflux and degradation , stress responses , and the drug target all contribute to resistance . The chemical-genomic screen revealed a global picture of antibiotic resistance that reflected this diversity in mechanism ( Fig 2A ) . Growth at 10°C resulted in multiple sensitivities from genes in pathways known to be affected by cold shock , with almost double the number of cold-sensitive mutations as the next lowest temperature ( 16°C ) . 10°C-sensitive genes were most enriched for COG categories [19] related to translation ( J , p = 0 . 005 ) and DNA replication and repair ( L , p = 0 . 01 ) , with 20% of the sensitive genes falling into one of these two categories ( Fig 2A , S1 Table ) . Ribosome assembly and function is particularly sensitive to temperatures of 10°C and below [20] , which may explain part of the expansion of sensitivities at this temperature . Deletions in the trans-translation complex ( ΔssrA and ΔsmpB ) were sensitive to aminoglycosides and macrolides ( Fig 2B ) , while the deletion of the alternative ribosome rescue factor ( ΔarfA ) was sensitized to members of the tetracycline family and chloramphenicol ( Fig 2B ) . Deletions in the cytoplasmic protease HslUV ( ΔhslU , V ) and adaptors to the inner membrane protease FtsH ( ΔhflC , K ) were sensitized to blasticidin S ( Fig 2B ) . These protease deletions have been demonstrated to be sensitive to aminoglycosides , both in E . coli [5 , 8] and in P . aeruginosa [21 , 22] . This set of pathways could be directly counteracting harmful effects of the translation inhibitors , with different pathways required to respond to unique mechanisms of action . As an example , deletion of the ribosome-bound ppGpp synthase ( ΔrelA ) resulted in sensitivity to both tRNA synthetase inhibitors used in the screen ( Fig 2B ) . RelA has a known role in sensing and responding to uncharged tRNAs in the A-site of the ribosome [23] . Sensitivities from the screen also indicated that many of the antibiotics were subject to drug efflux and degradation ( Fig 2A ) . Loss of AcrAB-TolC , the major efflux pump of E . coli [24] , sensitized cells to gliotoxin , clindamycin , and pseudomonic acid A ( Fig 2C ) . Other efflux pumps had more specific effects . The deletion strain ΔcrcB was sensitive to fluoride [25] while ΔmdtK was sensitive to serine hydroxamate [26] . The strain ΔyjjG was sensitive to 5-fluorouridine , consistent with the role of YjjG as a protective nucleotidase [27] ( Fig 2C ) . For ΔcrcB , ΔmdtK , and ΔyjjG these chemical sensitivities were the strongest for each strain across the integrated dataset . Additionally , gene deletions within two peptide ABC-importers were resistant to the antibiotics kasugamycin and blasticidin S . The ABC-importers of bacteria have been implicated in uptake of a diverse set of antibiotics , including blasticidin S , in a process termed illicit transport [28–32] . This phenotype was particularly interesting because of the agricultural importance of kasugamycin and because the uptake mechanism for these drugs has not been described in E . coli K-12 . We therefore tested whether Ksg and BcS were directly imported by these peptide ABC-importers . Hierarchical clustering of the fitness scores revealed a potential connection between the translation inhibitors kasugamycin ( Ksg ) , an inositol-based aminoglycoside , blasticidin S ( BcS ) , an aminonucleoside , ( Fig 3A ) and the major peptide ABC-importers of E . coli K-12; oligopeptide permease ( Opp ) and dipeptide permease ( Dpp ) [33–36] ( Fig 3B ) . Dpp import is specific for dipeptides [37] while Opp can import peptides less than 5 amino acids in length [38] . Cells harboring deletions of each component of the Dpp complex were resistant to Ksg and three clustered ( ΔdppC , ΔdppD , and ΔdppF ) with Ksg resistance as their major phenotype . Similarly , most deletions of Opp subunits were resistant to BcS and three clustered ( ΔoppB , ΔoppD , and ΔoppF ) . In addition , deletions of the negative regulators of Opp and Dpp expression ( ΔΔgcvB , Δhfq , and ΔgcvA ) led to hypersensitivity to both Ksg and BcS . We investigated these phenotypes further by constructing precise deletions of the ABC-importer operons ( Δopp and Δdpp ) and analyzing their phenotypes in MG1655 , the standard wild-type background , growing in minimal media at a neutral pH . This growth condition enhances drug efficacy [16 , 17] and is associated with increased expression of opp and dpp [39 , 40] . Spot dilution tests revealed that the operon deletion strains grew equivalently to MG1655 in minimal media ( Fig 3C , left ) , but were resistant to Ksg ( Fig 3C , middle ) and BcS ( Fig 3C , right ) . Individual deletion strains Δopp and Δdpp were resistant to Ksg , but the highest level of Ksg resistance was conferred by the Δopp Δdpp double mutant . This suggested that both complexes participate in Ksg import . For BcS , Δopp alone was sufficient to confer high-level resistance . Consistent with elevated expression of the importers leading to sensitivity to Ksg and BcS , expression of the entire Opp operon from a plasmid ( pOpp ) was sufficient to confer sensitivity to both drugs ( Fig 3D ) . Furthermore , the sensitivities of ΔgcvA and ΔgcvB to Ksg and BcS were completely suppressed in a Δopp Δdpp background ( S2 Fig ) confirming that these phenotypes were due to overexpression of Opp and Dpp . Both Ksg and BcS must transit through the inner membrane into the cytoplasm before they can bind to the ribosome and inhibit translation . The rate at which the drug enters the cytoplasm is likely the rate-limiting step for drug action . If so , an in vivo assay quantifying the rate translation is inhibited after drug treatment is a reasonable proxy for the rate of drug import . We used the amount of 35S-methionine ( 35S-Met ) incorporated in a 1 min pulse as a measure of translation rate , and then quantified its rate of decline after addition of antibiotic . We found that the rate of translation inhibition after addition of Ksg ( Fig 4A ) and BcS ( Fig 4B ) was dependent on the presence of the peptide ABC-importers , with the ABC-importer deletion strains showing a 7 to 10-fold slower rate of decrease in translation than MG1655 . In contrast , the rate of translation inhibition by kanamycin and spectinomycin was similar between MG1655 and the operon deletion strains ( S3 Fig ) . This indicated that , like the antibiotic sensitivities , the effect of deleting these importers on antibiotic uptake was specific to Ksg and Bls . Finally , although less striking in magnitude , the rate of translation inhibition by kasugamycin was significantly faster when the opp operon was overexpressed ( p<0 . 02 Paired Student’s t-test ) ( Fig 4C ) . These results provided evidence that the observed antibiotic sensitivities were due to altered drug uptake . To further test the hypothesis that Ksg was being directly imported by Opp and Dpp , we used an in vivo substrate competition assay ( Fig 5A ) to test whether the presence of high affinity substrates of Opp ( Pro-Phe-Lys; PFK ) and Dpp ( Ala-Ala; AA ) [41 , 42] competed with Ksg for uptake . Indeed , when Ksg was co-administered with these substrates , the rate of translation inhibition by Ksg was slowed dramatically , approximating that of the Δopp Δdpp double mutant strain . In contrast , the inhibition rate of a Δopp Δdpp strain was virtually insensitive to the addition of competitors . We next used an in vitro binding assay to test for a direct interaction between Ksg and OppA . We expressed and purified His-tagged OppA and tested for Ksg binding using intrinsic tryptophan fluorescence . Addition of 1 mM Ksg markedly increased the apparent KD of OppA for its high-affinity substrate PFK [43 , 44 , 42] ( Fig 5B ) but did not appreciably shift OppA fluorescence when added alone . A similar phenomenon has been shown for DppA , for which the relative change of fluorescence differs markedly even between natural high affinity peptide substrates [41] . While we cannot exclude the possibility that Ksg only indirectly alters OppA affinity for PFK in our in vitro assays , the increased apparent KD of OppA for PFK in the presence of Ksg is consistent with competitive binding between Ksg and PFK . The solute binding protein ( SBP ) of each peptide ABC-importer freely diffuses in the periplasm , binding its substrate and delivering it to the pore of the complex ( Fig 6A ) . In some cases , a single SBP has been shown to interact with the pores of multiple importers [45–47] . In particular , the SBP MppA has been shown to interact with both the Opp [46] and Dpp [47] pore in E . coli , depending on its substrate . Furthermore , DppA moonlights as the chemoreceptor for the peptide chemotaxis system of E . coli [37] . We refer to this phenomenon as crosstalk . To test whether crosstalk between importers or to other systems contributed to the import pathway for Ksg or BcS , we measured genetic interactions between components of the two complexes using the gold-standard assay for drug efficacy: MIC changes determined from a liquid 2-fold dilution series ( see Methods ) . We first validated that the MIC changes of the peptide importer mutants were consistent with observations from the spot test assay . The single mutant Δopp was sufficient to confer an increase in the MIC for blasticidin S by more than an order of magnitude . For kasugamycin , Δopp conferred a 2-fold MIC increase , and the double mutant Δopp Δdpp exhibiting a a 4-fold MIC ( Table 2 ) . Δdpp did not confer greater than a two-fold increase in MIC in this assay . Overall , liquid MIC measurements were consistent with the original observation that BcS was imported through Opp while Ksg was imported through both Opp and Dpp . We then tested for crosstalk during illicit transport . We asked whether deleting the SBP had the same quantitative effect on drug efficacy as deleting the pore . If crosstalk occurred , deleting a single SBP would provide less resistance than deleting the pore because all SBPs that deliver that substrate to the same pore must be removed to completely eliminate transport . For Opp , deleting the SBP ( ΔoppA ) , the pore ( ΔoppB ) , or the entire complex ( Δopp ) all resulted in the same extent of resistance to both Ksg and BcS ( Table 2 ) . This is consistent with OppA delivering Ksg and BcS to the Opp pore without crosstalk . Import of Ksg through Dpp also depended on DppA alone . Deleting both SBPs ( ΔoppA ΔdppA ) or both pores ( ΔoppB ΔdppB ) conferred the same degree of resistance to Ksg as deleting both operons ( Δopp Δdpp ) , excluding a contribution from any additional unidentified SBPs in Ksg import ( Table 2 ) . We suggest a straightforward model in E . coli K-12 in which Opp directly imports both Ksg and BcS , Dpp directly imports Ksg , and there is no crosstalk for either complex ( Fig 6A ) .
We report a chemical-genomic screen in E . coli K-12 focused on antibiotics with poorly characterized modes of action and determinants of resistance . We expect that this chemical-genomic dataset will function as a valuable community resource for generating new hypotheses based on these 26 stresses . Integrating the smaller chemical-genomic screen with a larger resource was critical for extracting more information from the new stresses , and this success sets a precedent for continuing to add to the dataset to characterize new antibiotics and find leads for investigating gene function . Characterizing the mechanisms of resistance to new antibiotics will be valuable for future development by identifying those compounds least likely to face already prevalent antibiotic resistance . The ability to integrate smaller-scale screens with larger resources , which we demonstrate here , will facilitate the economical application of chemical-genomics in drug discovery pipelines . Starting with one feature in this screen , we describe the illicit transport of blasticidin S ( BcS ) and kasugamycin ( Ksg ) through the peptide ABC-importers oligopeptide permease ( Opp ) and dipeptide permease ( Dpp ) . The peptide ABC-importer family is central to the uptake of multiple antibiotics in bacteria [28–32] . The flexible binding mechanisms used to accommodate the varied side chains of different peptides [49–51] could explain the susceptibility of these importers to being hijacked by so many illicit substrates . However , biophysical details of substrate binding by OppA and DppA are currently limited to natural substrates , so the nature of the interaction between these importers and their illicit substrates remains unclear . Purified OppA appears to have a weak affinity for Ksg , but Opp rapidly imports this compound in vivo nonetheless . Further characterization of the binding determinants of OppA and DppA for Ksg could be highly informative for defining the minimal requirements of binding and import by these promiscuous complexes . The shared uptake pathway for Ksg and BcS is likely to be conserved among many of the pathogens against which these antibiotics are regularly used [52–54] , although mechanistic details may differ from species to species . Both operon structure and sequence of the SBPs are highly conserved between E . coli and E . amylovora , but E . amylovora carries a duplication of oppA ( EAMY_RS26335 and EAMY_RS26330 ) ( Fig 6B ) which may complicate the uptake mechanism . In Pseudomonas aeruginosa , there are five dppA homologues ( dppA1-5 ) scattered across the genome and no clear homologue of the opp system . Despite this divergence , it was recently shown that both Dpp and a “third” peptide ABC-importer , Npp , contribute to BcS sensitivity ( Fig 6C ) . The exact SBPs that participate in BcS uptake in P . aeruginosa are currently unknown [31] . There are no sequence homologues of either oppA or dppA in Magnaporthe oryzae . However , strains isolated for resistance to either Ksg ( B1-100-4 kas-3 ) [48] or BcS ( Bu7 ) [55] can display cross-resistance . Given their distinct chemical structures , unique binding sites on the ribosome [56] , and demonstration of a shared uptake mechanism in E . coli , this cross-resistance is most likely due to a simultaneous loss of uptake of both compounds . Indeed , a functionally equivalent importer that remains to be identified could be responsible for uptake of both Ksg and BcS in this fungal pathogen ( Fig 6D ) . Cross-resistance from Ksg treated fields [57] suggests that loss of uptake is a common resistance mechanism in M . oryzae . However , naturally occurring Ksg-resistant ABC-importer mutants have yet to be isolated from any bacterial pathogens . Redundancy between import complexes may reduce the occurrence of this type of mechanism . The fitness impacts of deleting any of the opp or dpp genes are negligible within the integrated screen , but fitness effects have not been measured in the context of infection and these mutants may be quickly outcompeted in this environment . In addition , there are a number of alternative resistance mechanisms for Ksg including efflux [58] , chemical modification of the antibiotics [53] , and alteration of the binding site [52 , 59] . Further research in these organisms will be critical for predicting the contribution that loss of uptake will have on resistance in an agricultural or medical setting . The mechanism we describe is an example of one discovery from our chemical-genomic screen with broad implications for antibiotic resistance in different species and we expect many more discoveries to be made from this large-scale dataset .
Chemical sensitivity screens used LB Lennox agar plates ( 1% ( w/v ) tryptone , 0 . 5% ( w/v ) yeast extract , 90 mM sodium chloride , 2% ( w/v ) bacto agar ) unless otherwise specified . M9 minimal plates used in the screen contained M9 salts , 0 . 2% ( w/v ) glucose , and 2% ( w/v ) bacto agar . Media for kasugamycin and blasticidin S sensitivity was M9 minimal supplemented with metal cations and buffered at pH 7 . 5 ( M9 salts , 0 . 4% ( w/v ) glucose , 100 μM magnesium sulfate , 100 μM calcium chloride , 5 μM iron ( III ) chloride , 20 mM Tris-HCl , pH 7 . 5 ) . To promote high translation rates , media used for 35S-methionine incorporation was MOPS EZ rich ( -Met ) , 0 . 4% ( w/v ) glucose ( Teknova M2101 , M2102 , M2103 , M2109 , G0520 ) . Ordered libraries grown during the chemical-genomics screens were incubated at 37°C until the majority of colonies reached a defined size ( ~8 hours ) then a photograph was taken of the plate . Spot-dilution plates were grown roughly 24 hours at 37°C before a photograph was taken . For MIC determination , cultures were grown in deep 96-well plates for 24 hours in an Infors-HT shaker , 900 r . p . m . , 37°C . For 35S-methionine incorporation , cultures were grown at 37°C in baffled flasks in a gyrotory® water bath shaker at 350 r . p . m . Strains used in this study are listed ( S2 Table ) . The KEIO deletion library is derived from BW25113 ( F- λ- Δ ( araD-araB ) 657 ΔlacZ4787 ( ::rrnB-3 ) rph-1 Δ ( rhaD-rhaB ) 568 hsdR514 ) [60] . All experiments subsequent to the chemical-genomics screen were conducted using strains of MG1655 ( F- λ- ilvG- rfb-50 rph-1 ) [61] . Operon deletions were generated using λ-red recombineering to replace the operon with a kanamycin resistance cassette amplified from pKD4 . The resistance cassette was subsequently excised to generate deletions with a single FRT site as a scar . Plasmids and oligonucleotides used in the study are listed ( S2 Table ) . The chemical genomics screen was conducted using the same methodology as reported previously [8] with few modifications . Ordered libraries were arrayed on rectangular agar plates , grown in the presence of antibiotic or other stress until the colonies reached a defined average size , and then imaged . One condition , 4°C survival , involved growth of colonies on LB plates at 37°C for 6 hours , and transfer of the colony array to 4°C for 5 weeks . Colonies were then pinned onto a fresh plate and surviving cells were allowed to grow for 6 hours at 37°C before being imaged . Plate photographs were taken with a Canon Powershot G10 , using an in-house assembly to control plate illumination . Images were analyzed using Iris to measure the total intensity of pixels within the colony to calculate an opacity metric . Data filtering , normalization , and calculation of the fitness-score was conducted using an in-house analysis pipeline . Code for the analysis is available online ( https://github . com/AnthonyShiverMicrobes/fitness_score . git ) . Steps added to the original analysis pipeline [18] include simultaneous input and analysis of colony size , opacity , and circularity from Iris ( read_data . m ) , manual removal of data based on plate position ( to eliminate false positives from minor pinning problems ) ( filter_data . m ) , higher-order surface normalization ( incorporating a quartic smoothing function that better describes the systematic errors due to pinning effects for E . coli ) ( smooth_data . m ) , power-transformation of the data ( to reduce variability of extreme values ) ( transform_data . m ) , and variance normalization of the data ( to improve reproducibility of measurements between plates ) ( RC4_scale . m ) . The raw data and metadata files for this analysis are available online ( http://dx . doi . org/10 . 5061/dryad . f3kc0 ) . Unreliable measurements were removed from the dataset at multiple points in the analysis and each condition had a different number of measurements ( fitness-scores ) that passed analysis . Before data integration and clustering were performed , conditions and strains with less than 75% reliable measurements were removed from analysis . Hierarchical clustering was performed using the Cluster 3 . 0 [62] command line interface , using Pearson’s correlation and average-value linkage . Data was visualized using Java Treeview [63] . To predict reliable phenotypes and pairwise correlations , we used the method described by Nichols et al . [8] to determine the false-discovery rate ( FDR ) , defined as the fraction of positive test results that are expected to be due to type I error , and set a cut-off for the fitness-score that reflected an FDR of 5% . Using this method , 95% of the cutoff values for negative ( sensitization ) fitness-scores fell in the range ( -2 . 0 , -1 . 2 ) while 95% of the cutoff values for positive ( resistance ) fitness-scores fell in the range ( +1 . 2 , +2 . 1 ) . To identify gene sets enriched for cold-sensitivity at 10°C , we used Gene Set Enrichment Analysis ( GSEA ) [64 , 65] of the fitness-scores for 10°C . We grouped genes according to the functional categories of their COG assignments . Input files for this analysis are available online at ( http://dx . doi . org/10 . 5061/dryad . f3kc0 ) . A liquid-broth dilution method was used to determine MIC values for the antibiotics . Fresh colonies were picked , resuspended in minimal media , and diluted to a final O . D . 450 of 5x10-4 . Antibiotics were added in a 1:2 dilution series spanning a 64-fold dilution range . After 24 hours of growth , 150uL of culture was transferred to a 96-well spectrophotometer plate , and the O . D . 450 was measured using a Varioskan spectrophotometer ( Thermo electron corporation ) . After blank subtraction , culture densities were normalized to a no drug control , and the concentration of the first drug dilution step at which the normalized culture density fell below 10% was defined as the MIC of the drug . This quantitative measure corresponded well with a qualitative metric based on pelleting the cells and visually inspecting the size of cell pellet . Overnight cultures of relevant strains were grown inoculated into MOPS rich defined methionine dropout media ( MOPS RDM-Met ) at a starting O . D . 450 of 5x10-3 and grown to an O . D . 450 of 0 . 2 . To quantify translation rate , 900 μL of culture was added to 30 μL of labeling mix ( 10 μCi L-35S-methionine , 50 μM cold L-methionine , in MOPS RDM-Met ) ( Easytag L-35S-Methionine , Perkin Elmer Corp . ) , incubated for 1 min in the water bath , then quenched with 100 μL of 50% ( w/v ) trichloroacetate ( TCA ) and stored on ice . A 100 μL aliquot of the quenched reaction mixture was deposited on a glass fiber filter ( Merck Millipore Ltd . APFC02500 ) and washed with 10% ( w/v ) TCA followed by 95% ( v/v ) ethanol . 35S-methionine incorporation into the TCA-insoluble fraction was quantified on a scintillation counter ( Beckman Coulter LS6500 multipurpose scintillation counter ) . To follow translation inhibition , saturating concentrations of antibiotics were added to growing cultures . Translation rate was quantified in intervals following addition of drug and normalized to a timepoint taken two minutes before drug treatment . Data was fit to a double exponential decay function ( kasugamycin treatment ) or single exponential decay function ( blasticidin S , kanamycin , spectinomycin ) using QtiPlot [66] . The oppA gene from E . coli MG1655 was amplified and cloned into pBAD22 [67] using NcoI and HindIII restriction enzymes . E . coli BW25113 was then transformed with the resulting plasmid , and over-production was induced in an exponentially growing culture at O . D . 600 of 0 . 6 by addition of 0 . 05% ( w/v ) arabinose . The purification , including partial unfolding of the protein to remove bound substrates , as well as the binding assays followed the protocol in Klepsch et al . [42] . The changes made were clearing of the lysate for 1 hour at 140 , 000 g ( Beckman L8-M ultracentrifuge ) , replacement of guanidinium hydrochloride with urea for partial unfolding of OppA , and adjusting the 4-morpholineethanesulfonic acid ( MES ) buffer to a pH of 6 . 7 instead of 6 . 0 during purification to increase stability . OppA was highly concentrated and free of visible protein contaminations after Ni-IMAC as judged by SDS-PAGE and staining with Coomassie Blue , and was thus used in binding assays after extensive buffer exchange to remove the imidazole ( Milipore Amicon Ultra centrifugation filters , 10 , 000 MWCO ) . The spectral characteristics of purified OppA were identical to those reported previously . All Fluorescence assays ( Fluoromax-3 , Jobin Yvon Horiba ) were performed in 20 mM MES pH 6 . 0 and 150 mM sodium chloride , as described [42] . Data for the normalized change in intrinsic fluorescence was fit to a quadratic function that models ligand depletion [68] in QtiPlot [66] .
|
Bacterial species differ in their susceptibility to antibiotics but the reason for these differences remains an open question . Understanding the genetic basis of antibiotic susceptibility will be critical for predicting the efficacy of new antibiotics and possibly finding new antibiotic targets . Here we report a large-scale study that connects bacterial genes to antibiotics , using a set of antibiotics that were chosen to include poorly characterized compounds . We discovered genes that confer resistance to a number of neglected antibiotics , expanding our knowledge of gene function and antibiotic resistance in Escherichia coli K-12 . Starting from this large-scale screen , we then investigated how two antibiotics with a common history , kasugamycin and blasticidin S , enter bacterial cells . Both mimic naturally occurring nutrients to trick E . coli into actively bringing them inside . Kasugamycin is used to control microbes that cause agricultural diseases and mutations that reduce uptake like those we describe here may be an underappreciated factor in the development of resistance to kasugamycin .
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2016
|
A Chemical-Genomic Screen of Neglected Antibiotics Reveals Illicit Transport of Kasugamycin and Blasticidin S
|
We have developed an enhanced form of reduced representation bisulfite sequencing with extended genomic coverage , which resulted in greater capture of DNA methylation information of regions lying outside of traditional CpG islands . Applying this method to primary human bone marrow specimens from patients with Acute Myelogeneous Leukemia ( AML ) , we demonstrated that genetically distinct AML subtypes display diametrically opposed DNA methylation patterns . As compared to normal controls , we observed widespread hypermethylation in IDH mutant AMLs , preferentially targeting promoter regions and CpG islands neighboring the transcription start sites of genes . In contrast , AMLs harboring translocations affecting the MLL gene displayed extensive loss of methylation of an almost mutually exclusive set of CpGs , which instead affected introns and distal intergenic CpG islands and shores . When analyzed in conjunction with gene expression profiles , it became apparent that these specific patterns of DNA methylation result in differing roles in gene expression regulation . However , despite this subtype-specific DNA methylation patterning , a much smaller set of CpG sites are consistently affected in both AML subtypes . Most CpG sites in this common core of aberrantly methylated CpGs were hypermethylated in both AML subtypes . Therefore , aberrant DNA methylation patterns in AML do not occur in a stereotypical manner but rather are highly specific and associated with specific driving genetic lesions .
Acute myeloid leukemia ( AML ) is considered to be a genetically heterogeneous group of diseases , featuring functionally distinct somatic mutations and chromosomal translocations [1] . Many of these mutations involve aberrant transcriptional and epigenetic regulators , such as translocations involving chromosome 11q23 , which fuse the N-terminal portion of the Mixed Lineage Leukemia protein ( MLL ) to various fusion partners . MLL fusion proteins feature aberrant chromatin modifying functions and drive leukemogenesis through aberrant transcriptional activation of target genes such as HOXA9 [2]–[4] . More recently , AML associated heterozygous somatic mutations of isocitrate dehydrogenase 1 or 2 ( IDH1 or 2 ) were shown to result in a gain of function enzyme that uses alpha-ketoglutarate ( αKG ) as a substrate to generate the oncometabolite 2-hydroxyglutarate ( 2HG ) [5] . Accumulation of 2HG inhibits the function of αKG-dependent enzymes including the TET family of dioxygenases [6]–[8] . TET proteins contribute to DNA demethylation by hydroxylating 5-methylcytosine ( 5mC ) [9] . 2HG-induced suppression of TET proteins leads to accumulation of DNA methylation with effects on epigenetic gene regulation [10] . DNA methylation profiling of AMLs indicate that disruption of promoter cytosine methylation patterning is a universal feature of the disease . Promoter methylation signatures identify AML as composed of sixteen epigenetically defined subtypes [11] . One of these epigenetically defined AML subtypes feature 11q23 translocations and another features IDH1/2 somatic mutations . Indeed , abnormal promoter methylation has been noted in several other cancers . Recent more comprehensive DNA methylation sequencing studies indicate that cancers display perturbed cytosine methylation compared to normal tissues either on the basis of changes in CpG island methylation or alternatively at CpG shores , and have offered partially different visions of how DNA methylation is perturbed in tumor cells , in part influenced by technical differences in methods used to capture this information [12]–[14] . However , direct and quantitative genome scale studies of cytosine methylation perturbation in the context of tumors with specific genetic backgrounds have not been published for any cancer . Hence it is not known whether epigenetic patterning in cancer has a stereotypical pattern with a subtext of certain promoter specific aberrancies , or whether epigenetic patterning is mechanism and tumor subtype specific . One practical way to approach this question is through reduced representation bisulfite sequencing ( RRBS ) , an efficient method for quantitative , base-pair resolution of cytosine methylation across the genome [15] , [16] . However , this procedure has been shown to mainly represent CpG islands at the expense of other genomic regions [17] , [18] . In order to address the question of whether DNA methylation patterning is stereotyped or mechanism specific in tumors , we established an enhanced RRBS procedure ( ERRBS ) that provides biochemical and bioinformatic methodological improvements that generate more extensive and balanced coverage of CpGs . ERRBS analysis of normal hematopoietic stem cells in comparison with MLL rearranged ( MLLr ) or IDH1/2 mutant ( IDH-mut ) AMLs reveals that DNA methylation patterning is established in a profoundly distinct and mechanism specific manner in AMLs .
We sought to examine quantitative , base-pair resolution DNA methylation patterns in clinical specimens with limited cell numbers , with adequate coverage of CpGs both within and outside of CpG islands . To accomplish this , we developed a modified version of the RRBS protocol , which retains its quantitative base-pair resolution while improving the coverage of regions outside CpG islands . We first validated the performance of the original RRBS assay using genomic DNA extracted from the HCT116 colon cancer cell line . We observed that RRBS yielded robust and reproducible results over a wide range of starting material ranging from 5 ng to 1000 ng ( Figure S1A ) without any significant sequencing strand bias ( Figure S1B ) . We next modified RRBS into a format that would be practical to perform in limited clinical specimens . First , we eliminated an intermediate clean-up procedure between the two rounds of bisulfite treatment in order to minimize sample loss during library preparation . Instead of two rounds of bisulfite conversion as previously described [16] , [19] we used just one 16-hour round using the EZ DNA Methylation Kit ( Zymo Research , CA ) with slight modifications to the manufacturer's suggested protocol ( see methods section ) . This approach routinely achieves a conversion rate greater than 99 . 8% in both human and murine samples ( Table S1 ) . Conversion rates >99% with RRBS have been observed but not consistently achieved and rarely surpass 99 . 5% , even with repeated rounds of bisulfite conversion [16] . While RRBS has been shown to reliably detect gain of methylation , its ability to accurately detect genome-wide loss of methylation has not been extensively probed . Yet this is essential for clinical samples , since aberrant hypomethylation can be a dominant feature of tumor cells [7] , [11] , [12] , [20] . Furthermore , DNA methyltransferase ( DNMT ) inhibitor drugs currently used in the clinic are capable of inducing extensive hypomethylation [21] . In order to examine the dynamic range of the RRBS , we compared and contrasted the methylomes of HCT116 cells and the related cell line HCT116-DKO clone 2 ( DKO2 ) which lacks DNMT1 and DNMT3b [22] . DNA methylation in HCT116 showed the expected bimodal distribution , with the vast majority of CpG sites in the 0–10% and 90–100% methylation range ( Figure S1B ) . In contrast , the DKO2 cell line had a unimodal peak containing >83% of the reads with levels of methylation of 0–10% . Only 5 . 5% of DKO reads displayed >50% methylation . ( Figure S1C ) . Even under these extreme hypomethylated conditions the modified bisulfite treatment protocol continued to perform robustly ( conversion rate = 99 . 9% ) . We further validated the accuracy of the ERRBS assay with MassArray Epityping at 45 individual CpG sites , showing an extremely high degree of correlation ( r = 0 . 97 ) ( Figure S1D ) . An increasing body of evidence demonstrates that biologically relevant changes in DNA methylation in cancer occur beyond CpG islands [12] , [13] , [23] . Because RRBS only interrogates CpGs within short MspI delimited fragments between 40 to 220 bp , it is inherently biased towards representing CpG islands , which typically contain more densely clustered MspI sites [17] , [18] . In order to enhance the capture of regions beyond CpG islands , MspI fragments ranging from 70–320 bp were selected instead . This enhanced RRBS ( ERRBS ) method yielded a 75% increase in coverage of CpG sites with a 54% increase in coverage of CpG shores ( defined as 2000 bp flanks on upstream and downstream of CpG islands ) . We also observed a 58% increase in the number of introns captured vs . RRBS , a 54% increase in the number of exons and an 11 . 9% increase in the number of promoter regions ( Figure 1A and 1B ) . While the original RRBS alignment strategy used an MspI fragmented genome as a reference , whole-genome alignment strategies can also be applied to these data [18] . In a direct comparison of both strategies , we observed that a whole-genome alignment approach using the Bowtie aligner via the Bismark software [24] more than doubled the number of aligned reads which resulted in an increased recovery of the number of CpGs ( mean increase 200 , 154+/−135 , 012 ) ( Figure 1C ) . Eliminating the use of an MspI site as the absolute alignment requirement at the beginning of reads , as well as the use of a longer ( 32 bp ) seed length , further improved accuracy by excluding those reads that had the potential for more than one unique match or mismatch ( Figure 1D ) . Theoretically , no reads should map to regions of the genome that are not flanked by an MspI restriction sites , yet we found that on average 29% of the aligned reads mapped to non-MspI fragments . These fragments , which would be discarded when using in silico digested genomes for alignment , were likely produced due to either restriction enzyme non-specific activity , the presence of partially degraded DNA at the onset of the protocol , or variations in the patient genome compared to the reference genome . Collectively these approaches enhanced not only genomic coverage , but also alignment efficiency and accuracy . We previously reported that IDH-mut and MLLr AMLs distribute to different DNA methylation clusters and have distinct promoter DNA methylation signatures compared to normal CD34+ bone marrow controls ( NBM ) [10] , [11] . We performed ERRBS in two IDH-mut AML samples , two MLLr cases harboring t ( 9;11 ) ( q22 , q23 ) translocations , and two NBM samples . ERRBS covered an average of 2 , 082 , 426 CpGs per sample . In addition to the expected high correlation between the NBMs ( r = 0 . 96 ) there was a remarkable degree of correlation between the two IDH patients ( r = 0 . 93 ) and the two MLLr patients ( r = 0 . 92 ) ( Figure S2 ) , which far exceeded the correlation between MLLr and IDH-mut patients ( r = 0 . 85–0 . 88 ) , suggesting a strong link between genetic background and its effects on cytosine methylation . Unsupervised analyses using hierarchical clustering ( 1-Pearson correlation distance + Ward clustering method ) and principal component analysis revealed that , even with this greatly enhanced representation of the genome , ERRBS methylation profiles from IDH-mut and MLLr naturally segregate from each other just as strongly as from NBM ( Figure 2A and Figure S3A ) . In order to determine whether this natural segregation was driven solely by promoter differences in methylation , as captured in our previous studies , or whether biologically relevant differences were conserved in all genomic regions , we repeated the clustering analysis using only CpG sites within defined genomic regions . We found that using either ( 1 ) all non-promoter CpGs , ( 2 ) non-promoter intron CpGs , or ( 3 ) CpG sites at CpG islands and shores regardless of genomic location , the clustering results still retained the natural segregation into the biological groups ( Figure 2B and Figure S3B–S3E ) . Notably , when the clustering was performed on CpG island-associated CpG sites ( Figure S3D ) , IDH-mut AMLs segregated further apart from NBMs and MLLr AMLs , indicating that these sites are likely to be more heavily involved in the aberrant DNA methylation profiles of these AMLs . These findings demonstrate the existence of robust AML subtype-specific DNA methylation patterns , which extend beyond promoters to include other genomic regions . In order to identify the nature of the differences between IDH-mut and MLLr AMLs , the cytosine methylation profiles of these tumors were compared to normal CD34+ bone marrow cells from healthy donors ( NBM ) , using logistic regression ( FDR at alpha = 0 . 01 ) . In addition to statistical significance , we required a minimum cutoff of 25% methylation difference . This analysis revealed striking differences in the way that these two forms of AML differed from normal hematopoietic stem and progenitor cells . Specifically , we observed that IDH-mut AMLs display profound hypermethylation distributed across all chromosomes . In marked contrast , comparison of the cytosine methylation profiles of MLLr AMLs to NBM samples identified a predominance of aberrantly hypomethylated CpG site ( Figure 2C and 2D ) . More specifically , we identified 62 , 367 differentially methylated cytosines ( DMC ) between IDH-mut AMLs and NBM , 89 . 6% of which were aberrantly hypermethylated in the leukemias and only 10 . 4% hypomethylated . Among the 85 , 216 DMCs identified in MLLr AMLs we observed a vastly different and opposing distribution ( Chi-square test , p-value<0 . 0001 ) , with only 28 . 5% of DMCs displaying hypermethylation and 71 . 5% being hypomethylated . The above results remained valid even when we used a more stringent cutoff of 40% methylation difference or a more relaxed cutoff of 10% ( Figure S4A and S4B ) . These results demonstrate that the directionality of DNA methylation changes acquired during malignant transformation of myeloid hematopoietic cells is not uniform across all AML subtypes and that DNA methylation changes are indeed diametrically opposed in these two AML subtypes . Previous studies in AML were restricted to promoter microarrays [11] , [25] or locus specific assays [25] that do not provide wide-spread and unbiased base pair resolution . Thus , it is not yet fully understood how aberrant DNA methylation is distributed in AML beyond these limited regions . Moreover , it is not clear whether results from studies carried out on certain solid tumor specimens [12] , [13] are generally applicable to cancer , nor whether genetic background of tumors , and more specifically AML , can have an influence on what regions are perturbed . The base pair resolution and extended genomic coverage of ERRBS make it well suited to address these questions . To compare methylation status across all samples , we first identified a total of 574 , 178 CpGs adequately represented by ERRBS ( >10× coverage; on average 53× coverage per base ) in all specimens . Of these , 94 , 245 CpGs were differentially methylated ( methylation difference >25% ) in either one or both subtypes . Notably , 87 . 3% ( n = 82 , 312 ) of these DMCs were non-overlapping and thus unique to either IDH-mut or MLLr leukemias ( Figure 3 ) . More specifically , 51 , 586 DMCs were identified in IDH-mut AMLs , of which the majority of CpGs , or 76 . 8% , were unique and non-overlapping with MLLr . In the case of MLLr AMLs , there were 54 , 592 DMCs , 78% of which were unique and non-overlapping with IDH-mut cases . Even more strikingly , 93% of the IDH-mut specific DMCs were hypermethylated vs . NBM , whereas 80 . 8% of MLLr specific DMCs were aberrantly hypomethylated . Comparable results were observed even when either a more stringent 40% or a less stringent 10% cutoff was used for calling DMCs ( Figure S4 ) . Pathway enrichment analysis of the DMCs observed in each subtype was performed using GREAT [26] . Only pathways with an FDR q-value<0 . 05 in both the hypergeometric and binomial tests were included . This analysis revealed that IDH-mut DMCs were enriched in several pathways , including cadherin , Notch and TGFb signaling ( Table S3A ) . MLLr DMCs on the other hand featured enrichment of two pathways , one involving integrin signaling while the other included transcriptional activators EP300 , CREBBP , FOS , JUN as well as several genes involved in regulation of apoptosis such as BAX , CASP3 , CASP6 and TP73 ( Table S3B ) . Hence the DNA methylation defect of these two AML subtypes is not only perturbed in opposite directions but is also based on the differential methylation of an almost completely distinct set of CpGs , which affect distinct pathways . Since our alignment strategy spanned the entire genome and used exact matches , we were able to determine whether DMCs were preferentially associated with certain repetitive sequences in the genome . Overall , we found that only 15% of CpG sites covered by ERRBS in all samples overlapped with repeat regions . However , we found that hypomethylated DMCs were enriched for repeat elements , with 24% overlap in MLLr ( Odds-Ratio: 1 . 8 , p-value 2 . 2e-16 ) and 26% in IDH-mut ( Odds-Ratio: 2 . 0 , p-value 2 . 2e-16 ) , and most of those DMCs were found at Alu elements ( 8% in IDH-mut and 10% in MLLr ) . Hypermethylated DMCs , on the other hand were depleted for repeat elements , with only 7 and 8% of hypermethylated DMCs overlapping with repeats in IDH-mut and MLLr , respectively ( Odds-Ratio for both 0 . 4 , p-value 2 . 2e-16 ) . These were , mostly low complexity and simple repeats ( Figure S5 ) . Next we examined the common differentially methylated CpG sites in IDH-mut and MLLr AML ( n = 11 , 933 ) . Of these , 76 . 6% ( n = 9148 ) were coordinately differentially methylated in the same direction in both AML subtypes , and the majority of these DMCs were aberrantly hypermethylated vs . NBMs ( 79% , n = 7223 ) . These concordantly hypermethylated DMCs were more frequently associated with polycomb repressive marks than concordantly hypomethylated DMCs ( 66 . 2% vs . 46 . 2% , p-value<2 . 2e-16 , Fisher's exact test ) . Concordantly hypermethylated CpGs were associated with genes involved in Cadherin , Wnt and Notch signaling pathways , many of which have been previously reported as frequently methylated in a variety of neoplasms , such as APC2 [27] , [28] , SFRP2 [29] , CDH13 [30] , [31] , CDH15 [32] and PCDH10 [33] , [34] ( see Table S4 ) . However , concordantly hypomethylated CpGs were not associated with any pathway but were instead significantly associated with repeat elements: 27 . 7% of concordantly hypomethylated CpGs overlapped with repeats , but only 7 . 4% of concordantly hypermethylated CpGs overlapped with a repeat ( Fisher's exact test p-value<2 . 2e-16 ) . This degree of overlap is similar to what we observed in the more global analysis of repeat elements mentioned above , indicating that concordantly hypomethylated DMCs are not enriched for repeat elements compared to subtype-specific DMCs . Hence , although the majority of DMCs in these two AML subtypes affect different CpGs in opposite directions there remains a core set of commonly affected CpGs , which are mostly concordantly hypermethylated regardless of genetic background . These results are consistent with an observation based on HELP assays indicating the frequent hypermethylation of a core set of 45 genes in AML [11] . Despite the differences in coverage between ERRBS and the microarray platform used in our previous studies , we found that 15/18 genes covered by both assays again presented with aberrant CpG hypermethylation in both AML subtypes in this current study ( Table S5 ) . Altogether , the data suggest two layers of epigenetic programming in AML , the first represented by perturbations specific to tumor subtype , and the second encompassing defects representative of the leukemic phenotype in general . Different types of analyses and platforms used in previous studies have tended to favor either aberrant methylation of CpG islands [14] , [35] or CpG shores [12] , [13] as the dominant defect in tumors . However it is not clear whether these observed differences are dependent on tumor type and/or methodology utilized in the different studies . The use of the ERRBS platform allowed us to explore differential methylation of both of these regions simultaneously . In order to understand which genomic regions present the highest variation in leukemias compared to NBM cells , we calculated differential methylation levels for individual CpG sites annotated to both CpG islands and shores . Our data revealed that CpG shores represented the regions with the highest variability in methylation in the MLLr AMLs ( Wilcoxon rank sum test p-value 3 . 190e-11 ) ( Figure 4B ) . In contrast , IDH-mut AMLs had higher variability in DNA methylation at CpG islands than CpG shores ( Wilcoxon rank sum test P- value<2 . 2e-16 ) . We also observed significant differences in the absolute numbers of DMCs distributed to CpG islands and shores between the two subtypes ( Chi-square test , p-value<0 . 0001 ) . Specifically , we found that DMCs more frequently mapped to CpG islands in the IDH-mut AMLs cases ( 50% in IDH-mut vs . 29% in MLLr ) . In contrast , 50% of DMCs in the MLLr AMLs were found neither at CpG islands nor CpG shores but were instead annotated to regions even beyond CpG shores ( Figure 4C ) . These findings indicate that distribution of DNA methylation changes during malignant transformation do not follow a uniform rule across all tumor types and genetic backgrounds , but rather that specific changes within and beyond genes are observed with distinct malignancy driving mechanisms . When considering the relation of DMCs to RefSeq annotated genes we observed that approximately 40% of all DMCs in both AML subtypes were found within gene bodies . However , more detailed analysis identified markedly dissimilar regional distribution of DMCs between the IDH-mut and MLLr AMLs . First , MLLr AMLs displayed significantly more abundant DMCs at introns than IDH-mut AMLs ( 31 vs . 25% ) and intergenic regions ( 39 vs . 35% ) . In contrast , promoter-associated DMCs were twice as frequent in IDH-mut AMLs ( 27 vs . 16% ) ( Figure 5A ) ( Chi-square test , p-value<2 . 2e-16 ) . A similar trend exists if we look at the percentages of introns , exons and promoters overlapping with a DMC in MLLr and IDH-mut . In IDH-mut , promoters more frequently overlap with DMCs whereas in MLLr , introns more frequently overlap with DMCs . This result demonstrates preferential localization of DMCs in different samples , where variability of methylation shifts its focus to different genomic features ( Figure 5B ) . Moreover , the median upstream distance from the transcription start site ( TSS ) to observed DMCs was significantly greater in MLLr AMLs than in IDH-mut AMLs ( 11 , 013 bp vs . 5 , 737 bp , Wilcoxon rank sum test p-value<2 . 2e-16 , Figure 5C ) . These analyses reveal yet another layer of difference between the two AML subtypes , with IDH-mut AMLs mainly affecting DNA methylation of CpG island promoter regions surrounding the TSS whereas MLLr AMLs mainly disrupt upstream and downstream regions , mostly independent of CpG islands . When considering promoters according to CpG frequency ( as defined by Weber et al according to CpG ratio , GC content and length of CpG-rich region [36] ) , we found that more of the high CpG promoters ( HCPs ) overlap with DMCs in both IDH-mut and MLLr compared to low CpG promoters ( LCPs ) ( 17 . 1% vs 7 . 9% in IDH-mut: p-value<2 . 2e-16 and , 11 . 1% vs 4 . 9% in MLLr: p-value = 9 . 4e-12 ) . However , it was intermediate CpG promoters ( ICPs ) that were the most enriched with DMCs in both leukemia subtypes: 73% of ICPs in IDH-mut and 71% of ICPs in MLLr with covered CpGs overlapped with DMCs . We then examined these regional differences in cytosine methylation relative to known regulatory elements . We compared the DMC sites from both IDH-mut and MLLr AMLs to available ENCODE data sets [37] for CTCF binding and H3K4me1 and H3K4me3 data to define enhancer sites ( defined as sites positive for H3K4me1 and devoid of H3K4me3 ) [38] . We found that CTCF binding sites and enhancers were more frequently found in the vicinity of MLLr DMCs ( +/−500 bp ) than of IDH-mut DMCs ( Fisher's exact test p-value: <0 . 001 for both CTCF and enhancer sites ) . Enhancers and CTCF binding sites were more frequently hypomethylated in MLLr AMLs ( Fisher's exact test p-value<0 . 001 ) , while in IDH-mut AMLs these sites were more frequently hypermethylated ( Fisher's exact test p-value<0 . 001 ) ( Table 1 ) . Whereas the mechanism through which IDH mutations affect particular genes is unknown , MLL fusion proteins are known to directly upregulate specific target genes , such as HOXA9 , which are essential for the transformation process [2] . To investigate this , we surveyed the genomic localization of MLL fusions , MEIS1 or HOXA9 by ChIP-seq and examined whether aberrant DNA methylation was associated with binding of these factors . We found that MLL bound more frequently at promoters , and that only 6 . 5% of the 833 MLL peaks covered by the ERRBS assay occurred within 500 bp of MLLr DMCs . While only 49 out of 614 of the HOXA9/MEIS1 peaks [39] were covered by the ERRBS assay , 24 . 4% of them were associated with DMCs in MLLr AMLs , the majority of which were hypomethylated ( 22 . 4% vs 2% , Fisher's exact test p-value<0 . 004 ) , suggesting that aberrant hypomethylation in MLLr AMLs is more closely linked to the HOXA9 and its co-factor MEIS1 than to the MLL fusion protein itself . In order to determine the potential functional significance of the distinct DNA methylation patterning observed in IDH-mut and MLLr AMLs we examined gene expression microarray profiles from the same AML cases [10] , [40] . We assigned CpG sites into 3 types of regions: CpG islands overlapping a TSS , intergenic CpG islands upstream of the TSS ( up to −5 kb ) and intragenic CpG islands downstream of the TSS ( up to +5 kb ) . Both in normal CD34+ samples and leukemia specimens , hypomethylation within CpG islands overlapping TSSs was associated with highly expressed genes , while hypermethylation was observed for low expression genes ( top and bottom 15th percentile of expressed transcripts , Wilcoxon rank sum test p-value<0 . 005 ) ( Figure 6 and Figure S6 ) . However , the relationship between CpG shore methylation status and gene expression levels was different in all three sample types . Hypermethylation of CpG shores was associated with low levels of gene expression only in MLLr AMLs , both at CpG shores overlapping the TSS as well as at downstream intragenic and upstream intergenic CpG shores . In marked contrast , CpG shore methylation levels in IDH-mut AMLs behaved in the opposite way , so that lower levels of methylation were in fact associated with lower levels of expression ( Wilcoxon rank sum test p-value<0 . 005 ) , while in normal CD34+ cases gene expression levels did not appear to depend on CpG shore methylation status at all . Furthermore , when examining DMCs and their correlation with differential gene expression between the different AMLs and the normal bone marrow specimens , we found that only DMCs at the core promoter regions were significantly inversely correlated with differential gene expression in IDH-mut AMLs ( p-value = 0 . 0047 ) . However , for MLLr AMLs , we observed that while core promoter DMCs were also significantly associated with differential expression ( p-value = 3 . 1e-16 ) , this association was also significant at upstream DMCs ( up to 10 kb ) , both for DMCs located at CpG islands ( p-value = 3 . 2e-11 ) and CpG shores ( p-value = 1 . 3e-23 ) . Finally , downstream intronic DMCs overlapping with CpG islands also showed a significant correlation with expression in MLLr AMLs ( p-value = 0 . 046 ) . Collectively , these findings suggest that subtype specific DNA methylation distribution in AMLs regulates gene expression in a subtype-defined manner . More precisely our data indicate a significant role for long-range epigenetic regulation in MLLr AML through distal intergenic and intronic CpG islands , whereas IDH-mut AMLs display a predominance of promoter-centric epigenetic regulatory effects .
The study of gene promoters and CpG islands under the assumption that variation in the 5-methylcytosine status at these locations would have functional importance has been the focus of most cancer-related DNA methylation studies . Building upon the previously described RRBS method , the ERRBS assay described here made it possible to measure DNA methylation in primary AML samples beyond promoter regions , extending even into distal intergenic regions . This significantly enhanced genomic coverage allowed us to demonstrate that heterogeneity in epigenomic profiles in AML is not only a factor of different genes being affected , but rather encompasses a far more complex scenario , which includes the aberrant DNA methylation of distinct regions of the genome as well as differential mechanisms of regulation of gene expression according to genetic background . Given the specificity and reproducibility of these aberrant DNA methylation patterns , it is likely that their establishment in malignant cells is directly linked to genetic driver lesions . Our previous studies using HELP promoter microarrays are consistent with these results in that they revealed a hypermethylated promoter signature in IDH-mut AMLs , and a hypomethylated signature in MLLr . However those studies did not have the resolution or depth to reveal the true genomic nature , complexity and qualitative differences that we are now able to report regarding the nature of cytosine methylation distribution in these AML patients . Specifically , in the case of MLLr leukemias , aberrant DNA methylation consists mostly of aberrant hypomethylation of upstream and intronic CpGs including CpG islands and shores , but extending to and heavily involving even more distal regions . Hypomethylation of regulatory elements is consistent with the actions of MLL fusion proteins as transcriptional activators . However , in these tumors the distal localization of hypomethylation was more closely associated with the presence of HOXA9 and MEIS1 binding sites and enhancer regions than with MLL binding sites , suggesting that aberrant DNA hypomethylation in these tumors may be more closely related to effects of downstream targets of MLL than to the fusion protein itself . However , it should be noted that a subset of MLL peaks ( 6 . 5% ) did indeed overlap with DMCs in the MLLr AMLs . Since our ChIP-seq antibody recognized both the wild-type and the rearranged copy of MLL . Given that MLL fusions have been shown to bind to a subset of wt-MLL target genes [41] , it still remains possible that the subset of overlapping peaks may be preferentially bound by the MLL fusion . Further studies with antibodies capable of distinguishing between the two forms of MLL will be required to properly address the role of MLL fusions in helping establish the aberrant methylation profile seen in these leukemias . The functional relevance of hypomethylation in MLLs is supported by the enrichment for highly transcribed genes at loci where this distal methylation pattern is observed . IDH mutant AMLs on the other hand , display a diametrically opposed pattern of aberrant methylation of CpGs , which results in the prefential hypermethylation of CpG islands surrounding TSSs , involving an almost entirely mutually exclusive set of CpGs but also resulting in the downregulation of genes with increased methylation . While it is clear that the generation of the 2-HG metabolite by the mutant forms of IDH1 and IDH2 results in inhibition of the hydroxylation reaction by TET proteins [6] , it is as yet unclear why this inhibition results in a promoter-specific hypermethylation pattern , and inhibition of other epigenetic modifiers such as Jumonji C domain histone demethylases by 2-HG [42] may also play a role in determining the aberrant epigenetic profiles of these AMLs . Moreover , it is possible that hydroxymethylation of DNA by TET proteins may depend on other DNA binding partners that direct them to specific genomic sites . Even though the two AML subtypes were dramatically different , they still shared a core hypermethylated signature including genes previously shown to be almost universally hypermethylated in AMLs [11] . Similar to what had been previously demonstrated in colon cancer murine models [43] , [44] , Broske and colleagues demonstrated that DNA methylation is required to fully transform hematopoietic stem and progenitors , even with a potent oncogene such as MLL-AF9 [45] . Taken together , these observations point towards the existence of a core of hypermethylation lesions that are a necessary event during malignant transformation , and that likely cooperate with the underlying genetic events in the different AML subtypes . Most importantly , abnormal DNA methylation patterning does not occur in a stereotypical manner in cancer , but instead adopts distinct and specific distributions dependent at least in part on genetic background , even when comparing cases of the same tumor type with different driver mutations . Our analysis comparing gene expression and DNA methylation at base-pair resolution across three different sample types demonstrates that epigenetic regulation of gene expression in tumors may at least in part be context dependent , suggesting that cell-type specific factors may come into play to establish and maintain unique regulatory mechanisms in these cells . Finally , the large distances between DMCs and transcription start sites support a potential role for epigenome regulation at distal regulatory elements , via looping or other mechanisms , in directly influencing the specificity of the transcriptional machinery . Taken together our data support the existence of divergent roles of the epigenome in regulating the transcriptional profiles of AML and indicate that altered gene expression is associated with the differential methylation of distinct and non-overlapping CpGs and regions in tumors with different genetic backgrounds . Moreover , in the case of MLLr AMLs , these abnormal regulatory mechanisms extend far beyond the classically described cancer-associated promoter CpG island hypermethylation , and indicate that distal intergenic DNA methylation abnormalities may also have functional consequences in certain tumors . These findings are consistent with those described by other groups which have seen an association between differentially methylated regions at CpG shores in solid tumors and changes in gene expression [13] . Indeed , these significant regional and CpG specific differences would be unlikely to be captured with any other method except whole genome bisulfite sequencing or methods like ERRBS with unbiased and adequate base-pair resolution detection of CpG methylation . Of note , the gene expression microarrays used in the current study only capture known coding transcripts . Yet the expanded coverage of ERRBS can also provide information on putative regulatory elements of non-coding RNAs as well as information on regulation of alternative promoters . It will be important for future studies perhaps using RNA-seq , to analyze the relationship between aberrant DNA methylation and the expression levels of non-coding RNAs or , the correlation between DNA methylation status at alternative promoters and the expression levels of transcript variants , a regulatory role previously described for DNA methylation [46] . High resolution comparative studies of genetically characterized primary human tumors using methods that adequately represent the genome at base pair resolution ( such as RNA-Seq ) may thus yield a wealth of new information on mechanisms driving tumor transcriptional and epigenetic programming and the true scope and nature of aberrant DNA methylation patterning in cancer . Studies integrating more comprehensive transcriptome data with transcription factor binding and histone modification patterns in concert with assays designed to explore chromosomal structure will yield further insight into such mechanisms .
The human colorectal carcinoma cell line HCT116 was a kind gift from Dr . John Mariadason . The cell line was maintained in DMEM supplemented with 10% fetal bovine serum ( FBS ) , 100 units/ml of penicillin and 100 µg/ml of streptomycin ( Invitrogen ) at 37°C and 5% CO2 . The HCT116 DNMT1 ( −/− ) DNMT3b ( −/− ) double knockout clone number 2 ( DKO ) cell line was a kind gift from Dr . Steve Baylin . The cell line was grown in McCoys'5A medium with 10% FBS , 0 . 2 mg/ml Neomycin ( G418 ) , and 0 . 1 mg/ml Hygromycin B . Genomic DNA was extracted from the cell lines using standard phenol:chloroform extraction followed by ethanol precipitation . AML samples were obtained from previously reported , de-identified patient samples , from individuals enrolled in the Eastern Cooperative Oncology Group's ( ECOG ) E1900 clinical trial [47] and from patients seen at Erasmus University MC , The Netherlands . Two IDH1/2 mutant AML samples ( IDH1 and IDH2 ) , two mixed lineage leukemia gene rearranged AML samples harboring t ( 9;11 ) ( MLL1 and MLL2 ) and one additional AML sample ( AML ) were available for processing . Two normal CD34+ bone marrow control samples were purchased from AllCells , LLC ( Emeryville , CA , USA ) . Institutional review board approval was obtained at Weill Cornell Medical Center and this study was performed in accordance with the Helsinki protocols . DNA was isolated from each primary sample using the Qiagen Puregene kit per manufacturer's recommendation . RRBS was performed as follows: i ) 5 , 50 or 1000 ng of high quality genomic DNA were digested with 200 U of MspI ( New England Biolabs , NEB ) which cuts DNA regardless of cytosine methylation status at CCGG sequence in a 100 µl reaction for up to 16 hours at 37°C . DNA was isolated using standard phenol chloroform extraction and ethanol precipitation and resuspended into 30 µl of 10 mM TrIs pH 8 . 0 . ii ) End repair of digested DNA was performed in a 100 µl reaction using 15 U of T4 DNA polymerase ( NEB M0203L ) , 5 U of Klenow DNA polymerase ( NEB M0210L ) , 50 U of T4 Polynucleotide Kinase ( NEB M0201L ) , 4 µl of premixed nucleotide triphosphates each at 10 mM ( NEB N0447L ) using T4 DNA ligase buffer with 10 mM dATP ( NEB B0202S ) . The reaction was incubated at 20°C for 30 minutes and products were isolated using QIAquick PCR purification columns per manufacturer's recommended protocol ( Qiagen ) into 32 µl of EB buffer . iii ) Adenylation was performed in a 50 µl reaction using 15 U Klenow fragment ( 3′ to 5′ exo minus , NEB M0212L ) , 10 µl of dATP at 1 mM concentration using Klenow buffer ( NEB ) . The reaction was incubated at 37°C for 30 minutes and products were isolated using MinElute PCR purification columns per manufacturer's recommended protocol ( Qiagen ) into 10 µl EB buffer . iv ) Adenylated DNA fragments were ligated with pre-annealed 5-methylcytosine-containing Illumina adapters in a 20 or 50 µl reaction for 5 ng or 50 ng or higher starting materials respectively using 2000 U T4 DNA ligase ( NEB M0202T ) and 1 . 2 µM final concentration of methylated adapters at 16°C for a minimum of 16 hours . Products were isolated using MinElute columns per manufacturer's recommended protocol ( Qiagen ) into 10 µl EB buffer . v ) Library fragments of 150–175 and 175–225 bp were gel isolated from a 1 . 5% agarose gel ( using low range ultra agarose from Biorad ) using the Qiaquick Gel Extraction kit per manufacturer's recommended protocol ( Qiagen ) into 40 µl EB buffer . vi ) bisulfite treatment was performed using the EZ DNA Methylation Kit ( Zymo Research ) per manufacturer's recommended protocol with the following modifications: 1 ) incubation after the addition of CT conversion reagent was conducted in a thermocycler ( Mastercycler ep gradient , Eppendorf ) with the following conditions: 30 seconds at 95°C followed by 15 minutes at 50°C for 55 cycles and , 2 ) products were eluted into 40 µl nuclease free water . vii ) PCR amplification for each library was performed in a 200 µl reaction containing 2 µl FastStart Hifidelity DNA Polymerase ( Roche ) , 0 . 5 µM each of Illumina PCR primers PE1 . 0 and 2 . 0 , 0 . 25 mM each nucleotide triphosphate using buffer 2 per manufacturer's recommendation and divided into four 50 µl reactions . The thermocycler conditions were: 5 minutes at 94°C , 18 cycles of 20 seconds at 94°C , 30 seconds at 65°C , 1 minutes at 72 , followed by 3 minutes at 72°C . PCR products were isolated using AMPure XP beads per manufacturer's recommended protocol ( Agencort ) into 50 µl of EB buffer . viii ) All amplified libraries underwent quality control steps including using Qubit 1 . 0 fluorometer and a Quant-iT dsDNA HS Assay Kit for quantitation ( Invitrogen ) and bioanalyzer visualization ( Agilent 2100 Bioanalyzer ) . Extended Reduced Representation Bisulfite Sequencing ( ERRBS ) was performed as described above for RRBS , except that in step number v , library fragment lengths of 150–250 bp and 250–400 bp were gel isolated . The amplified libraries were sequenced on an Illumina Genome Analyzer II or HiSeq2000 per manufacturer's recommended protocol for 50 bp single end read runs . Image capture , analysis and base calling was performed using Illumina's CASAVA 1 . 7 . Validation of select CpG methylation in HCT116 cell line was implemented by MALDI-TOF mass spectrometry using EpiTYPER by MassARRAY ( Sequenom , San Diego , CA ) as previously described [48] . Primers were designed to cover CpGs in various chromosomal locations with various methylation levels and sequencing coverage . Primers and amplicon sequences are listed in Table S2 . Pathway enrichment analysis was performed using the GREAT software [51] , which associates genomic regions with nearby genes and calculates enrichment statistics using annotations of those genes . In order to associate genomic regions to genes , each gene is assigned to a regulatory domain , which consists of a basal promoter and extension around that promoter to cover distal elements . Following that , the genomic regions falling on those regulatory domains are associated with the genes . Following parameters are used for definition of regulatory domain: 5000 bp upstream , 1000 bp downstream of TSS as basal regulatory domain and this is extended up to 100 kb maximum . GREAT calculates two enrichment statistics using the binomial test and the hypergeometric test . Only the pathways significant by both tests are shown ( FDR q-value<0 . 05 ) . Gene expression for IDH mutants and normal bone marrow cells are downloaded from the Gene Expression Omnibus ( GEO ) ( accession: GSE24505 ) . Normal bone marrow samples are not matched to the samples on this array however we averaged 5 normal bone marrow samples on the array to interpolate the expression profiles of our normal bone marrow samples . The sample matched gene expression profiles for cells with MLL translocation are downloaded from GEO ( accession: GSE6891 ) . Expression percentiles of each transcript are also calculated using R function “ecdf” . The transcripts for each sample are divided into two categories high expressed ( the top 15% ) and low expressed ( the bottom 15% ) . CpG islands are mapped to annotated transcripts for probes as follows . First , we mapped CpG islands to 10 kb window around the TSS of the annotated transcript , and CpG islands in this window are classified as TSS overlapping , upstream and downstream CpG islands depending on whether or not they overlap with TSS and relative location if they are not overlapping with TSS . Following that , we compared maximum methylation per island and maximum methylation per shore for high and low expressed genes on each sample . We used Wilcoxon's Rank sum test to compare maximum methylation distributions on each shore and CpG island for high and low expressed genes . For this comparison we only considered CpG islands and shores that have at least three genomic CpGs covered by bisulfite reads . When correlating DMCs with the differential expression , we first calculated fold-change of MLLr vs . NBM and IDH-mut vs . NBM samples . Expression data for NBM samples ( although not sample matched ) were available for both IDH-mut and MLLr fold-change calculations within the respective microarray types and downloaded from GEO ( accession numbers GSE24505 and GSE6891 respectively ) . We calculated fold-change between the average expression values of the groups . Following that we measured correlation between percent methylation difference at DMCs and fold-change of the nearest gene ( obtained by extracting the nearest TSS ) using “correlation . test” in R . We performed separate correlation analyses for DMCs at the core promoter ( −300 bp , +300 bp around TSS ) , upstream from the TSS ( up to 10 kb ) , within CpG islands ( up to 5 kb from TSS ) , within CpG island shores ( up to 5 kb from TSS ) , within intronic regions , at intronic CpGs , and at CpGs within intronic CpG islands and shores . MLL ChIP-seq experiments were performed in the MLL-AF4 cell line RS4;11 ( ATCC#CRL-1873 ) using antibodies to MLL1 ( Bethyl Laboratories A300-086A ) . ChIP-seq libraries were prepared from 10 ng of immunoprecipitated material using Illumina's ChIP-seq kit as per manufacturer's instructions , and then sequenced on a Genome Analyzer IIx sequencer . Alignment against the human genome , peak calling and downstream analysis was performed using ChIP-seeqer [52] . HoxA9 and Meis1 ChIP-seq peaks from murine cells from Huang et al [39] were annotated to the human genome using the LiftOver function from the UCSC browser [49] . The ENCODE CTCF , H3K27me3 , H3K4me1 and H3K4me3 peak locations are downloaded using UCSC table browser [53] . ChIP-seq experiments and peak finding were carried out by The Broad Institute for 9 different cell lines only 8 of which had H3K4me1 and H3K27me3 marks available for download [54] . Polycomb repressive marks were identified as those with K3K27me3 by Ernst et al using a hidden-markov model based approach [54] . For enhancer markers , we picked H3K4me1 sites that do not overlap with H3K4me3 in a given a given cell line as previously shown [38] . We merged all such H3K4me1 sites from 8 cell lines , so that if H3K4me1 sites overlap in different cell lines they will not be counted twice . The same merging procedure is applied for CTCF binding sites and H3K27me3 from 8 cell lines . Following that , we extended the peak locations for CTCF , enhancer markers , MLL , Meis1 and HoxA9 by 500 bp on each side of the peak location . We overlapped resulting regions with DMCs in IDH-mut and MLLr . We also overlapped those regions with CpGs covered by reads to see how many of those binding sites are covered by ERRBS . We applied Fisher's exact test to compare proportions of DMCs .
|
Acute myeloid leukemias ( AML ) are a group of malignancies that originate in the bone marrow . While many different genetic lesions have been linked to the different forms of this disease , it is also clear that these genetic lesions are not always sufficient to cause AML . DNA methylation plays a role in gene expression regulation , and abnormal distribution of DNA methylation has been observed in many cancers , including AML . Here we demonstrate that changes in DNA methylation in AML are not uniform across all AML subtypes , but rather they display unique patterns , which are closely linked to the underlying genetic lesions of each of the different forms of AML . Furthermore , these unique patterns of DNA methylation have different impacts on gene expression regulation in each AML subtype .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Methods"
] |
[
"hematologic",
"cancers",
"and",
"related",
"disorders",
"medicine",
"genome",
"expression",
"analysis",
"leukemias",
"genomics",
"biology",
"computational",
"biology",
"epigenomics",
"hematology"
] |
2012
|
Base-Pair Resolution DNA Methylation Sequencing Reveals Profoundly Divergent Epigenetic Landscapes in Acute Myeloid Leukemia
|
Phylosymbiosis was recently proposed to describe the eco-evolutionary pattern , whereby the ecological relatedness of host-associated microbial communities parallels the phylogeny of related host species . Here , we test the prevalence of phylosymbiosis and its functional significance under highly controlled conditions by characterizing the microbiota of 24 animal species from four different groups ( Peromyscus deer mice , Drosophila flies , mosquitoes , and Nasonia wasps ) , and we reevaluate the phylosymbiotic relationships of seven species of wild hominids . We demonstrate three key findings . First , intraspecific microbiota variation is consistently less than interspecific microbiota variation , and microbiota-based models predict host species origin with high accuracy across the dataset . Interestingly , the age of host clade divergence positively associates with the degree of microbial community distinguishability between species within the host clades , spanning recent host speciation events ( ~1 million y ago ) to more distantly related host genera ( ~108 million y ago ) . Second , topological congruence analyses of each group's complete phylogeny and microbiota dendrogram reveal significant degrees of phylosymbiosis , irrespective of host clade age or taxonomy . Third , consistent with selection on host–microbiota interactions driving phylosymbiosis , there are survival and performance reductions when interspecific microbiota transplants are conducted between closely related and divergent host species pairs . Overall , these findings indicate that the composition and functional effects of an animal's microbial community can be closely allied with host evolution , even across wide-ranging timescales and diverse animal systems reared under controlled conditions .
A large body of literature has documented genetic and environmental influences on the composition of host-associated microbial communities [1–10] . Although environmental factors are considered to play a much larger role than host genetics and evolutionary history [11] , host influences and their functional consequences are poorly elucidated and thus require systematic study across host–microbiota systems . Several outstanding questions remain regarding the nature of host effects on microbiota assembly . Are host–microbiota associations stochastically assembled , or might there be deterministic assembly mechanisms that predict these associations ? How rapidly do microbiota differences form between closely related host species , and are interspecific microbiota differences prone to decay over evolutionary time ? Can host-driven assembly of the microbiota be isolated from confounding variables such as diet , age , sex , and endosymbionts ? If there are microbiota differences between species , are they functional in an evolutionarily informed manner , such that mismatches between host and interspecific microbiota lead to reductions in fitness or performance , particularly when interspecific microbiota transplants are conducted between older host species pairs ? If host-associated microbial communities assemble stochastically through environmental acquisition with no host-specific influence , then microbiota compositions across related host species will not differ from expectations based on random community assemblies and dispersal limitations . Therefore , in a common environment , microbiota will form independent of host species ( Fig 1A ) , and any interspecific differences in microbiota composition would be arbitrary . In contrast , if hosts influence a sufficient amount of the composition of the microbiota , then under controlled rearing conditions , intraspecific microbial communities will structure more similarly to each other than to interspecific microbial communities ( Fig 1B ) . Similarly , if microbial communities are randomly established or are not distinguishable with regard to host evolutionary relationships , then dendrograms illustrating beta diversity distance relationships between microbial communities will not parallel the phylogeny of the host species ( Fig 1C ) . However , if microbial communities are distinguishable , then hosts with greater genetic divergence may exhibit more distinguishable microbiota . In this case , there will be congruence between the host phylogeny and microbiota dendrogram ( Fig 1D ) . As this outcome is not likely due to coevolution , cospeciation , or cocladogenesis of the entire microbial community from a last common ancestor , "phylosymbiosis" was proposed as a new term that does not necessarily presume that members of the microbial community are constant , stable , or vertically transmitted from generation to generation [1 , 12] . Rather , phylosymbiosis refers to an eco-evolutionary pattern in which evolutionary changes in the host associate with ecological changes in the microbiota . Phylosymbiosis leads to the explicit prediction that as host nuclear genetic differences increase over time , the differences in host-associated microbial communities will also increase . Indeed , phylosymbiosis has been observed in natural populations of sponges [13] , ants [10] , bats [14] , and apes [15 , 16] . However , other studies on termites [17] , flies [18–20] , birds [21] , and mice [22] have not observed strict patterns of phylosymbiosis or host-specific microbial signatures . In natural population studies , determining the forces driving phylosymbiosis is equivocal , as both environmental and host effects can covary and contribute to microbiota assembly . Importantly , major effects of the environment , age , or sex may overwhelm the ability to detect phylosymbiosis . Indeed , diet is a stronger determinant of whole microbial community structure than genotype in lab-bred mice [23] . Additionally , conjecture about the formation of host-specific communities should be resolved in a wider context , especially their functional significance , as microbiotas may be inconsequential to host biology or uniquely situated for certain host genotypes and fitness . Thus , the prevalence and functional significance of phylosymbiosis is uncertain and requires reductionist approaches to discriminate among the frequently confounded variables of host , environment , development , sex , and even endosymbiont status . Here , we quantify phylosymbiosis under laboratory conditions to control for environmental and host rearing variation . Prior investigations of phylosymbiosis have not typically controlled for these confounding variables , with the exception of male Nasonia wasps [1 , 2] and Hydra [5 , 24] . Specifically , we reared 24 species in the laboratory while controlling for sex ( virgin females ) , age , diet , and endosymbionts , thus removing major environmental variables and isolating the contribution of host species on microbiota assembly . The experimental systems , or “host clades , ” span four species of Nasonia parasitic jewel wasps , six species of Drosophila fruit flies , eight species of Anopheles , Aedes , and Culex mosquitoes , and six species of Peromyscus deer mice . An externally derived dataset with seven members of the hominid lineage [16] provides another mammalian and multigenus clade for reference and facilitates examination of natural populations in which phylosymbiosis was previously documented . Together , the five host clades include 31 distinct taxa and span a range of estimated divergence times from 0 . 2–108 million y . Last , we test the hypothesis that phylosymbiosis represents a functional association through a series of microbial transplants with autochthonous ( intraspecific ) and allochthonous ( interspecific ) microbiota in Nasonia and Peromyscus . We expect that an experimentally mediated disruption of phylosymbiosis will have functional costs that may lower host fitness or performance in an evolutionarily informed manner . Our findings demonstrate that a consistent set of controlled experimental and bioinformatic approaches in comparative microbiota studies can isolate host-driven phylosymbiosis .
Phylosymbiosis predicts that host clades will harbor distinguishable microbial communities ( e . g . , jewel wasps versus fruit flies versus deer mice , etc . ) and that more closely related host clades will exhibit more similar microbial communities ( e . g . , insects versus mammals ) . Indeed , at a broad scale , we found that host clades harbored relatively distinct microbial communities ( Fig 2A , ANOSIM , R = 0 . 961 , p < 1e–6 ) . Furthermore , there was significant microbiota differentiation between the mammalian and invertebrate host clades in the principle coordinates analysis ( PCoA ) ( Fig 2A , ANOSIM , R = 0 . 905 , p < 1e–6 ) . The PCoA shows insect groups separating along two dimensions of a plane , with the mammals distinguished orthogonally from that plane in a third dimension , suggesting that variance in insect microbial communities is fundamentally different than that in mammals . As is well established , the gut communities of mammals were dominated by the bacterial classes Clostridia ( Firmicutes ) ( Fig 2B , hominid 42% , Peromyscus 37% ) and Bacteroidia ( Bacteroidetes ) ( Fig 2B , hominid 15% , Peromyscus 37% ) , while the insect clades were dominated by Proteobacteria ( Fig 2B , Drosophila 78% , mosquito 69% , Nasonia 77% ) . This same bacterial divide is also seen in the network analysis , with significant clustering of the insect microbial communities around Proteobacteria , and the mammal microbial communities around subsets of shared and unique Firmicutes and Bacteroidetes ( G-test , p < 1e–6 , Fig 2C ) . Microbial diversity as measured by the Shannon index [25] was approximately 35% higher in mammalian hosts compared to insects , indicating more diverse symbiont communities among the mammalian clades ( Fig 2D; Nested analysis of variance [ANOVA]: phylum effect [mammals versus insects]: F1 , 302 = 419 . 82 , p < 0 . 001; clade effect nested within phylum: F3 , 298 = 18 . 46 , p < 0 . 001; species effect nested within clade and phylum: F26 , 272 = 7 . 94 , p < 0 . 001 ) . We implemented a random forest classifier ( RFC ) supervised learning algorithm to quantify the degree to which individual microbial communities can be classified into their respective host clade . RFC models show a strong ability to classify microbial communities to their correct host clades based on OTUs ( 98 . 5% classification accuracy ) ( S1 Table ) . Additionally , models distinguish mammals and insect samples with high accuracy ( 95 . 9% classification accuracy ) ( S1 Table ) . Cross-validation prevents overfitting by ensuring that classification accuracy is assessed using only samples excluded from model training . We also used RFC models to identify the most distinguishing bacterial taxonomic level for both interclade distinction and the divide between mammals and insects . Genera provided the strongest ability to predict host clade ( 99 . 0% classification accuracy ) ( S1 Table ) ; however , the major groups of insects and mammals were better distinguished by family-level community classification ( 98 . 3% classification accuracy ) ( S1 Table ) . Taken together , these results illustrate that evolutionary relationships of the host clades broadly covary with differences in microbial communities . While differentiation of the five clades could in part be attributable to varied experimental conditions for each animal group ( since they were reared separately ) , clustering of the vertebrate microbial communities from the insect microbial communities is independent of rearing conditions and suggests a host-assisted structuring of microbial communities . Phylosymbiosis predicts that an individual’s microbial community will exhibit higher similarity to communities of the same host species than to those from different host species . The degree of similarity can be variable but should correlate with genetic relatedness of the host species . Pairwise comparisons of beta diversity distances between all individuals within each host clade reveal that the average distance between microbial communities within a species is always less than between species ( S1 Fig ) . Summarized beta diversity also reveal lower intraspecific versus interspecific distances , with significant differences observed for all clades ( Fig 3A , Each dataset: Mann–Whitney U , p < 1e–6 ) . We next evaluated intraspecific microbiota clustering through Bray–Curtis beta diversity interrelationships with PCoA and statistically assessed the strength of interspecific microbiota distinguishability with ANOSIM ( Fig 3B ) . Visualization of the first three principle components revealed that individual samples clustered around their respective species’ centroid position . In all host clades , each host species harbored significantly distinguishable microbial communities ( Fig 3B , ANOSIM p < 0 . 001 for all host clades ) . Notably , the ANOSIM R-values of interspecific microbiota distinguishability within a host clade positively correlated with the maximal age of divergence of the species in the host clades ( Fig 3C , Regression Analysis Log Transformed Clade Age , R2 = 0 . 92 , p = 0 . 006; Untransformed Clade Age , R2 = 0 . 70 , p = 0 . 048 ) . Thus , host clades with higher total divergence times between species had stronger degrees of microbiota distinguishability , while less diverged host clades exhibited less microbiota distinguishability . For example , with an estimated host divergence time of 108 million y [27] , mosquitoes showed the greatest distinguishability of their microbiota . Conversely , in Nasonia jewel wasps , which only diverged between 200 , 000 and 1 million y ago [28] , the relative strength of clustering was less distinct but still statistically significant . The three intermediate aged clades showed corresponding intermediate levels of clustering: Drosophila had an estimated divergence time of 62 . 9 million y [29] , hominids diverged 9 million y ago [30] , and Peromyscus diverged 11 . 7 million y ago [31] . Therefore , the phylosymbiotic prediction that host species will exhibit significant degrees of specific microbiota assembly was supported in these observations , even under highly controlled conditions in the laboratory models . Microbiota specificity was maintained among very closely related and very divergent species , and a connection was observed between the magnitude of host genetic divergence and microbiota similarity . As microbiota clustering was supported within species across all five animal clades , it should be possible to model the strength of how well communities of bacteria predict their host species and how specific members of the microbiota affect these predictions . We therefore used RFC models trained on the microbiota of each host clade to evaluate classification accuracy ( i . e . , the percentage of assigning microbiota to their correct host species ) and the expected predicted error ( EPE , i . e . , the ratio of model accuracy relative to random classification ) . RFC results indicated that the operational taxonomic units ( OTUs ) for Drosophila and Peromyscus and genus taxonomic levels for hominid , mosquito and Nasonia have the highest classification accuracies , with significant EPE observed for all clades ( EPE > 2 , S1 Table ) . At the genus level , the mosquito and Drosophila host clades exhibited the strongest results ( mosquito , classification accuracy = 99 . 8% , EPE = 558 . 9; Drosophila , classification accuracy = 97 . 2% , EPE = 31 . 7 ) . Other host clades demonstrated significant but comparatively lower strength models . The reduced predictive power of these models may be due to a number of factors , such as a lower number of host species ( Nasonia , classification accuracy = 88 . 7% , EPE = 13 . 4 ) , uneven sample representation from each species ( hominid , classification accuracy = 53 . 4% , EPE = 2 . 1 ) , and lower sequencing coverage ( Peromyscus , classification accuracy = 61 . 4% , EPE = 2 . 5 ) . To determine the most distinguishing genera of the bacterial community , we examined the resulting loss of model classification accuracy when each genus was excluded from RFCs ( S2 Table ) . Distinguishability within the Drosophila , Nasonia , and mosquito clades was driven primarily by genera in Proteobacteria , which represent five ( 14 . 0% model accuracy ) , seven ( 11 . 3% model accuracy ) , and eight ( 18 . 2% model accuracy ) of the top ten genera , respectively . Three of the ten most distinguishing genera in Drosophila females are from the Acetobacteraceae family ( 9 . 5% model accuracy ) , previously recognized to be “core” microbiota members [19 , 32] . Three of the twenty most distinguishing genera in Nasonia females were closely related symbionts from the Enterobacteriaceae family ( genera: Proteus , Providencia , Morganella; 3 . 1% model accuracy ) , consistently found in our previous studies of Nasonia males [1 , 2] . Eight genera from the phylum Proteobacteria dominate mosquito female distinguishability , primarily three Gammaproteobacteria of the order Pseudomonadales ( 8 . 2% model accuracy ) , and three Betaproteobacteria of the family Comamonadaceae ( 5 . 9% model accuracy ) . Hominid interspecific distinguishability was driven by the phylum Firmicutes , particularly of the order Clostridiales that contains three of the most distinguishing genera ( 1 . 5% model accuracy ) . The genus Allobaculum conferred nearly double the distinguishing power of any other bacteria in Peromyscus ( 3 . 8% model accuracy ) , and it is associated with low-fat diet , obesity , and insulin resistance in mice [33] . As may be expected , genera of the abundant phyla Firmicutes and Bacteroidetes dominated the majority of distinguishability in Peromyscus ( 10 . 6% model accuracy ) , but genera from Proteobacteria in the family Helicobacteraceae comprised four of the top eleven genera ( 4 . 4% model accuracy ) . Overall , microbiota composition can be used to predict host species with high accuracy , and genera commonly observed in other studies of these host clades underlie interspecific distinguishability . The major prediction of phylosymbiosis is that phylogenetic relatedness will correlate with beta diversity relationships of microbial communities among related host species . Microbiota dendrograms were constructed by collapsing individual samples to generate an aggregate microbial community for each species and then by comparing relationships of their beta diversity metrics . The matching cluster and Robinson–Foulds tree metrics were utilized to calculate host phylogenetic and microbiota dendrogram topological similarity , with normalized distances ranging from 0 . 0 ( complete congruence ) to 1 . 0 ( complete incongruence; [34] ) . Matching cluster weights topological congruency of trees , similar to the widely used Robinson–Foulds metric [34 , 35] . However , matching cluster takes into account sections of subtree congruence and therefore is a more refined evaluation of small topological changes that affect incongruence . Significance of the matching cluster and Robinson–Foulds analyses was determined by the probability of randomized bifurcating dendrogram topologies yielding equivalent or more congruent phylosymbiotic patterns than the microbiota dendrogram . Additionally , using the same methodology , matching cluster and Robinson–Foulds metrics were evaluated for Bray–Curtis , unweighted UniFrac [36] , and weighted UniFrac [36] beta diversity dendrograms at both 99% and 97% clustered OTUs ( S2 Fig ) . The cytochrome oxidase I ( COI ) gene was used to construct the phylogeny for each host clade , which compared well to established phylogenetic or phylogenomic trees for all species included in the study ( Nasonia [27]; Drosophila [28]; hominids [29]; mosquitoes [26] ) . Peromyscus was further resolved with an additional marker ( arginine vasopressin receptor 1A [AVPR1A] ) to reflect the latest phylogenetic estimates [37 , 38] . Nasonia female wasps exhibited an equivalent phylogenetic tree and microbial community dendrogram , representing exact phylosymbiosis ( Nasonia wasps , Fig 4A ) . These results parallel previous findings in Nasonia males [1 , 2] . Despite congruency , the Nasonia clade has limited topological complexity with only four species , therefore resulting in a relatively marginal significance . Mice also show nearly perfect congruence , with the exception of Peromyscus eremicus ( Fig 4B ) . Drosophila fruit flies ( Fig 4C ) showed the lowest topological congruency but were still moderately significant . Four of the six species show correct topological relationships , while the microbial community relationships of Drosophila pseudoobscura and D . erecta are topologically swapped . These results are different from previous findings in Drosophila that utilized a different experimental design , set of taxa , and sequencing technology [19] . However , the evidence for phylosymbiosis is tentative in Drosophila as , unlike other clades , there is no significant congruence for either unweighted or weighted UniFrac metrics ( S2 Fig ) . Previous studies detected no pattern of phylosymbiosis across Drosophila species [19] , which could be attributed to Drosophila’s constant replenishment of microbes from the environment [18 , 20] or the dominance by the bacterial genus Acetobacter , which is important for proper immune and metabolic development [19] . The two additional clades , mosquitoes and hominids , showed significant phylosymbiosis ( Fig 4D and 4E ) . Specifically , the mosquitoes showed accurate separation of Culex and Aedes genera from Anopheles , and the topological departures from phylosymbiosis appeared in two of the bifurcations between closely related species . The hominid microbial community dendrogram reflects the correct branching of Gorilla from Homo sapiens , followed by bonobos and chimpanzees , with the exception that one of the chimpanzee subspecies grouped more closely with the bonobo lineage . These results are similar to previous observations that the relationships of the microbial communities parallel those in the host phylogeny [16] . With the exception of Drosophila , which yielded variable evidence for host–microbiota congruence , significant degrees of phylosymbiosis were observed across clades with varying tree similarity metrics and microbiota beta diversity analyses . Microbiota–host distinguishability and topological congruence does not strictly imply that the phylosymbiotic associations are fitness directed , though it naturally follows that a particular host species may be more ideally suited for an autochthonous versus allochthonous microbiota . We therefore performed a series of microbial transplants to test the prediction that inoculated microbiota from a different species would decrease aspects of host performance or fitness in contrast to inoculated microbiota from the same species . Moreover , if there is selection on host–microbiota interactions such that microbiotas are uniquely or better situated for resident host backgrounds , then transplanted microbiota from a divergent species could drive more pronounced reductions in host functions than transplanted microbiota from a closely related species . In Peromyscus , we followed a previously established protocol [39] to transplant the microbial communities from six rodent donor species into a single recipient species , P . polionotus , as well as a control group in which the microbial communities from P . polionotus were introduced to intraspecific individuals of P . polionotus . Inventories of fecal microbiota from donor and recipient mice revealed that portions of the donor microbiota successfully transferred . The estimated amount of transplanted OTUs and their relative abundance ranged from 6 . 5%–26 . 2% and 11 . 4%–40 . 7% , respectively , when analyzed at the 99% OTU cutoff level . Variation in the transfer of foreign microbes was dependent on donor species and its divergence from the recipient species ( S3 Fig ) . We then measured dry matter digestibility , or the proportion of food material that is digested by the animal . Consistent with selection on host–microbiota interactions , mice that were inoculated with microbial communities from more distantly related hosts exhibited decreased dry matter digestibility ( Fig 5 ) . These results were only significant when the group receiving feces from P . eremicus donors was removed ( Fig 5 ) . Notably , the microbiota of P . eremicus is not congruent with our predictions of phylosymbiosis ( Fig 4 ) . Thus , only the taxa showing phylosymbiosis exhibited the functional trend with digestibility . Distantly related donor species ( Neotoma lepida and Mus musculus ) did not drive significance , as the correlation remained statistically significant when investigating only Peromyscus donors ( excluding P . eremicus; Fig 5 ) . In the most extreme cases in which mice were inoculated with the microbial communities from P . californicus or M . musculus , there was approximately a 3% decrease in dry matter digestibility , which is on par with the decrease in digestibility observed as a result of helminth infections in Peromyscus [40] . Animals must consume more food to meet energy demands when faced with decreases in digestibility . Indeed , mice inoculated with microbial communities from P . californicus or M . musculus exhibited significantly higher food intakes than the control group ( S4 Fig; Tukey’s honest significant difference ( HSD ) test: p = 0 . 001 for P . californicus to P . polionotus; p = 0 . 044 for M . musculus to P . polionotus ) . The mice inoculated with the microbes from P . eremicus performed just as well , if not better , than the control groups in terms of dry matter digestibility ( Fig 5 ) but still had slightly higher food intakes ( S4 Fig ) . In Nasonia , we used an in vitro rearing system to transplant heat-killed microbial communities from three Nasonia donor species into larvae of N . vitripennis or N . giraulti [41] . We then measured the survival of the recipients from first instar larva to adulthood . In both N . vitripennis and N . giraulti hosts , interspecific microbiota transplantations exhibited significant decreases in survival to adulthood when compared to intraspecific microbial transplantations ( Fig 6 ) . Specifically , N . giraulti with a N . vitripennis microbiota yielded a 24 . 5% average survival decrease in comparison to a N . giraulti microbiota ( Fig 6A , Mann–Whitney U , p = 0 . 037 ) . Interestingly , N . giraulti with a microbiota from the more closely related N . longicornis exhibited a similar but nonsignificant survival reduction ( 23 . 7% , Fig 6A , Mann–Whitney U , p = 0 . 086 ) . N . vitripennis with a N . giraulti or N . longicornis microbiota exhibited a 42 . 6% ( Fig 6B , Mann–Whitney U , p < 0 . 0001 ) and 23 . 3% ( Fig 6B , Mann–Whitney U , p = 0 . 003 ) average survival decrease in comparison to a N . vitripennis microbiota , respectively ( Fig 6A , Mann–Whitney U , p < 0 . 0001 ) . Comparisons were also made between noninoculated hosts and those inoculated with interspecific backgrounds ( N . giraulti background: N . vitripennis inoculum p = 0 . 07 , N . longicornis inoculum p = 0 . 26; N . vitripennis background: N . giraulti inoculum p = 0 . 001 , N . longicornis inoculum p = 0 . 15 ) .
Under phylosymbiosis , host-associated microbial communities form , in part , as a result of interactions with the host rather than through purely stochastic processes associated with the environment . Specifically , we predicted that given closely related animals reared in controlled environments , the relationships of the microbiota would be congruent with the evolutionary relationships of the host species . Previous evidence for phylosymbiosis under controlled regimes existed in Nasonia [1 , 2] and Hydra [24] , and wild populations of sponges [13] , ants [10] , and apes [15 , 16] also exhibited this pattern . Here , in a comprehensive analysis of phylosymbiosis in a diverse range of model systems , we report the widespread occurrence of this pattern under strictly controlled conditions as well as a functional basis in the context of host digestive performance in mice and survival in wasps . These results represent the first evidence for phylosymbiosis in Peromyscus deer mice , Drosophila flies , a variety of mosquito species spanning three genera , and Nasonia wasp females with the inclusion of N . oneida . Previous studies in Nasonia measured male phylosymbiosis and did not include N . oneida [1 , 2] . By rearing closely related species from the same host clade in a common environment , and by controlling age , developmental stage , endosymbiont status , and sex , the experiments rule out confounding variables that can influence microbiota relationships in comparative analyses . Eliminating these variables is important because they often substantially correlate with interspecific differences . Thus , our findings demonstrate that a uniform experimental and bioinformatic methodology can excavate host effects on phylosymbiosis from other potentially confounding variables in comparative microbiota studies . We observed marked differences in microbial diversity and community structure between mammalian and invertebrate host clades . Mammalian communities were more diverse and dominated by Bacteroidetes and Firmicutes , while insect-associated communities were less diverse and primarily dominated by Proteobacteria . These results are consistent with previous microbial inventories conducted in mammals and insects [6 , 42] . Together , these findings suggest large-scale differences in the host–microbiota interactions between mammals and insects . These differences across host phyla could be due to a variety of possibilities , including host genetics , diet , age , and rearing environment . To remove confounding variables that structure host–microbiota assemblages and to rigorously test phylosymbiosis , we utilized an experimental design within four host clades that isolated the effects of host evolutionary relationships from other effects ( i . e . , diet , age , rearing environment , sex , endosymbionts ) . We found that host species consistently harbored distinguishable microbiota within each host clade . Additionally , we found significant degrees of congruence between the evolutionary relationships of host species and ecological similarities in their microbial communities , which is consistent with the main hypothesis of phylosymbiosis . These results importantly expand previous evidence for this eco-evolutionary pattern and demonstrate that related hosts reared under identical conditions harbor distinguishable microbial assemblages that can be likened to microbial community markers of host evolutionary relationships . It is conceivable that recently diverged species ( i . e . , those younger than several hundred thousand years ) would have less genetic variation and fewer differences in microbiota composition . Furthermore , divergent hosts may have vast differences in physiology that overwhelm the likelihood of observing phylosymbiosis . Surprisingly , we observed phylosymbiosis to varying degrees in all host clades , and the age of clade divergence positively correlates with the level of intraspecific microbiota distinguishability . Thus , as host species diverge over time , microbial communities become more distinct [1 , 12] , and the limits of detecting phylosymbiosis may occur at extreme scales of incipient or ancient host divergence times . The mechanisms by which phylosymbiosis is established requires systematic investigation . Perhaps the most apparent regulator of host–microbiota interactions is the host immune system . A previous study of phylosymbiosis in Hydra demonstrated that antimicrobial peptides of the innate immune system are strong dictators of community composition , and expression of antimicrobial peptides are necessary for the formation of host-specific microbiota [5] . Furthermore , genome-wide association studies in humans [43] , mice [8] , and Drosophila [44] have identified a large immune effect in which host immune genes can explain variation in microbial community structure . Interestingly , host immune genes often exhibit rapid evolution and positive selection compared to genes with other functions [45 , 46] . While this trend is often explained by the host–pathogen arms race [45] , it is also likely due to host evolutionary responses for recruiting and tending a much larger collection of nonpathogenic microbes . Other host pathways may also underlie the observed species-specific microbiota signatures . Hosts produce glycans and mucins on the gut lining that may serve as biomolecular regulators of microbial communities [47 , 48] . For example , knocking out the gene for α1–2 fucosyltransferase inhibits production of fucosylated host glycans on the gut surface and significantly alters microbial community structure [49] . Additional knockout studies have demonstrated the roles of circadian clock genes [50] , microRNAs [51] , and digestive enzymes [52] in determining microbial community structure . These various physiological systems might also interact with one another and may have even evolved in tandem to regulate microbial community structure . Alternatively , rather than hosts “controlling” their microbiota , microbes may be active in selecting which host niches to colonize . For example , hosts have been compared to ecological islands , where environmental selection of the microbiota through niche availability may occur [53] . However , given the large number of studies that demonstrate the role of microbes in improving host performance [54] , we find it unlikely that hosts would assume a solely passive role in these interactions . An elegant study allowed microbial communities from various environments ( soil , termite gut , human gut , mouse gut , etc . ) to compete within the mouse gut [55] . This study found that a foreign community of the human gut microbiota exhibited an early competitive advantage and colonized the mouse gut first . Later , the mouse gut microbiota dominated and outcompeted the human gut microbiota [55] . Thus , community assembly is not a monolithic process of host control but likely a pluralistic combination of host control , microbial control , and microbe–microbe competition . In this context , both population genetic heritability and community heritability measurements of the microbiota will be useful in prescribing the varied genetic influences of a foundational host species on microbiota assembly [56] . The acquisition route of microbes could also influence our understanding of phylosymbiosis . If phylosymbiosis is observed when the microbiota is acquired horizontally from other hosts , the environment , or some combination of the two , then phylosymbiosis is presumably influenced by host-encoded traits such as control of or susceptibility to microbes . However , maternal transmission of microbes is argued to be a common trend in animals [57] . For example , sponges exhibit vertical transmission of a diverse set of microbes in embryos [58] . Transmission of full microbial communities is unlikely in most systems , given that the communities of developing animals tend to exhibit markedly lower diversity and distinct community structure compared to adults [1 , 59 , 60] . Thus , it is improbable that phylosymbiotic relationships are explained simply by community drift over host evolutionary divergence . There could be a subset of microbial taxa that are more likely to be transmitted from mother to offspring that in turn affect what other microbes colonize . For instance , in humans , the family Christensenellaceae is situated as a hub in a co-occurrence network containing several other gut microbes and has a significant population genetic heritability [61] . When Christensenella minuta was introduced into the guts of humanized mice , the microbial community structure was significantly altered [61] . This microbe , as well as others , can therefore be likened to a keystone taxa or "microbial hub" that can impact community structure despite low abundance [61–63] . Thus , one could hypothesize that phylosymbiotic relationships in some systems may be driven by host transmission of microbial hubs that determine whole community structure through ensuing microbe–microbe interactions . However , further work is needed to test this hypothesis . The congruent relationships between hosts and associated microbial communities are likely maintained through their positive effects on host performance and fitness but could be neutral or harmful as well . While the importance and specificity of hosts and microbes in bipartite associations has been demonstrated on host performance [64] , it is unclear whether such effects commonly occur for hosts and their complex microbial communities . If they exist , disruption of phylosymbiosis via hybridization or microbiota transplants should lead to reduced fitness or performance . For instance , hybridization experiments demonstrate negative interactions or "hybrid breakdown" between host genetics and the gut microbiota that drives intestinal pathology in house mice [65] and severe larval lethality between N . vitripennis and N . giraulti wasps [2] . Furthermore , transplant experiments show that all microbes are not equal for the host . An early study demonstrated that germ-free rabbits inoculated with a mouse gut microbiota exhibited impaired gastrointestinal function compared to those given a normal rabbit microbiota [66] . Together , these functional studies and others suggest that interactions between hosts and their microbiota are not random and instead occur at various functional levels . Here , we add an evolutionary component to these ideas by demonstrating that microbial communities from more evolutionarily distant hosts can be prone to more pronounced reductions in host performance or fitness . Specifically , Peromyscus deer mice inoculated with microbial communities from more distantly related species tended to exhibit lower food digestibility . The exception to this trend was the P . eremicus to P . polionotus group , which did not exhibit any decrease in digestibility . It should be noted that P . eremicus also did not follow phylosymbiosis ( Fig 4B ) , which may explain the departure from our expected trend in digestibility . For example , deviations from phylosymbiosis could be due to a microbial community assembly that is inconsequential to host digestibility . Therefore , transferring a nonphylosymbiotic community between host species may not yield performance costs . An alternative explanation for our results could be that hosts are acclimated to their established microbiota , and the introduction of foreign microbiota either elicits a host immune response or disrupts the established microbiota , thus decreasing digestibility . One technique to distinguish between adaptation and acclimation would be to conduct experiments in germ-free P . polionotus recipients . However , the derivation of germ-free mammals is a difficult and expensive process [67] and has not been conducted for Peromyscus . Earlier studies utilizing germ-free mammals demonstrate that microbial communities from evolutionarily distant hosts negatively impact gastrointestinal function [66] and immune development [68] , thus supporting our hypothesis of functional matching between host and the gut microbiota . Additionally , among very closely related species , Nasonia exposed to interspecific microbiota have lower fitness than those exposed to intraspecific microbiota . While this experiment utilized heat-killed bacteria to avoid shifts in the microbiota composition during media growth , the protocol is sufficient to test the predictions of phylosymbiosis . First , isolated microbial products can exert drastic effects on eukaryotic partners . For example , a sulfonolipid purified from bacteria can induce multicellularity in choanoflagellates [69] . Additionally , the insect immune system can respond with strain-level specificity to heat-killed bacteria [70] . Therefore , we hypothesize that each Nasonia host species evolved to the products of their own gut microbiota rather than those of gut microbiota from related host species . Together , results from the Peromyscus and Nasonia functional experiments reveal the importance of host evolutionary relationships when considering interactions between hosts and their gut microbial communities and ultimately the symbiotic processes that can drive adaptation and speciation [71 , 72] . The molecular mechanisms underlying the functional bases of phylosymbiosis in various systems demand further studies Overall , we have established phylosymbiosis as a common , though not universal , phenomenon under controlled rearing with functional effects on host performance and survival . It is worth emphasizing again that this term is explicit and different from many other similar terms , such as coevolution , cospeciation , cocladogenesis , or codiversification [73] . While cospeciation of hosts and specific environmentally or socially acquired microbes—e . g . , hominids and gut bacterial species [74] or the bobtail squid and Vibrio luminescent bacteria [75]—could contribute in part to phylosymbiosis , concordant community structuring with the host phylogeny is not dependent on parallel gene phylogenies but instead on total microbiota compositional divergence . Phylosymbiosis does not assume congruent splitting from an ancestral species because it does not presume that microbial communities are stable or even vertically transmitted from generation to generation [1 , 12] . Rather , phylosymbiosis predicts that the congruent relationships of host evolution and microbial community similarities could have varied assembly mechanisms in space and time and be newly assembled each generation ( though see our discussion of transmission routes above ) . Moreover , the findings here imply that across wide-ranging evolutionary timescales and animal systems , there is a functional eco-evolutionary basis for phylosymbiosis , at least under controlled conditions . It may be difficult to detect phylosymbiosis in natural populations because of extensive environmental variation that overwhelms the signal . We suggest that one way to potentially overcome this challenge is to start with laboratory-controlled studies that identify ( i ) phylosymbiotic communities and ( ii ) the discriminating microbial taxa between host species . Resultantly , investigations can test whether these microbial signatures exist in natural populations , albeit perhaps in a smaller fraction of the total microbiota that is mainly derived by environmental effects . Another advantage of controlled studies is that the functional effects , both positive and negative , of a phylosymbiotic community assembly can be carefully measured in the context of host evolutionary history .
Procedures involving functional microbiota transplants in Peromyscus mice were approved by the University of Utah Institutional Animal Care and Use Committee under protocol 12–12010 . Mice obtained from the Peromyscus Genetic Stock Center were reared under IACUC approved protocols , and only fecal samples were directly utilized . While our paper contains data for several primate species , this data was conducted by another research group , has been previously published , and is now publicly available . Thus , there was no requirement of approved protocols for the primate species . Nasonia were reared as previously described [2] . Four strains were used: Nasonia vitripennis ( strain 13 . 2 ) , N . longicornis ( IV7U-1b ) , N . giraulti ( RV2x ( u ) ) , N . oneida ( NAS_NONY ( u ) ) . To collect individuals for microbiota analysis , virgin females were sorted as pupae into sterile glass vials and collected within the first 24 h of eclosing as adults . Subsequently , they were rinsed with 70% ETOH for 2 min , a 1:10 bleach solution for 2 min , followed by two rinses in sterile water . Individuals were then placed in 1 . 5 ml tubes and flash frozen in liquid nitrogen . They were then stored at –80°C until DNA extractions . Fifty individuals were collected per strain . Nine strains of Drosophila were obtained from the University of California San Diego Drosophila Species Stock Center . Six strains were used in the microbiome analysis because they were Wolbachia-free: Drosophila melanogaster ( Strain Dmel , stock number 14021–0248 . 25 ) , D . simulans ( Dsim , 14021–0251 . 195 ) , D . yakuba ( Dyak , 14021–0261 . 01 ) , D . erecta ( Dere , 14021–0224 . 01 ) , D . pseudoobscura ( Dpse , 14011–121 . 94 ) , and D . mojavensis ( Dmow , 15081–1352 . 22 ) . The three strains that tested positive for Wolbachia ( method described below ) were: D . sechellia ( 14021–0248 . 25 ) , D . ananassae ( 14021–0371 . 13 ) , and D . willistoni ( 14030–0811 . 24 ) . All strains were reared on a cornmeal media ( Drosophila Species Stock Center: http://stockcenter . ucsd . edu/info/food_cornmeal . php ) with a sterile Braided Dental Roll ( No . 2 , Crosstex , Atlanta , Georgia , US ) inserted into the surface of the media . All stocks were incubated at 25°C with a 12-h light–dark cycle and monitored every 24 h . Every 14 d , stock vials were cleared of any emerged adults , and 6 h later , ten virgin females and three males were transferred to new food vials . This conditioning on the same food was done for five generations before setting up media vials for sample collection . For each of the six strains , five virgin females were mated with two males and allowed to oviposit for 24 h; afterwards , the parents were removed and the vials were incubated as per above . After 12 d , vials were cleared and virgin females were collected every 4–6 h over a 36-h period . All females were rinsed with 70% ETOH for 2 min , a 1:10 bleach solution for 2 min , followed by two rinses in sterile water . Individual adult flies were then placed in 1 . 5 ml tubes and flash frozen in liquid nitrogen . They were then stored at –80°C until DNA extractions . Approximately 25–30 virgin adult females were collected per strain . Mosquitoes were acquired from the Malaria Research and Reference Reagent Resource Center as eggs on damp filter paper within 24 h of being laid . Eight strains were used: Anopheles funestus ( strain name FUMOZ ) , An . farauti s . s . ( FAR1 ) , An . quadrimaculatus ( GORO ) , An . arabiensis ( SENN ) , An . gambiae ( MALI NIH ) , Aedes aegypti ( COSTA RICA ) , Ae . albopictus ( ALBO ) , and Culex tarsalis ( YOLO F13 ) . Eggs were floated in 350 ml of sterile water with 1 . 5 ml of 2% yeast slurry and autoclaved within a sterile and lidded clear plastic container . Containers were enclosed within a larger sterile clear container and placed inside an incubator set at 25°C with a 12-h light–dark cycle and monitored every 24 h . After 48 h , the hatched larvae were sorted out and 100–150 of each species were placed in new sterile water ( 150 ml ) with 30 mg of powdered koi food ( Laguna Goldfish & Koi all season pellets ) . Water level was maintained at 150 ml , and larvae were fed 30 mg of powdered koi food every day for a total of 13 d . All pupae were discarded ( frozen and autoclaved ) on day 10 , and new pupae were collected every 12 h on day 11 , 12 , and 13 . Water samples were also collected and frozen for microbial analysis on day 11 . To collect individuals for microbiota analysis , pupae were sorted according to sex , and all females were rinsed with 70% ETOH for two min , then 1:10 bleach solution for two min , followed by two rinses in sterile water . Individual pupae were then placed in 1 . 5 ml tubes and flash frozen in liquid nitrogen . They were then stored along with their corresponding water sample at –80°C until DNA extractions . Ten to 25 individuals were collected per strain . Fecal samples were collected from the Peromyscus Genetic Stock Center at the University of South Carolina . Six stock species of Peromyscus were used: P . maniculatus ( stock BW ) , P . polionotus subgriseus ( PO ) , P . leucopus ( LL ) , P . californicus insignis ( IS ) , P . aztecus hylocetes ( AM ) , and P . eremicus ( EP ) . All mice were reared using their standard care practices at the stock center on the same mouse chow diet . Cages were cleaned at regular intervals for all species , and all species were caged within the same facility . Individuals from nonmating cages of females ( five to six per cage ) were used for collections . Fecal pellets were collected on a single morning from individual mice directly into a sterile tube and placed on dry ice before being stored at –80°C for 24 h . Samples were then shipped overnight on dry ice and again stored at –80°C until DNA extractions . One to three pellets from 15 individuals were collected per strain . In order to eliminate the introduction of confounding factors and exclude any subjects that had a pinworm infection at the time of sample collection , we conducted a screen to confirm the pinworm status of each mouse . Pinworm status was confirmed by PCR . Primers utilized to amplify the 28S rDNA D1 and D2 domains of multiple pinworm species were developed and confirmed with positive DNA samples of Syphacia obvelata and Aspiculuris tetraptera ( received from the Feldman Center for Comparative Medicine at the University of Virginia ) . The C1 primer 5ʹ-ACCCGCTGAATTTAAGCAT-3ʹ and the D1 primer 5ʹ-TCCGTGTTTCAAGACGG-3ʹ were amplified under the following reaction conditions: 94°C for 1 min; 35 cycles of 94°C for 30 s , 55°C for 30 s , 72°C for 30 s; and a final elongation time at 72°C for 2 min . The resultant samples were then visualized on a 1% agarose gel . Of the 84 fecal specimens analyzed , 8 of the samples showed amplification at 750 bp corresponding to the expected amplification size of the pinworm DNA sequence . For confirmation , the 750 bp bands were extracted using a Wizard Gel Extraction Kit ( Promega Corporation , Madison , Wisconsin , US ) and sequenced ( GENEWIZ , Inc , New Jersey , US ) . Sequence results confirmed the presence of Aspiculuris tetraptera infection , and these 8 samples and were excluded from further analysis . The presence or absence of Wolbachia was checked using two replicates of three individuals per species . DNA extraction was performed with PureGene DNA Extraction Kit ( Qiagen ) , and fragments of the 16S rDNA gene were PCR amplified using primer set WolbF and WolbR3 [76] . Only stock strains that were Wolbachia negative were used in the experiments . Individual insects ( and the mosquitoes’ corresponding water samples ) were mechanically homogenized with sterile pestles while frozen within their collection tube . The samples were then thawed to room temperature for 30 s and flash frozen again in liquid nitrogen with additional mechanical homogenization . The samples were finally processed using the ZR-Duet DNA/RNA MiniPrep Kit ( Zymo Research , Irvine , California , US ) . Samples were then quantified using the dsDNA BR Assay kit on the Qubit 2 . 0 Fluorometer ( Life Technologies ) . The PowerSoil DNA isolation kit ( Mo Bio Laboratories , Carlsbad , California , US ) , was utilized to extract DNA from 20 mg of mouse fecal material per sample according to manufacturer’s protocol after being mechanically homogenized with sterile pestles while frozen within their collection tube . Samples where then quantified using the dsDNA BR Assay kit on the Qubit 2 . 0 Fluorometer . Total genomic DNA was quantified using dsDNA HS Assay kit on the Qubit . Using two μl of DNA , a 20 μl PCR reaction of 28S general eukaryotic amplification was conducted on each sample , with only 25 cycles . Products were purified using Agencourt AMPure XP , quantified using the dsDNA HS Assay kit on the Qubit , and compared to the amount of 16S amplification from the same DNA volume and PCR reaction volume as previously described [2] . PCR amplification of the bacteria 16S rRNA was performed with the 27F 5ʹ-AGAGTTTGATCCTGGCTCAG-3ʹ and 338R 5ʹ-GCTGCCTCCCGTAGGAGT-3ʹ “universal” bacterial primers with the NEBNext High-Fidelity 2X PCR Master Mix; duplicate reactions were generated per sample , which were pooled together postamplification . For sequencing runs 1 ( Peromyscus ) and 2 ( Nasonia , mosquito , and Drosophila ) , 16S PCR products that were made into libraries had their concentrations normalized relative to about 1 , 000 ng/ml and 2 , 000 ng/ml of the 28S quantity for library prep respectively . Using the Encore 384 Multiplex System ( NuGEN , San Carlos , California , US ) , each samples’ 16S product was ligated with Illumina NGS adaptors and a unique barcode index ( after the reverse adaptor ) . The samples were then purified using Agencourt AMPure XP and quantified using the dsDNA HS Assay kit on the Qubit . Samples were subsequently pooled . Each pooled library was run on the Illumina MiSeq using either the MiSeq Reagent Kit V2 or V3 for paired-end reads . Run 1 was conducted at the University of Georgia Genomics Facility and run 2 was conducted at Vanderbilt Technologies for Advanced Genomics ( VANTAGE ) . Sequence quality control and OTU analyses were carried out using QIIME version 1 . 8 . 0 [77] . Forward and reverse paired-end sequences were joined and filtered if they met the following criteria: they fell below an average Phred quality score of 25 , contained homopolymer runs or ambiguous bases in excess of 6 nucleotides , or were shorter than 200 base pairs . Sequences were also removed if there were errors in the primer sequence or if barcodes contained errors and could not be assigned to a sample properly . A total of 5 , 065 , 121 reads passed quality control for the meta-analysis , with an average read length of 310 ± 48 nucleotides . Drosophila: 648 , 676 reads , average length 315 ± 23 . hominid: 1 , 292 , 542 reads , average length 247 ± 38 . mosquito: 664 , 350 reads , average length 328 ± 19 . Nasonia: 864 , 969 reads , average length 322 ± 15 . Peromyscus: 295 , 752 reads , average length 347 ± 12 . Chimeric sequences were evaluated and removed using the UCHIME algorithm [78] for the intersection of de novo and GreenGenes 13_5 non-chimeras [79] . The sequences were then clustered into OTUs at 94% , 97% , and 99% similarity using the USEARCH open-reference method [80] . OTUs were mapped at the respective percent against the GreenGenes 13_5 database and screened for a minimum group size of two counts , with dereplication based on full sequences [79] . Representative sequences were chosen as the most abundant representative in each OTU cluster and aligned using GramAlign [81] . A phylogenetic tree of the representative sequences was built in QIIME [77] with the FastTree method and midpoint rooting [82] . Taxonomy was then assigned to the OTU representatives with the UCLUST method against the GreenGenes 13_5 database [79] . OTU tables were constructed in QIIME [77] and sorted by sample IDs alphabetically . OTU tables were screened to remove any OTUs classified as chloroplast , unassigned , and Wolbachia . Individual samples were assessed for low sequence coverage affecting community profiles and diversity as well as for processing errors based on minimum count thresholds assessed against group means . Following rarefaction , counts were subsequently chosen as the highest rarefaction number allowed by the smallest sample’s count representation in each respective clade and the meta-analysis . Alpha diversity was measured using Shannon and Chao1 metrics generated with the QIIME alpha_rarefaction script . Plots of alpha diversity at a range of rarefied levels were used to assess and remove samples with low diversity . The PCoA ( Fig 2A ) components for the meta-analysis were constructed using the QIIME jackknifed_beta_diversity script . The OTU table first underwent rarefaction , followed by the computation of Bray–Curtis beta diversity distances for each rarefied table . PCoA plots of the first three coordinate dimensions were generated using a custom Python script . Individual samples are each depicted as a point and are colored by host clade of origin . The community profile ( Fig 2B ) for the meta-analysis was generated using a custom Python script and BIOM tools [83] . OTU tables were first converted to relative abundance for each sample , and bacterial taxonomy was collapsed at the class level . Bacterial classes were sorted alphabetically , and a stacked bar chart representing the relative abundance for each sample was constructed . The network analysis ( Fig 2C ) was visualized using Cytoscape [84] . OTU tables were first collapsed by bacterial taxonomy at the genus level , and QIIME’s make_otu_network script was used to construct connections between each bacterial genus to individual hosts based on relative abundance . Network files were then imported into Cytoscape , where the network was computed using an edge-weighted force directed layout . Nodes were colored by host clade , and connections were colored by key bacterial phylum observed in high abundance ( i . e . , Actinobacteria , Bacteroidetes , Firmicutes , Proteobacteria ) and gray for additional phylum . Alpha diversity plots ( Fig 2D ) were prepared using the Phyloseq package [85] . OTU tables collapsed by host species were imported into Phyloseq , and the plot_richness function was used to generate box-and-whisker plots of Shannon alpha-diversity . Plots were colored by host clade of origin . Microbiota dendrograms were constructed using the QIIME jackknifed_beta_diversity script . OTU table counts were first collapsed by host species of origin to get representative species microbiota profiles . The pipeline script performed 1 , 000 rarefactions on each table and calculated Bray-Curtis beta diversity distances for each . Bray–Curtis distance matrices were UPGMA clustered to give dendrograms of interspecific relatedness . The role of 97% versus 99% OTU clustering cutoffs and weighted and unweighted UniFrac beta diversity measures ( S2 Fig ) were evaluated for Robinson–Foulds and matching cluster congruence with host phylogeny . Host phylogenetic trees were constructed using sequences for each host species’ cytochrome oxidase gene downloaded from the NCBI . COI was chosen as a highly conserved molecular marker , and it is widely used for interspecific phylogenetic comparison [86] . Sequences were initially aligned using Muscle v3 . 8 . 31 [87] . Gap positions generated through inserts and deletions were removed , and overhanging sequence on 5ʹ and 3ʹ ends were trimmed . Models of molecular evolution were evaluated using jModelTest v2 . 1 . 7 [88] , and the optimal model was used for final alignment and tree building in RaxML v8 . 0 . 0 [89] . The Nasonia and Peromyscus clades were carried out using the same methodology—except for final alignment and tree building in PhyML v3 . 0 [90]—and for Peromyscus the AVPR1A gene was concatenated with COI to further resolve the phylogeny . All trees are concordant with well-established phylogenies from literature references noted in the Results section . Quantifying congruence between host phylogeny and microbiota dendrogram relationships ( Fig 4 ) was carried out with a custom Python script and the TreeCmp program [91] . The topologies of both trees were constructed , and the normalized Robinson–Foulds score [35] and normalized matching cluster score [34] were calculated as the number of differences between the two topologies divided by the total possible congruency score for the two trees . Next , 100 , 000 random trees were constructed with the same number of leaf nodes , and each was compared to the host phylogeny . The number of trees which had an equivalent or better score than the actual microbiota dendrogram were used to calculate the significance of observing that topology under stochastic assembly . Normalized results of both statistics have been provided to facilitate comparison . Matching cluster and Robinson–Foulds p-values were determined by the probability of 100 , 000 randomized bifurcating dendrogram topologies yielding equivalent or more congruent phylosymbiotic patterns than the microbiota dendrogram . Within each clade , the Bray–Curtis distances calculated by the jackknife_beta_diversity script ( Fig 3A ) were separated by those that compared microbiota within a host species and those that compared between host species . The box-and-whisker plots were constructed in Python . Coloring indicates host clade of origin , and all intraspecific and interspecific distances are represented for each clade . These distances were then compared between the groups using a nonparametric , two-tailed Mann–Whitney U test implemented in SciPy [92 , 93] . To evaluate intraspecific clustering ( Fig 3B ) , the ANOSIM test was used to calculate the distinguishability of Bray–Curtis distances based on species of origin . Bray–Curtis distance matrices were generated using the QIIME jackknifed_beta_diversity script on tables of individuals rarefied 1 , 000 times . The QIIME script compare_categories was used to calculate ANOSIM scores using the Bray–Curtis distance matrix and host species as categories . 1 , 000 permutations were used to calculate the significance of clustering for each clade . Three-dimensional PCoA plots were generated in Python using components generated from Bray–Curtis distance matrices in QIIME , and the first three components are shown . Points are colored by host species within each clade , and colors correlate with the species labels in Fig 4 for reference . A general linear regression was performed to test the correlation between age of clade origin and the intraspecific clustering measured through ANOSIM R-statistic scores . Cladogenesis Age was Log10 transformed to normalize the distance scale between samples ( 1 , 10 , 100 MYA ) . The regression was carried out in Stata v12 . 0 to determine the coefficient ( R2 ) and significance ( p-value ) . OTU tables were first collapsed at each bacterial taxonomic level ( i . e . , phylum… genus ) using the QIIME script summarize_taxa . Then , both the raw OTU table and each collapsed table underwent ten rarefactions to an even depth using the QIIME script multiple_rarefactions_even_depth . RFC models were constructed with the supervised_learning script for 1 , 000 rounds of ten-fold Monte Carlo cross validation on each table . At each level , the results were collated and averages were taken for the ten rarefied tables . Host species were used as the category for RFC model distinguishability , testing the ability to assign samples to their respective host species . The average class error for each clade was subtracted from 100 to get the percent accuracy of the models at each taxonomic level . The same methodology was used for constructing RFC models for the meta-analysis , with the only exception being that host species , host clade , and vertebrate or invertebrate categories were tested for distinguishability .
|
Studies on the assembly and function of host-microbiota symbioses are inherently complicated by the diverse effects of diet , age , sex , host genetics , and endosymbionts . Central to unraveling one effect from the other is an experimental framework that reduces confounders . Using common rearing conditions across four animal groups ( deer mice , flies , mosquitoes , and wasps ) that span recent host speciation events to more distantly related host genera , this study tests whether microbial community assembly is generally random with respect to host relatedness or "phylosymbiotic , " in which the phylogeny of the host group is congruent with ecological relationships of their microbial communities . Across all four animal groups and one external dataset of great apes , we apply several statistics for analyzing congruencies and demonstrate phylosymbiosis to varying degrees in each group . Moreover , consistent with selection on host–microbiota interactions driving phylosymbiosis , transplanting interspecific microbial communities in mice significantly decreased their ability to digest food . Similarly , wasps that received transplants of microbial communities from different wasp species had lower survival than those given their own microbiota . Overall , this experimental and statistical framework shows how microbial community assembly and functionality across related species can be linked to animal evolution , health , and survival .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"taxonomy",
"invertebrates",
"organismal",
"evolution",
"medicine",
"and",
"health",
"sciences",
"microbiome",
"microbiology",
"vertebrates",
"animals",
"mammals",
"animal",
"phylogenetics",
"animal",
"models",
"phylogenetics",
"drosophila",
"melanogaster",
"model",
"organisms",
"data",
"management",
"phylogenetic",
"analysis",
"microbial",
"evolution",
"molecular",
"biology",
"techniques",
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"medical",
"microbiology",
"epidemiology",
"evolutionary",
"systematics",
"molecular",
"biology",
"insects",
"disease",
"vectors",
"molecular",
"biology",
"assays",
"and",
"analysis",
"techniques",
"arthropoda",
"mosquitoes",
"rodents",
"peromyscus",
"genetics",
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"life",
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"genomics",
"evolutionary",
"biology",
"amniotes",
"organisms"
] |
2016
|
Phylosymbiosis: Relationships and Functional Effects of Microbial Communities across Host Evolutionary History
|
Within-population genetic diversity is greatest within Africa , while between-population genetic diversity is directly proportional to geographic distance . The most divergent contemporary human populations include the click-speaking forager peoples of southern Africa , broadly defined as Khoesan . Both intra- ( Bantu expansion ) and inter-continental migration ( European-driven colonization ) have resulted in complex patterns of admixture between ancient geographically isolated Khoesan and more recently diverged populations . Using gender-specific analysis and almost 1 million autosomal markers , we determine the significance of estimated ancestral contributions that have shaped five contemporary southern African populations in a cohort of 103 individuals . Limited by lack of available data for homogenous Khoesan representation , we identify the Ju/'hoan ( n = 19 ) as a distinct early diverging human lineage with little to no significant non-Khoesan contribution . In contrast to the Ju/'hoan , we identify ancient signatures of Khoesan and Bantu unions resulting in significant Khoesan- and Bantu-derived contributions to the Southern Bantu amaXhosa ( n = 15 ) and Khoesan ! Xun ( n = 14 ) , respectively . Our data further suggests that contemporary ! Xun represent distinct Khoesan prehistories . Khoesan assimilation with European settlement at the most southern tip of Africa resulted in significant ancestral Khoesan contributions to the Coloured ( n = 25 ) and Baster ( n = 30 ) populations . The latter populations were further impacted by 170 years of East Indian slave trade and intra-continental migrations resulting in a complex pattern of genetic variation ( admixture ) . The populations of southern Africa provide a unique opportunity to investigate the genomic variability from some of the oldest human lineages to the implications of complex admixture patterns including ancient and recently diverged human lineages .
Southern Africa is home to populations carrying significant human genomic variation . The analysis of patterns of DNA variation , have placed modern human origins within Africa [1] , with the most divergent contemporary lineages found in the indigenous Khoesan inhabitants of southern Africa [2]–[6] . Defined by their use of clicking languages and a foraging-based subsistence , contemporary Khoesan are largely restricted to the greater Kalahari regions of Namibia and Botswana . Representing a collection of isolated subpopulations with dwindling numbers and subpopulation extinctions , the Khoesan population identifier once represented a broader geographical dispersal reaching the most southern tip of Africa . Historical migrations into southern Africa including agro-pastoral Southern Bantu from a western/central African homeland beginning roughly 1 , 500 years ago [7] , [8] , followed over a millennium later by the arrival of European settlers and East-Indian slaves [9] , shaped the ancestral contributions of contemporary southern Africans . These intra- and inter-continental contributions led to historical events that perpetuated population dispersals , isolations and assimilation between populations , ultimately giving rise to complex genomic admixture . The pattern of genomic variation in contemporary southern African populations thus resulted from unions between the most diverse genomes found within Africa to the least differentiated as represented by populations impacted by a severe founder effect ( bottleneck ) associated with the out-of-Africa dispersal [2] , [10]–[13] . Determining the ancestral origins of contemporary southern African admixture is limited by a number of factors including the availability of well-characterized subjects , limited availability of genomic data for appropriate founder populations , biases in current content genotyping arrays and analytical challenges . Lack of genomic data for southern African populations defined based on linguistics and culture broadly as Bantu and Khoesan , has perpetuated biases . To minimize these limitations , we leveraged genotype information from the largest current content array dataset that was available at the time the study was initiated in 2010 , interrogating over 1 million genome-wide data points ( Illumina HumanOmni1-Quad BeadChips ) . The 103 individuals in this study represent five southern African populations defined as Khoesan , specifically Ju/'hoan and ! Xun , Southern Bantu , specifically amaXhosa , and European-initiated admixed populations , specifically Coloured and the newly described Baster population ( Figure 1 ) . At the time of submission there had been limited largely gender-specific analyses performed for pooled subsets of Southern Bantu [14] , [15] , while gender-specific [14] , [16] and more extensive analysis for the Coloured had focused on non-regional sub-structure [17] , [18] . While we previously considered the extent of whole exome diversity between two Ju/'hoan and a single ! Xun , providing limited genome-wide analysis using the smaller 500 K Illumina arrays [5] , no study had determined possible admixture contributions to these foraging-based populations . We merge our data with the only Khoesan-derived genome-wide dataset , the South African #Khomani [4] . The availability of globally relevant genomic data ( published and from the Illumina iControl Database ) provides a means to predict contributing migratory homogenous founder populations ( specifically as a result of Bantu migration and European colonization ) , which most closely represent historical events that have impacted relations between southern African populations ( Figure 1 ) . In contrast , identifying indigenous founder contributions is more problematic . Contemporary Khoesan populations have either themselves experienced varying degrees of non-Khoesan contribution , or may not accurately represent the likely lost ancient ancestral lineages that once thrived along the southern coast of Africa at the time of non-Khoesan arrival . A major goal of our study was therefore to define a Khoesan population with negligible non-Khoesan contribution . Using anthropological , cultural , linguistic , as well as personal interactions within the remaining Khoesan communities of Namibia , the Ju/'hoan and ! Xun were identified as likely candidates . Identifying early human divergence and unique forager-based genomic signatures , we further assess the significance of ancestral contributions within our study sample using multiple analytical approaches , while providing significant insights into the history of the region .
Our data suggests that the Ju/'hoan represent the most likely homogenous contemporary Khoesan population . Two factors that set the Khoesan apart from other global populations include early divergence and forager substituted by hunting existence . We use genomic data to look for signatures that differentiate the Ju/'hoan in this study based on these criteria .
Although previous studies have considered the role of admixture in shaping the genetic diversity among southern Africans , in particular the Coloured , no study has assessed ( i ) the significance of these contributions , ( ii ) how this admixture has shaped or contributed to distinct population subgroups among southern Africans , or ( iii ) the possibility that southern Africans may be harboring ancient vestiges of a ‘lost’ or understudied source of genetic diversity . The extent of admixture within people today defined broadly as Khoesan complicates these analyses , further compounded by subject heterogeneity . We attempt to assess sources of admixture and heterogeneity and ultimately identify and characterize a Khoesan-representative population that displays little to no significant non-Khoesan ancestral contribution . Such a population we identify as the Ju/'hoan . This study suggests that the Ju/'hoan form a unique ancestral population for the human lineage , distinct ( i . e . , most dissimilar ) from all contemporary populations for which data is currently available , including other forager populations . Gender-specific analysis confirms genetic isolation of the Ju/'hoan from non-Khoesan populations , while autosomal analysis shows no significant non-Ju/'hoan ancestral contribution . While the rest of the world was driven into agriculture at the end of the Last Glacial Maximum [48] , the Ju/'hoan appear to have maintained their hunter-gatherer based subsistence . Significant agricultural-driven genomic signatures were absent from the study subjects , while previously described functionally significant ancestral forager-based alleles were identified . One of the most interesting findings to emerge from our analysis of foraging versus agricultural genome profiles was a potential for an increased chemical dependency for tobacco . We observed heavy tobacco usage by all study participants , both male and female . Historical accounts include the successful use of tobacco as a means of trade or coercion of indigenous Khoesan by European settlers [49] . Anthropological observational studies suggest an unusual devotion of Ju/'hoan to master the difficult task of tobacco cultivation over food-based cultivation when minimal farming is adopted [50] . Our data therefore suggests that the Ju/'hoan have not had adequate time to adapt to selective pressure associated with the use of tobacco . The significance of genes associated with inflammatory , autoimmune or immune diseases , being significantly enriched between forager and agriculturalist requires further investigation . Coined ‘the harmless people’ [51] , it may not be surprising that we found a greater representation of loci associated with mood-based disorders . Physical characteristics within the Ju/'hoan with possible links to enriched pathways include ( i ) maintaining both thermal and fluid homeostasis within desert climates , ( ii ) the need for rapid wound repair , and ( iii ) a possible state of semi-erection in males . The latter , a locally accepted trait , has been documented in Bushmen rock art [52] and reported as a defining characteristic [53] . Unlike the Ju/'hoan , the ! Xun exhibit significant male-derived non-Khoesan African ancestral contribution to their gene pool . While autosomal marker analysis suggests roughly 20 . 5% non-Khoesan admixture , Y-chromosomal analysis suggests a possible East African Nilotic contribution , although extended autosomal substructure analysis suggests a proto-Bantu and Sandawe contributions while excluding for a Nilotic contribution . Evidence for Bantu migration into the northern Kalahari region of Namibia appears as early as the 7th century [54] . Bantu-Khoesan interaction is evident by the introduction of iron-based arrow tips and cooking utensils , as well as the use of cultivated tobacco by the Khoesan , and conversely the inclusion of clicks within the non-click languages of early Bantu immigrants , for example isiXhosa ( the language of the amaXhosa ) . The possibility of a pre-Bantu , likely east African migration into the region requires further investigation . The Ju/'hoan-Yoruba differentiating AIMs defined two unique ! Xun subgroups suggesting independent genomic prehistories . The Ju/'hoan-ancestral ! Xun share on average 54 . 8% ( range 43 . 6–67 . 5% ) of their genomic heritage with contemporary Ju/'hoan , and include the Angolan ! Xun from this study ( Figure 4C ) . In contrast , we identify a new non-Ju/'hoan ( range 0 . 9–5 . 3% ) ancestral contribution to 50% of the ! Xun , averaging 71 . 1% ( range 65 . 9–76 . 9% ) ( Figure 4D ) . We suggest that the ! Xun identifier as used today incorporates different Khoesan prehistories , one independent from contemporary Ju/'hoan . Interestingly , the non-Khoesan African contribution to the ! Xun appears to be uniform with ancestral signatures shared by contemporary Bantu and Sandawe . Our data therefore suggests that these two independent ! Xun lineages carry the same non-Khoesan African contributions . The amaXhosa Bantu carry an almost equal ancient ancestral Khoesan contribution , while AIMs analysis suggests that this contribution is largely non-Ju/'hoan . It is highly feasible to assume that the southward migration of the amaXhosa along the eastern coast would constitute differing Khoesan contribution from the more westerly located inland Ju/'hoan . This observation is further supported by the lack of L0k mtDNA representation within the amaXhosa . Further analysis would be required to determine the relationship between the Khoesan contribution to the amaXhosa and the ‘unknown’ ! Xun lineage identified in this study . While the ! Xun and amaXhosa show evidence for historical admixture , inter-continental migrations to the region has led to the emergence of more recent admixture . Considering a highly variable non-Khoesan contribution to the #Khomani , the Coloued and Baster populations represent a complex admixture pattern that transverses both the earliest and the most recently diverged human lineages . Defining and tracing such significant ancestral contributions provides a unique model not only to track human expansion and prehistories , but also define gene regions undergoing selection [55]–[57] and recombination [58] , [59] . The datasets presented in this study provide a unique resource for further genomic analyses . In the Ju/'hoan we speculate that the fraction of ROH has been lowered as a result of early divergence with other populations , while increased as a result of a smaller effective population size ( Ne ) . Unlike cosmopolitan societies , the maintenance of population size is an essential survival mechanism for foragers . As a result of varied contribution of ancestrally distinct chromosomal segments , contemporary southern African populations would display admixture-based recombination , decreasing total ROH . The complex ‘Khoesan-African-Asian-European’ ancestral admixture fractions of the Baster and Coloured would be further impacted by gender-specific meiotic recombination rates [60] . The observation of gender biased ancestral contributions include a paternally-driven ‘African non-Khoesan’ contribution to the ! Xun , maternally-driven ‘Khoesan’ contribution to the amaXhosa , and maternally-driven ‘Khoesan’ and paternally driven ‘non-African’ ( likely European ) contribution to the Baster and Coloured . Although previous studies have looked at the ancestral contributions to the Coloured [17] , [18] , no studies have to date addressed complex admixture within the Basters . Emerging from a common historical background to the Coloured , the Baster population have since the late 1800 s distinguished themselves as independent from the Coloured , migrating to the now Baster nation of Rehoboth in Namibia [9] . In contrast to the Coloured we show the Baster population to carry the largest Khoesan-derived maternal contribution ( 91 . 7% compared to 64 . 3% in the Coloured ) and the largest paternal European-derived contribution ( 93 . 3% compared to 71 . 8% ) , while autosomal marker analysis confirmed increased ‘Khoesan’ and ‘European’ contributions and decreased ‘Asian’ and ‘African non-Khoesan’ contributions . Geographic distribution of the ‘African non-Khoesan’ admixture fraction showed an increased contribution and significance from west to east ( Baster , NC- , D6- to EC-Coloured , Figure S7 ) , with significance of the Bantu-derived fraction ( 1 . 6% , 5 . 8% , 15 . 4% and 16 . 6% , respectively ) based on nine ancestral fractions ( Figure 2B ) and mirroring Bantu population distributions ( Statistics South Africa Census 2011 and Community Survey 2007 , ( http://www . statssa . gov . za ) ) . The most significant ‘Asian’ contribution was found within persons who were residents of District Six . Previously a residential region of Cape Town , District Six was geographically located at the heart of the Dutch-East Indian slave trade [43] , [47] . In this study we define an almost equal ‘broadly Indian’ and ‘Sino-Tibetan’ contribution to the D6-Coloured . Besides fixation for the dry earwax allele in the Han Chinese and Koreans , an elevated frequency ( 71% ) has been reported for the Indian Dravidian inhabitants of Tamil Nadu ( correlating to the DR-S-LP3 population from this study ) [41] . Lack of this allele in our subjects alludes to a non-Dravidian Indian contribution which was further supported by non-contributing independent GC4 Dravidian subgroup substructure . Since the submission of this paper , two publications have emerged that have addressed genomic variation within the southern African region we studied . The first assessed ∼500 K custom designed variants including study subjects described as Ju/'hoan and ! Xun ( ! Xuun ) and grouped together as Kx'a speakers [61] . Significant findings consistent with our analyses include ∼20% non-Khoesan contribution to the ! Xun ( after fixing non-Khoesan contribution to the Ju/'hoan at 6% ) , while confirming minimal admixture contribution within the Ju/'hoan . Additionally this study dates the ! Xun African non-Khoesan-mixture time to around 450 years ago and implies an ancient genetic link between southern and Eastern Africa . Our observation for a predominance of the East African Nilotic ( non-Bantu ) E1b1b Y-chromosomal haplogroup within the ! Xun may provide further confirmation for a southern-eastern link , although our autosomal analysis suggests that this link is more likely related to the Sandawe and not the Nilotic peoples . No ancestral link was observed between east Africans and the Ju/'hoan from our study . The second paper looked at ∼2 . 3 million variants including study subjects described as Ju/'hoan , ! Xun , Coloured ( Colesburg ) , Coloured ( Wellington ) and undefined South African Bantu-speakers [62] . Consistent with our findings and the first paper , this study depicts the Ju/'hoan as a relatively homogenous population , while depicting a non-Khoesan contribution to the ! Xun . In contrast to both studies , we suggest additional ! Xun substructure and present the notion of two distinct ! Xun prehistories . Our assumption is that contemporary ! Xun represent a unique ancestral Khoesan lineage with an ancient non-Khoesan African ( predominantly Bantu ) contribution , with one subgroup having shared an ancient genetic link with the Ju/'hoan while the other remained genetically isolated from the Ju/'hoan . Notably the second study reports a predominance of Angolan ! Xun study representation , represented in our study by the Ju/'hoan-ancestral genetic link . Further between study confirmation includes the representation of a South Asian ( Indian ) contribution to the Coloured , in particular the Wellington-Coloured ( approximately 60 miles from Cape Town and District Six ) compared with the Colesburg-Coloured ( approximately 500 miles from Cape Town and District Six ) , with minimal East Asian ancestral contribution . Unlike the Wellington-Coloured , however , no subject in this study presented with non-African ancestry ( Bantu and/or Khoesan ) . A single individual from District Six lacked any observable Khoesan contribution . No distinction was made for the Southern Bantu included in the latter study , so no correlation could be made with regards to the amaXhosa . The availability of new southern African datasets will allow for a more comprehensive analysis of population substructure within the region . This study demonstrates both ancient and recent admixture within southern Africans . Cautionary concerns include: ( i ) bias in current content arrays towards non-African populations will greatly impact inferences about diversity among southern Africans , while lack of rare allele representation would diminish an ability to separate southern African subpopulations , ( ii ) lack of an available common ancestral genome that truly represents the earliest modern humans results in biases in methods used to attain divergence times among populations , ( iii ) inferences regarding population structure and recent admixture events are currently based on analyses of data reflecting contemporary genetic variation between populations , which is still largely lacking for the region of Southern Africa , and ( iv ) this is confounded by lack of data for populations that may actually be extinct . Taking these cautionary observations into consideration , we present an analysis of a set of individuals that , to the best of our knowledge , most accurately defines a homogenous ancestral Khoesan contribution , the Ju/'hoan . Additional cultural differences that may have restricted interbreeding between our Ju/'hoan and local agro-pastoral groups include; economic distinction ( those without and those with possessions ) , language ( Khoesan versus Bantu ) , social practices ( egalitarian versus patriarchal society ) , kinship ( bilineal/patrilineal versus matrilineal ) , marital locality ( matrilocal versus patrilocal ) , and marital practices ( monogamy versus polygamy , and no bridal payment versus a bridal payment ) . While a recent study acknowledges western influences as a result of the establishment of a Ju/'hoan ‘reserve’ near Tsumkwe [62] , for this reason we actively avoided recruitment within the immediate vicinity of Tsumkwe . In contrast to the Ju/'hoan , we describe not only a ‘African non-Khoesan’ ( almost equal proto-Bantu and Sandawe ) contribution to the ! Xun , but define two distinct ! Xun lineages , with , and largely without , a shared Ju/'hoan ancestry . Additionally we describe a new population with complex ancient and recently diverged genomic contribution , the Basters of Namibia . Sharing a history with the South African Coloured , population-defining genetic signatures include increased significance of Khoesan and European contribution with gender-specific bias to a maternal and paternal contribution , respectively . In contrast , while we confirm increased ‘African non-Khoesan’ ( largely Bantu and to a lesser extent Sandawe ) and ‘Asian’ ( Indian and Indonesian ) contribution to the Coloured , we demonstrate significant regional-based ancestral differences which would have important implications for gene mapping studies that rely on self-reported ancestry among Coloured and non-Coloured populations . As inter- and intra-continental migration increases globally , so will the impact of admixture on disease gene mapping studies . The dataset presented provides an opportunity to investigate the impact of arguably some of the most diverse genomic contributions within single population identifiers .
The study was approved by the Ministry of Health and Social Services in Namibia , the human Research Ethics Committee at the University of Stellenbosch , South Africa ( Project # N08/03/072 ) , the Institutional Review Board Committee at the J . Craig Venter Institute ( IRB# 2010-126 ) and previously the Human Research Ethics Committee at the University of New South Wales Australia ( HREC# 08244 ) . Consents were acquired either via verbal or written documentation with the understanding that the data generated will be made freely available to the scientific community as a collective . There are no known cultural limitations that would prohibit open access of the data . Genomic DNA was isolated from whole blood using the QIAamp DNA Blood Mini kit or the FlexiGene DNA kit ( QIAGEN ) and quantified on the NanoDrop spectrophotometer ( Thermo Scientific ) . A total of 105 subjects were genotyped using the Illumina HumanOmni1-Quad Beadchips . The Illumina GenomeStudio software ( version1 . 7 . 4 ) was used for the data analysis with a GenTrain score of 0 . 5 as the minimum for inclusion . Excluding indels , mtDNA , Y-chromosome markers and no-calls resulted in 927 , 298 variants . Two subjects ( both Ju/'hoan ) were excluded as a result of likely first cousin relatedness based on the estimation of the probable number of shared alleles at any given marker , identity by descent ( IBD ) values of 0 . 4351 and 0 . 4129 . Genotype calls have been made available without restrictions at http://www . jcvi . org/cms/research/projects/southern-african-genome-diversity-study/ for the complete dataset of 103 subjects according to population identifiers . MT-haplogroup specific markers ( Table S1 ) were identified using the phylogenetic tree ( www . phylotree . org ) build 13 [68] , amplified and Sanger sequenced . Briefly , PCR was performed using the FastStart Taq system ( Roche ) , product cleanup using the ExoSap method and Sanger Sequencing using the BigDye terminator cycling kit and the ABI 3100 Genetic Analyzer ( Applied Biosystems ) . Paternally derived Y-chromosome haplogroups were assessed from a total of 1 , 283 Y-chromosome markers represented in the Illumina Human Omni1-Quad array content . Markers were identified from the Y-Chromosome Consortium ( YCC ) 2008 nomenclature [69] . E1b1b-specific marker M215 ( rs2032654 ) was determined by amplicon-specific Sanger sequencing . All primer information and amplification conditions are available within Table S14 .
|
The Khoesan have received recent attention , as they are the most genetically diverse contemporary human populations . However , Khoesan populations are poorly defined , while archeological evidence suggests a once broader dispersal of click-speaking southern African foragers . Migrations into the regions populated by contemporary Khoesan involved agro-pastoral Bantu around 1 , 500 years ago , followed over a millennium later by the arrival of European colonists establishing a halfway station for a maritime route between Europe and the East , which led to unions between diverse global populations . Using almost a million genetic markers for 103 individuals , we confirmed a significant Khoesan contribution to five southern African populations . The Ju/'hoan show genetic isolation ( early divergence from all other modern humans ) , carry no significant non-Khoesan contributions , and unlike most global populations lack signatures of gene-based adaption to agriculture . The ! Xun show two distinct Khoesan prehistories; while comparable to the female-derived Khoesan contribution to the amaXhosa Bantu , the male-derived Bantu contribution to the ! Xun most likely represents cultural-driven gender-biased gene-flow . Emanating largely from male-derived European ancestral contributions , the Basters showed the highest maternal Khoesan contribution , while the Coloured showed the largest within population and regional-associated variability . The unique admixture fractions of the two latter populations reflect both early diverged and recently diverged human lineages .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genome",
"complexity",
"genomics",
"genome",
"evolution",
"genetics",
"population",
"genetics",
"biology",
"evolutionary",
"biology",
"genetics",
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"genomics",
"human",
"genetics"
] |
2013
|
Complex Patterns of Genomic Admixture within Southern Africa
|
Trypanosoma cruzi is a protozoan parasite that is transmitted by triatomine vectors to mammals . It is classified in six discrete typing units ( DTUs ) . In Chile , domestic vectorial transmission has been interrupted; however , the parasite is maintained in non-domestic foci . The aim of this study was to describe T . cruzi infection and DTU composition in mammals and triatomines from several non-domestic populations of North-Central Chile and to evaluate their spatio-temporal variations . A total of 710 small mammals and 1140 triatomines captured in six localities during two study periods ( summer/winter ) of the same year were analyzed by conventional PCR to detect kDNA of T . cruzi . Positive samples were DNA blotted and hybridized with specific probes for detection of DTUs TcI , TcII , TcV , and TcVI . Infection status was modeled , and cluster analysis was performed in each locality . We detected 30 . 1% of overall infection in small mammals and 34 . 1% in triatomines , with higher rates in synanthropic mammals and in M . spinolai . We identified infecting DTUs in 45 mammals and 110 triatomines , present more commonly as single infections; the most frequent DTU detected was TcI . Differences in infection rates among species , localities and study periods were detected in small mammals , and between triatomine species; temporally , infection presented opposite patterns between mammals and triatomines . Infection clustering was frequent in vectors , and one locality exhibited half of the 21 clusters found . We determined T . cruzi infection in natural host and vector populations simultaneously in a spatially widespread manner during two study periods . All captured species presented T . cruzi infection , showing spatial and temporal variations . Trypanosoma cruzi distribution can be clustered in space and time . These clusters may represent different spatial and temporal risks of transmission .
Chagas disease is a zoonotic parasitic disease , endemic in 22 countries of America , caused by the flagellated protozoa Trypanosoma cruzi . This disease affects approximately 7 million people in the world and represents the third parasitic disease of major world impact [1] . The parasite is transmitted through contact of contaminated feces of hematophagous insects from the Triatominae subfamily with wounds or mucosae of mammals , by blood transfusions , congenital transmission , organ transplants , laboratory accidents , and oral transmission [2] . Vectorial transmission occurs from southern United States to Patagonia ( 40°N to 45°S ) [3 , 4]; however , in the last decades , Chagas disease has spread to other continents due to alternative infection routes and migration [1] . In Chile , the disease is endemic in rural and suburban areas from latitudes 18°30’ to 34°36’ S [5] . Trypanosoma cruzi is a mono-flagellar protist ( Kinetoplastida ) . Its kinetoplastidic DNA ( kDNA ) displays like a concatenated discal web of maxicircles ( 20–40 kb; 20–25 copies/cell ) and minicircles ( 0 . 5–10 kb; 20000 copies/cell ) [6] . Minicircles are organized in four conservative regions ( conserved sequence blocks , CSB ) separated by four variable regions [7] . Due to the high number of minicircle copies and conserved sequences , they are used in the diagnosis of infection through polymerase chain reaction ( PCR ) , using primers that bind to the CSB [8] . Minicircle DNA amplification creates a very polymorphic product of the variable region , useful for T . cruzi genotyping through hybridization methods using characterized probes [9] . Six genetically related lineages of T . cruzi have been described , identifiable by markers , called discrete typing units ( DTUs ) : TcI , TcII , TcIII , TcIV , TcV , TcVI [10] . A new genotype called TcBat has been discovered in bats from Brazil [11] . Several approaches have been used to evaluate the biochemical and genetic diversity of T . cruzi isolates , but there is no unique genetic target that allows complete DTU resolution [12] . TcI exhibits the wider distribution , from Southern USA to Central Chile and Argentina [3] . TcII is found primarily in the domestic cycle from the South-Central region of South America . TcIII ranges from western Venezuela to the Argentine Chaco , mainly linked to the wild cycle in Brazil [13] . TcIV possess a similar distribution to TcIII but is absent in the Gran Chaco area . Finally , TcV and TcVI are found in Central and Southern South America [12] . So far , no clear association between the parasite genotype and the manifestation of the disease or drug resistance has been detected [14] , but there is evidence that suggests a selective role of hosts and vectors on the different DTUs [15–17] . More than 150 wild , synanthropic , and domestic mammal species have been found infected with T . cruzi in America , including most of the terrestrial mammal orders present [3 , 18] , playing a relevant role in the maintenance and interplay among wild , peridomestic and domestic cycles [19] . Small mammals are common feeding sources for triatomines in the sylvatic cycle of the endemic zone of Chile [20] , presenting smaller home ranges than larger mammal species [21] . Since the home range of triatomines is also small [22] , these mammals can act as important T . cruzi hosts , acquiring and maintaining the infection [23 , 24] . Infected species in Chile are the rodents Octodon degus , Phyllotis darwini , Abrothrix olivaceus , Rattus rattus , the lagomorph Oryctolagus cuniculus , and the marsupial Thylamys elegans , ranging from 32% to 83 . 6% [18 , 23 , 25–27] . North-Central Chile is a Mediterranean climatic influenced area characterized by lower richness of terrestrial mammals than other Mediterranean areas of the world [28] , and over 40% of the 30 wild or synanthropic mammal species present are small mammals , exhibiting relatively high abundances [29] . Triatomines can get infected with T . cruzi at any stage posterior to hatching , by consumption of contaminated mammal blood , cannibalism or coprophagy [3] . In Chile there are four triatomine species: Triatoma infestans , Mepraia spinolai , M . parapatrica , and M . gajardoi [30 , 31] , where T . infestans has been found in domiciliary and wild habitats [32 , 33] , while M . spinolai in domestic , peridomestic but mainly wild habitats [34] . Mepraia gajardoi and M . parapatrica are present in wild coastal areas [31] . Mepraia spinolai and T . infestans are distributed sympatrically in part of the endemic area [35] . Infection rates of T . cruzi detected by conventional PCR in sylvatic T . infestans and M . spinolai from Chile vary spatially and temporally , ranging from 36 . 5% to 68 . 6% and 14 . 9% to 76 . 1% , respectively [32 , 33 , 36–38] . TcI is the most frequently circulating DTU in T . infestans [36] , and M . spinolai [37] , as well as in Chilean small mammals , present as single and mixed infections [16 , 27] . However , there are differences between species regarding their infecting DTU [16 , 25 , 27 , 36 , 37] . Infection events can have one of three different spatial configurations: regular or uniform , random , or aggregated ( clustered ) ; however , to our knowledge the spatial configuration of T . cruzi infection in hosts and vectors has not been previously evaluated . To understand transmission cycles , it is important to establish whether cases of an infection–i . e . , the infected individuals—have the tendency to cluster together more than it would be expected by the natural clustering of the population affected [39] . In the present study , we aimed to assess spatial and temporal variations of T . cruzi infection , detecting the DTUs , by sampling triatomines and small mammals of the same areas in two contrasting seasons of the same year , using conventional and spatially-explicit statistical techniques .
Small mammals and triatomines were captured from January to February ( austral summer season ) and from July to August ( austral winter season ) of 2011 . The six study sites—Localities 1 to 6—were in North-Central Chile , from 30º49’S to 33º39’S , encompassing around 300 km from the northernmost to the southernmost study site ( S1 Fig ) . Most of the rainfall in all study sites concentrates between May and August , which are also the colder months [40] Details of each locality are shown in S1 Table . Base layers ( shapefiles ) of administrative boundaries , rivers and elevation were obtained from freely available sources for academic use and other non-commercial use [41 , 42]; point shapefiles of trapping sites and maps were produced specifically for this investigation , in QGIS Desktop 2 . 18 . 2 software , a free and open source Geographic Information System [43] . Small mammals were captured using live traps ( Rodentrap Special Forma and Rodentrap Berlin Forma , Santiago , Chile , and Tomahawk traps , Wisconsin , USA ) with rolled oat as bait and cotton as shelter for the captured animals . Traps were placed in linear patterns separated by approximately 10 m , labeled and georeferenced . Each captured mammal was anesthetized with isoflurane and blood sampled in a field laboratory . Detailed procedure is available at dx . doi . org/10 . 17504/protocols . io . wnxfdfn . Triatomines were captured using baited traps . A total of 90 yeast baited traps per day were set during summer , and 72 mouse baited traps during winter . Traps were placed following linear patterns , separated by 10 m in rocky outcrops and rock piles; and assorted according to the availability of terrestrial bromeliads if present . Each trap was georeferenced in UTM WGS84 19S coordinate system . Traps were activated at sunset and collected the next morning . Detailed capture procedure is available at dx . doi . org/10 . 17504/protocols . io . wnpfddn . Captured triatomines were transferred to individual flasks for transportation . Triatomine species were identified based on its morphological description [30 , 48] . Insects were euthanized with ether overdose , and their abdomens were dissected using individual scalpels . DNA was extracted from small mammals’ blood samples ( 100 μl ) using the Quick-gDNA Blood MiniPrep kit . Triatomines’ abdomens were macerated with 190 μl Guanidine-HCl 6 M—EDTA 0 . 2 M solution and incubated with 10 μl of proteinase K solution ( 20 mg/ml ) during 3 hours at 54°C . Samples were then centrifuged for 1 min at 10 . 000 x g; the supernatant was transferred to another microcentrifuge tube and its DNA was extracted using the Quick-gDNA MiniPrep ( Zymo Research ) kit . Both blood and triatomine eluates were resuspended in 100 μl nuclease free water . Trypanosoma cruzi infection status of each DNA sample was determined by conventional PCR using a master mix containing 5 μl of DNA sample , buffer solution 1x; dATP , dCTP , dGTP y dTTP 0 . 38 mM each; MgCl2 1 . 37 mM; 1 . 3 units of Paq DNA Polimerase ( Agilent ) ; 0 . 4 μM of each oligonucleotide: 121 and 122 , which anneal to CSB2 and CSB3 of T . cruzi’s kinetoplast minicircles , respectively [49]; and nuclease free water to complete 32 μl . Each run included a positive ( purified T . cruzi kDNA ) and a template free control ( nuclease free water ) . Amplification was performed with a cycling protocol of: 1 min at 98°C and 2 min at 64°C; followed by 33 cycles at: 94°C for 1 min and 64°C for 1 min; ending with a 10 min cycle at 72°C . Ten μl of amplified samples were run in a 2% Tris-Borate-EDTA agarose gel with GelRed nucleic acid stain for 60 min at 90 volts . Samples were considered positive to T . cruzi infection when a 330 pair base band was observed by ultraviolet transillumination after electrophoresis . PCR positive samples were genotyped with a DNA blot technique . In this procedure it is expected that two minicircle sequences will cross-react if hybridized under high stringency conditions only if they belong to the same sequence class; that is , if they present homologies in the divergent region [50] . Minicircle hybridization is a complex technique that has the advantage of working with low parasite amounts , and may be used for direct genotyping without the bias of parasite isolation and culture , which may favor the selection of some T . cruzi clones from a mixture [14 , 51] . Further details are specified in dx . doi . org/10 . 17504/protocols . io . sz2ef8e . R software version 3 . 5 . 1 , with packages rcompanion , RVAideMemoire , Lme4 and epiDisplay , were used for statistical analyses . Descriptive statistics of the infection status were included for species , localities , and study periods . Differences in the frequencies of infection were analyzed by species using Fisher’s exact test , testing a posteriori differences between mammal species using a pairwise test of independence for nominal data , with a significance level of α = 0 . 05 . The infection status of vectors was modeled using the locality ( 1–6 ) , study period ( summer vs . winter ) and the species ( T . infestans and M . spinolai ) as predictors in a factorial logistic regression . A separate model was generated for the infection status of small mammals , using three categories for the species variable: Octodon sp . , P . darwini , and all other small mammals combined , along with the variables locality and study period . Bar charts were created in Microsoft Excel ( Microsoft Office Professional Plus 2010 , version 14 . 0 . 7208 . 5000 ) . Cluster analysis was performed using SaTScan v9 . 4 . 4 64-bit software ( Kulldorff M . and Information Management Services , Inc . SaTScan v8 . 0: Software for the spatial and space-time scan statistics . http://www . satscan . org/ , 2009 . “SaTScan is a trademark of Martin Kulldorff . The SaTScan software was developed under the joint auspices of ( i ) Martin Kulldorff , ( ii ) the National Cancer Institute , and ( iii ) Farzad Mostashari of the New York City Department of Health and Mental Hygiene” ) . Spatial , temporal and space-time cluster detection were performed in each locality for small mammals and triatomines , separated and combined . We used the default software settings except by using an elliptical scanning window and 9999 iterations of Standard Monte Carlo procedure for calculations [52 , 53] . To determine if there was clustering of the infection status , we used the Bernoulli model [54] , where each individual ( triatomine or small mammal ) was either a case ( 1 ) —which corresponded to an infected vector or host—or a control ( 0 ) –an uninfected individual .
A total of 710 small mammals and 1140 triatomines were captured . Small mammals belonged to two rodent Suborders: Hystricomorpha and Myomorpha , and to one marsupial Order: Didelphimorphia [18] . Almost 76 . 5% of the captured mammals were Octodon sp . ( n = 356 ) and P . darwini ( n = 187 ) . Mepraia spinolai ( n = 595 ) and T . infestans ( n = 545 ) were collected , found in sympatry only in Locality 4 ( Fig 1 ) . Detailed number of small mammals and triatomines captured by locality and study period is available in S2 Table . All infection results are presented indicating the average and the 95% confidence interval , in Tables 1 and 2 . We detected 215 small mammals infected with T . cruzi ( 30 . 3% of infection; 95% CI 27 . 0–33 . 7% ) , presenting different infection rates among mammal species , without considering locality or study period ( Fisher’s exact test , p<0 . 001 ) . A posteriori pairwise comparisons showed that only Octodon sp . and P . darwini were statistically different ( adjusted p = 0 . 0287 ) , with P . darwini showing higher rates of infection ( 39 . 0% ) than Octodon sp . ( 25 . 0% ) . Rattus norvegicus and A . longipilis showed the highest and lowest infection rates , respectively . Locality 3 showed the highest , and Locality 1 showed the lowest infection rate when considering all mammals combined . During the summer , infection of small mammals was 35 . 6% and in the winter was 25 . 6% . In summer , the most and the less infected species were A . olivaceus and O . longicaudatus , respectively . During winter , R . norvegicus and A . longipilis presented the highest rate and lowest infection rates , respectively . Detailed results of infection are presented in Tables 1 and 2 , and in S2 Table . In the factorial logistic regression , all the tested variables were retained as predictors for infection status of small mammals . Phyllotis darwini and other small mammals presented greater odds of infection than Octodon sp; Locality 1 presented lower odds than all the rest localities; finally , small mammals exhibited lower odds of being infected in winter versus summer ( Table 3 ) . We detected 389 triatomines infected with T . cruzi ( 34 . 1% of infection; 95% CI 31 . 4–36 . 9% ) , with higher infection rates in M . spinolai ( 39 . 7% ) than T . infestans ( 28 . 1% ) when comparing both species without considering locality or study period ( Fisher's exact test , p<0 . 001 ) . Locality 5 presented the highest triatomine infection rates ( M . spinolai: 43 . 5% ) and Locality 4 the lowest ( both triatomine species combined: 26 . 5%; M . spinolai: 27 . 1%; T . infestans: 25 . 0% ) . Disregarding triatomine species and locality , higher infection rates were detected during winter ( 41 . 2% , n = 170 ) compared to summer ( 32 . 9% , n = 970 ) . During summer , Mepraia spinolai presented 38 . 9% of infection , and T . infestans 28 . 0% , combining all localities , and in winter , M . spinolai showed 41 . 6% , and T . infestans 33 . 3% . During summer , Locality 2 had the highest rate of infection , and Locality 3 the lowest . Meanwhile , during winter , Locality 3 presented the highest infection rates , and Locality 1 the lowest . Detailed results of infection are presented in Tables 1 and 2 , and in S2 Table . The model selected for triatomines retained only the species as predictor , showing that M . spinolai individuals were more frequently infected than T . infestans ( p<0 . 001; Table 3 ) . In small mammals , 45 out of 215 positive PCR samples hybridized with at least one probe tested ( 45 effective hybridizations ) . Only 110 out of 389 triatomine positive samples corresponded to effective hybridizations . We detected , in decreasing frequency , TcI , TcII , TcVI and TcV in small mammals ( Table 4 ) . Positive samples from A . bennetti , A . olivaceus and R . norvegicus did not bind to any probe . Only one positive sample of A . longipilis and R . rattus hybridized with TcI and TcII as single infections , respectively . Only Octodon sp . and P . darwini presented all four DTUs tested ( S3 Table ) . The DTUs detected in triatomines were TcI , TcII , TcV and TcVI , in decreasing frequency . In M . spinolai TcV was not detected , and TcVI was detected in only one sample ( Table 4 ) . We detected the four DTUs in all localities , except in Localities 2 and 4 where TcV was not detected . Locality 6 was the only study site with all four DTUs detected both in triatomines and small mammals . Disregarding locality , during summer in Octodon sp . and P . darwini only TcI and TcII were detected , as single infections , but during winter , all four DTUs were found . We observed the opposite pattern in triatomines , in which we detected all four DTUs during summer and just TcI and TcII in winter . Detailed results of DTUs are shown in S3 Table . In small mammals , we detected 62 . 2% single infections ( hybridization with just one DTU ) and 37 . 8% mixed infections ( hybridization with more than one DTU ) ( Table 5 ) . When analyzing the two most abundant species , Octodon sp . showed more single ( 72 . 7% ) than mixed infections ( 27 . 3% ) , while P . darwini presented a similar proportion of single ( 55 . 6% ) and mixed ( 44 . 0% ) infections . In triatomines , we detected 66 . 4% of single and 33 . 6% mixed infections , with T . infestans showing more mixed infections than M . spinolai ( 43 . 9% v/s 18 . 2% , respectively ) ( Table 5 ) . We detected a mixed infection in one O . longicaudatus with TcI+TcVI , and a single infection in the same rodent species with TcV . The marsupial species T . elegans presented a mixed infection with TcI+TcII , and the other two with DTU determined were single infections with TcI . When evaluating single and mixed infections by locality , there is not a clear pattern , but it seems that single infections were more frequent in both small mammals and triatomines . We did not detect mixed infections in small mammal species during summer . In triatomines we detected similar proportions of single and mixed infections in both study periods . We detected a total of 21 significant spatial , temporal and spatio-temporal clusters in five localities ( S4 Table ) . In general terms , T . cruzi clustering was more common in vectors than in hosts , with a total of 10 purely spatial and spatio-temporal clusters detected in triatomines in three localities; when combining vectors and hosts , we found 9 clusters . Most clusters were detected in Locality 6 ( 11 out of 21 ) . We mapped only purely spatial clusters of infection ( Fig 2 ) .
This study describes Trypanosoma cruzi infection status , infecting DTUs , and determines the spatial and temporal variations of infection in small mammals and triatomines of the endemic zone of Chile . Octodon sp . and Phyllotis darwini were the most represented small mammals , and they showed high infection rates , thus representing important wild hosts . Mepraia spinolai presented higher infection rate than Triatoma infestans; however , non-domestic populations of both vectors were infected in all localities and study periods evaluated , emphasizing the need for sustaining prevention measures even if domestic vectorial transmission has been interrupted . We detected the four tested DTUs in triatomines and small mammals , with an overall predominance of TcI , following the trend of Chile and America . Significant spatial , temporal and spatio-temporal clusters for infection were detected within localities , mainly in triatomines . Finally , we can conclude that T . cruzi infection varies between host and vector species , localities and study periods in North-Central endemic zone of Chile .
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Trypanosoma cruzi is a parasite that infects mammals , transmitted by triatomine insect vectors in America , causing Chagas disease in humans . There are six T . cruzi discrete typing units ( DTUs ) . Our goal was to estimate T . cruzi infection rates and describe the DTUs present in mammals and triatomines of Chile , evaluating spatial and temporal variation . We captured nine small mammal and two triatomine species in six localities during two periods ( summer/winter ) of the same year . We detected T . cruzi DNA and some DTUs were identified . We report one mammal species infected for the first time . Infection presented significant variation among species . The endemic vector had higher infection rates than Triatoma infestans . The DTUs TcI , TcII , TcV and TcVI were present , with predominance of TcI . Temporally , we detected higher rates of infection during summer in small mammals and during winter in triatomines . Infection was spatially and temporally aggregated in small mammals and vectors . Some species might have higher risk of infection , and this may be different between localities or periods , or even within the same locality .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
[
"medicine",
"and",
"health",
"sciences",
"chile",
"(country)",
"vector-borne",
"diseases",
"geographical",
"locations",
"vertebrates",
"parasitic",
"diseases",
"parasitic",
"protozoans",
"animals",
"mammals",
"organisms",
"seasons",
"protozoans",
"infectious",
"diseases",
"south",
"america",
"winter",
"protozoan",
"infections",
"disease",
"vectors",
"people",
"and",
"places",
"trypanosoma",
"cruzi",
"trypanosoma",
"eukaryota",
"earth",
"sciences",
"biology",
"and",
"life",
"sciences",
"species",
"interactions",
"amniotes",
"triatoma"
] |
2019
|
Spatio-temporal characterization of Trypanosoma cruzi infection and discrete typing units infecting hosts and vectors from non-domestic foci of Chile
|
Genome-wide association studies ( GWAS ) have established a variant , rs10993994 , on chromosome 10q11 as being associated with prostate cancer risk . Since the variant is located outside of a protein-coding region , the target genes driving tumorigenesis are not readily apparent . Two genes nearest to this variant , MSMB and NCOA4 , are strong candidates for mediating the effects of rs109939934 . In a cohort of 180 individuals , we demonstrate that the rs10993994 risk allele is associated with decreased expression of two MSMB isoforms in histologically normal and malignant prostate tissue . In addition , the risk allele is associated with increased expression of five NCOA4 isoforms in histologically normal prostate tissue only . No consistent association with either gene is observed in breast or colon tissue . In conjunction with these findings , suppression of MSMB expression or NCOA4 overexpression promotes anchorage-independent growth of prostate epithelial cells , but not growth of breast epithelial cells . These data suggest that germline variation at chromosome 10q11 contributes to prostate cancer risk by influencing expression of at least two genes . More broadly , the findings demonstrate that disease risk alleles may influence multiple genes , and associations between genotype and expression may only be observed in the context of specific tissue and disease states .
Variation at rs10993994 on chromosome 10q11 is associated with prostate cancer risk [1]–[5] . The risk polymorphism is located at the telomeric end of a 50 kilobase ( kb ) linkage disequilibrium block and is within 60 base pairs ( bp ) of the transcription start site of beta-microseminoprotein ( MSMB ) . MSMB has been characterized as a tumor suppressor [6] , and lower levels of its product , PSP94 , are associated with more aggressive forms of prostate cancer [7] . MSMB has therefore been a target of recent investigation into the mechanism of chromosome 10q-associated risk . Reporter assays demonstrate that plasmids carrying the rs10993994 risk allele ( T ) significantly decrease luciferase activity compared with the wild-type allele ( C ) [8] , [9] . In addition , in 19 cancer cell lines of various tissue types expressing MSMB , those carrying the TT genotype have decreased MSMB expression relative to those carrying a C allele [9] . However , no study has definitively linked MSMB expression to risk allele status in human prostate tissue . A second gene , nuclear receptor co-activator 4 ( NCOA4 , also known as ARA70 ) , is a ligand-dependent androgen receptor co-activator [10] , [11] and is within 16 kb telomeric of rs10993994 . Given its proximity to the risk variant and its activity in the prostate gland [12] , NCOA4 has also been considered a candidate gene involved in the mechanism of disease risk [8] . Gene expression is a heritable trait [13]–[16] and represents a powerful avenue for connecting risk variants with their target genes . Studies have demonstrated that variation at intergenic or intronic disease-associated loci can act through gene regulatory mechanisms [17]–[20] . Because regulatory elements can interact with many genes [21] , and since both MSMB and NCOA4 are strong candidates for prostate cancer risk , we evaluated the relationship between risk allele status and transcript abundance of these genes across both normal and tumor prostate tissues . We also tested the functional consequences of altering the expression levels of these candidate genes in immortalized prostate epithelial cells .
A total of 180 individuals of European ancestry were genotyped for the rs10993994 polymorphism , and MSMB and NCOA4 mRNA levels were quantified in tissue isolated from radical prostatectomy surgical specimens . Samples were derived from two cohorts- the Gelb Center at Dana-Farber Cancer Institute ( DFCI ) ( N = 121 – histologically normal and tumor prostate tissue ) and the Physicians’ Health Study ( PHS ) [22] ( N = 59 – prostate tumor tissue only ) . In the DFCI cohort , transcript levels were measured in both normal and tumor prostate tissue using a quantitative competitive PCR strategy ( Methods ) . Two MSMB and five NCOA4 isoforms annotated in the Ensembl database ( build 52 ) were evaluated ( Figure 1A ) . Each isoform of MSMB and NCOA4 was expressed in both normal and tumor prostatic tissue . Expression levels of MSMB and NCOA4 transcripts were significantly higher in normal compared with tumor tissue ( p<0 . 0001 ) , consistent with previously published reports [23]–[25] . In the PHS cohort , only tumor tissue was isolated from radical prostatectomy specimens . For this cohort the probes used to measure expression captured both MSMB isoforms and all but one of the NCOA4 isoforms . The T ( risk ) allele is significantly associated with transcript levels of both MSMB and NCOA4 in histologically normal prostate tissue ( N = 84 ) . While the T allele is associated with decreased expression of both MSMB isoforms ( p-value range , 0 . 0033–0 . 0042 ) , it is associated with increased expression of all five isoforms of NCOA4 ( p-value range , 6 . 7×10−7–0 . 0055 ) ( Figure 1B ) . In tumor tissue , MSMB retains its association with risk allele status ( p-value range , 0 . 016–0 . 053 , Figure 1C , Figure S1 ) . NCOA4 expression levels , however , are no longer associated with genotype in tumor tissue ( p>0 . 30 , Figure 1C , Figure S1 ) . The associations are specific to the genes in this region . Expression levels of TIMM23 , the next closest gene to the risk locus and 1 . 4 kb telomeric to NCOA4 , are not correlated with genotype status in either normal or tumor tissue . ( p>0 . 30 , Figure S2 ) . Because rs10993994 is not a risk allele in colon or breast cancers , we reasoned that the association between genotype and disease-relevant genes may be specific to prostate tissue and not observed in other tissue types . If an association between genotype and expression of MSMB or NCOA4 were observed in tissue other than prostate , then that gene may be less likely to be involved in prostate cancer risk . MSMB and NCOA4 expression levels were measured in histologically normal colon ( N = 72 ) and breast ( N = 56 ) tissue samples . While breast tissue expresses both genes , colon tissue only expresses NCOA4 . Unlike prostate tissue , neither breast nor colon tissue demonstrates convincing or consistent associations with genotype across isoforms ( Figure S3 ) . To evaluate the functional implications of the genetic findings , we tested the effect of increasing NCOA4 and suppressing MSMB expression levels in immortalized prostate epithelial cells ( LHSAR ) [26] . Specifically , we assessed the ability of NCOA4 and MSMB to promote anchorage-independent growth , a phenotype strongly associated with cell transformation [27]–[30] . Suppression of MSMB expression in LHSAR cells led to a significant increase in anchorage-independent colony growth ( p-values 0 . 0023–0 . 0001; Figure 2A , Figure S4 ) . Overexpression of NCOA4 in LHSAR cells also resulted in robust anchorage-independent colony growth ( p-value 0 . 0074; Figure 2B , Figure S4 ) . To assess whether these alterations were specific for prostate epithelial cells , similar functional studies were performed in immortalized human mammary epithelial cells [31] . In contrast to what was observed in prostate epithelial cells , manipulating expression levels of MSMB or NCOA4 did not result in any consistently significant change in the anchorage-independent growth in mammary epithelial cells ( Figure S5 ) . Together , these observations implicate a role for both NCOA4 and MSMB in the transformation of prostate epithelial cells .
Genetic data presented here demonstrate that the chromosome 10q11 prostate cancer risk locus is associated with decreased levels of MSMB and increased levels of NCOA4 RNA expression . Strikingly , the functional data fully corroborate the genetic data . When MSMB is knocked down or NCOA4 is overexpressed in immortalized prostate epithelial cells , the cells become anchorage independent . Our data suggest that both MSMB and NCOA4 mediate prostate tumorigenesis , and this study is the first , to our knowledge , to implicate these genes in actual human prostate tissue . As expected in a cohort comprised of subjects who underwent radical prostatectomy , a large majority of individuals included in the analysis were diagnosed with low- or intermediate-risk prostate cancer . Despite a relatively homogenous cohort , the results presented here are likely generalizable to most prostate cancer cases since rs10993994 appears to confer risk for all levels of prostate cancer aggressiveness [1] , [3] , [32] , [33] , [34] . Similar to the 10q11 risk allele , other disease risk loci have been shown to affect expression of more than one gene [17] , [34] . A variant associated with systemic lupus erythematous at chromosome 8p23 , for example , is associated with increased expression of one gene ( BLK ) and decreased expression of another ( C8orf13 ) in B cell lines [34] . As more genes underlying complex traits are discovered , it may be that certain risk alleles mediate their effects through multiple genes , or alternatively , that two risk variants in tight linkage disequilibrium influence separate genes . The findings at 10q11 highlight the importance of considering multiple genes when analyzing GWAS results . The findings at 10q11 also underscore the importance of evaluating risk loci in a tissue-specific context [35] . It is hypothesized that a fraction of non-protein coding risk alleles will alter disease risk by regulating gene activity , and these variants may exert their effects in a specific genetic and epigenetic context [21] , [36] . In the present study , the 10q11 risk variant is associated with transcript levels of MSMB and NCOA4 in primary prostate tissue . In contrast , no convincing or consistent association is observed in colon or breast tissue . Similarly , alteration of MSMB and NCOA4 expression significantly affects anchorage-independence of prostate but not breast epithelial cells . These findings may have implications for future studies attempting to connect risk alleles with target gene ( s ) . Evaluation of GWAS findings will focus , in part , on identifying the genes targeted by risk alleles , as these are the genes likely to drive the trait under study . Our findings suggest that this type of analysis should include evaluation of the tissue at risk for disease , although it is entirely plausible that variants associated with a particular disease may manifest their effects in tissues other than target tissue . Notably , associations between rs10993994 genotype and expression of MSMB and NCOA4 are observed in histologically normal prostate tissue , whereas in tumor tissue an association is detected with only MSMB ( albeit at an attenuated level relative to the normal tissue ) . It is conceivable that increased expression of NCOA4 is associated with tumor initiation , as reflected by its association with risk in solely normal tissue , while decreased expression of MSMB is associated with both tumor initiation and maintenance or progression . More studies , however , will be necessary before a general principle emerges . Cellular context also appears to be an important determinant in the functional analysis of candidate risk genes . This is illustrated by comparing data in the present study to previous work involving NCOA4 . The characteristics of two NCOA4 isoforms , alpha and beta , have been studied in functional assays . Upregulation of NCOA4beta increases anchorage-independent growth [11] , while overexpression of NCOA4alpha inhibits growth in LNCaP cells [37] . Functional analysis of immortalized prostate epithelial cells presented here demonstrates increased colony growth in the setting of an overexpressed alpha isoform ( Figure 2 ) . Distinctions between the cell lines used in these studies may account for these divergent results . LNCaP cells are derived from metastatic prostate lesions . Immortalized prostate epithelial cells , on the other hand , are not tumorigenic and differentiate in the presence of androgen [26] , suggesting that these cells are more closely related to normal prostate epithelial cells . The data presented here suggest that tissue type ( in this case , prostate versus non-prostate tissue ) and cellular states ( i . e . , normal versus tumor ) are likely important factors in the evaluation of complex trait loci . Chromatin context and differential use of gene regulatory elements across tissues and disease states may be the basis for expression effects specific to normal prostate tissue [36] . It can be difficult to accurately model these effects outside of the particular genetic and epigenetic context of specific tissues . Luciferase reporter assays , for example , are often utilized to define a relationship between a polymorphism and a gene . Reporter assays cannot , however , detect situations where a risk variant is associated with opposing transcriptional effects on two loci , as observed with rs10993994 . An alternative explanation for the effects on the two transcripts is that two separate variants in linkage disequilibrium are responsible for the different transcriptional effects . In contrast to Mendelian diseases , where resequencing protein-coding regions often reveals the causal gene , common complex trait alleles often occur outside of protein-coding regions . As is the case at 10q11 and other loci [38] , [39] , these alleles may be associated with expression of nearby and/or distal candidate genes . There are also situations in which associations with strong candidate genes , however , cannot be established by measuring steady-state expression at a single point in time [19] . In order to better understand the gene ( s ) involved in complex trait pathogenesis , experiments will need to integrate and to account for the genetic and epigenetic contexts of the particular tissue type and cellular state .
This study was conducted according to the principles expressed in the Declaration of Helsinki . The study was approved by the Institutional Review Board of Dana-Farber Cancer Institute . All patients provided written informed consent for the collection of samples and subsequent analysis . A total of 180 patients treated with radical prostatectomy ( RP ) for prostate cancer and 92 patients treated for colon cancer consented to provide tissue . Additionally , histologically normal breast tissue from 56 from women undergoing cosmetic reduction surgery was analyzed in the present study . Fresh frozen radical prostatectomy specimens were available from 121 subjects at the Dana-Farber Cancer Institute ( DFCI ) and Brigham and Women's Hospital ( Boston , MA ) and were reviewed by a pathologist ( J . C . or R . F . ) . Over 95% of the patients in the cohort were diagnosed with Gleason 6 or Gleason 7 disease and median PSA was 5 . 1 ng/ml . Areas of tumor consisted of >60% tumor cells and areas of benign tissue consisted of >80% non-neoplastic epithelial cells at least 5 mm away from any tumor focus . Biopsy cores of fresh frozen tissue were processed for RNA extraction using a modified Qiagen Allprep DNA/RNA protocol . Archival FFPE blocks were available for 59 men with prostate cancer enrolled in the Physicians’ Health Study ( PHS ) [22] , [40] . These men were diagnosed with prostate cancer between 1983 and 2003 and treated by radical prostatectomy . RNA were extracted from paraffin-embedded tumor tissue as described previously [40] . Areas of tumor consisted of >90% tumor cells . Fresh frozen colorectal cancer tissue samples were reviewed by a pathologist ( J . C . ) and areas of benign tissue were selected where 80% of cells consisted of non-neoplastic epithelium . RNA was extracted using a modified Qiagen Allprep DNA/RNA protocol . Fresh frozen normal breast tissue samples were reviewed to identify tissue blocks containing >40% normal epithelial cells . RNA was extracted using a modified Qiagen RNeasy protocol . Ethnicity was self-reported by most , but not all , subjects . Subjects in the DFCI cohort of unknown ancestry were genotyped for 59 ancestry-informative SNPs in order to ascertain ethnicity . The marker set primarily captured ancestral differences between European and African ancestries ( D . Reich , personal communication ) . Five samples found to be from subjects of African ancestry were excluded from analysis . The human tissues analyzed in this study were from patients treated at Brigham and Women’s Hospital , Dana-Farber Cancer Institute or Vall d'Hebron University Hospital in Barcelona , Spain , all of whom provided informed consent . The study was approved by the Institutional Review Board at Dana-Farber Cancer Institute . cDNA was prepared for expression analysis using Invitrogen SuperScript III Reverse Transcription kit . DFCI prostate samples , colon samples and breast samples were analyzed via competitive RT-PCR using Sequenom iPLEX matrix-assisted laser desorption/ionization ( MALDI ) -time of flight mass spectrometry technology . Expression levels of two MSMB isoforms and five NCOA4 isoforms were measured . These splice variants represent all isoforms reported in Ensembl genebuild 52 . RNA expression of TIMM23 and three housekeeping genes ( ACTB , MYL6 and RPL13A ) were also measured . Primer , probe and competitor oligo sequences are available upon request . Reactions were performed in quadruplicate using 8 serial dilutions of competitor , and the EC50 was calculated using QGE Analyzer software ( Sequenom ) . The PHS subgroup was analyzed using Illumina cDNA-mediated Annealing , Selection , Extension and Ligation ( DASL ) expression assay ( Illumina ) . A gene expression normalization factor was calculated using the geometric mean of expression level of the three housekeeping genes . Linear regression was used to assess whether expression levels increased ( or decreased ) as the number of T-alleles of rs10993994 increased; for prostate tissue , differences between prostate tumor and normal tissue levels were also assessed and random effects linear regression was used to account for within-sample correlation of tumor/normal pairs . Genotyping of DNA from each subject was carried out using Sequenom iPLEX matrix-assisted laser desorption/ionization ( MALDI ) -time of flight mass spectrometry technology . LHSAR: Prostate epithelial cells ( PrECs ) immortalized with hTERT , SV40 Large T and small t antigens and overexpressing androgen receptor were grown in PREGM ( Lonza CC-3166 ) . HMLE: Human mammary epithelial cells immortalized with hTERT , Large T and small t antigens were grown in MEGM ( Lonza CC-3150 ) . All growth media were supplemented with 100 ug/ml Penicillin/Streptomycin . The bottom layer contained 0 . 6% agar ( Sigma A5431 ) in DMEM and 8% FBS . The top layer contained 0 . 3% agar in PREGM or MEGM for LHSAR or HMLE respectively . Fifteen thousand cells were seeded in the top agar layer in triplicate wells of a 6 well plate . Colonies were counted from 2 to 6 weeks post-seeding . Image of each well was taken at a 6× magnification and analyzed with Image J software . Colonies that were 50 sq . pixels or larger were counted . Qiagen RNeasy kit was used for RNA extraction . Reverse transcription was carried out with Clonetech Advantage RT-to-PCR kit while the quantitative PCR was carried out using SYBR Green Master Mix ( Applied Biosystems ) . Two sets of NCOA4 and one set of MSMB primers were used: NCOA4 expression constructs were received from the human ORFeome V5 . 1 and cloned into pWZL-Neo retroviral expression vector . Retrovirus production , infection and selection were carried out as described previously [41] . Short hairpins in pLKO . 1 lentiviral constructs were received from the RNAi Consortium ( TRC ) . Lentivirus production and infection were carried out as described previously [42] .
|
Family history has long been recognized as an important risk factor for prostate cancer . Beginning in 2006 , researchers have identified several genetic variants that are associated with prostate cancer risk . Intriguingly , the majority of prostate cancer risk variants do not reside in genes . Determining the genes involved in the development of disease , therefore , has proved challenging . In this study we interrogate a known prostate cancer risk polymorphism on chromosome 10—rs10993994 . We report that this variant is significantly associated with the RNA expression levels of two genes—MSMB and NCOA4 . When these expression changes are modeled in a cell line , prostate cells that were previously non-tumorigenic acquire a property known as anchorage independence , a characteristic of cancer cells . Notably , the prostate risk variant is not associated with expression or functional changes in breast or colon cells . In addition , the effects are most pronounced in normal rather than tumor prostate tissue . Overall , these findings help define the genes driving prostate cancer risk at chromosome 10 . More generally , the discoveries demonstrate the importance of considering several target genes , as well as the importance of cellular context ( tissue type and histological state ) , in future analyses of other genetic risk regions .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"genetics",
"and",
"genomics/genetics",
"of",
"disease",
"oncology/prostate",
"cancer",
"genetics",
"and",
"genomics/gene",
"expression"
] |
2010
|
Analysis of the 10q11 Cancer Risk Locus Implicates MSMB and NCOA4 in Human Prostate Tumorigenesis
|
Over 200 , 000 new cases of leprosy are detected each year , of which approximately 7% are associated with grade-2 disabilities ( G2Ds ) . For achieving leprosy elimination , one of the main challenges will be targeting higher risk groups within endemic communities . Nevertheless , the socioeconomic risk markers of leprosy remain poorly understood . To address this gap we systematically reviewed MEDLINE/PubMed , Embase , LILACS and Web of Science for original articles investigating the social determinants of leprosy in countries with > 1000 cases/year in at least five years between 2006 and 2016 . Cohort , case-control , cross-sectional , and ecological studies were eligible for inclusion; qualitative studies , case reports , and reviews were excluded . Out of 1 , 534 non-duplicate records , 96 full-text articles were reviewed , and 39 met inclusion criteria . 17 were included in random-effects meta-analyses for sex , occupation , food shortage , household contact , crowding , and lack of clean ( i . e . , treated ) water . The majority of studies were conducted in Brazil , India , or Bangladesh while none were undertaken in low-income countries . Descriptive synthesis indicated that increased age , poor sanitary and socioeconomic conditions , lower level of education , and food-insecurity are risk markers for leprosy . Additionally , in pooled estimates , leprosy was associated with being male ( RR = 1 . 33 , 95% CI = 1 . 06–1 . 67 ) , performing manual labor ( RR = 2 . 15 , 95% CI = 0 . 97–4 . 74 ) , suffering from food shortage in the past ( RR = 1 . 39 , 95% CI = 1 . 05–1 . 85 ) , being a household contact of a leprosy patient ( RR = 3 . 40 , 95% CI = 2 . 24–5 . 18 ) , and living in a crowded household ( ≥5 per household ) ( RR = 1 . 38 , 95% CI = 1 . 14–1 . 67 ) . Lack of clean water did not appear to be a risk marker of leprosy ( RR = 0 . 94 , 95% CI = 0 . 65–1 . 35 ) . Additionally , ecological studies provided evidence that lower inequality , better human development , increased healthcare coverage , and cash transfer programs are linked with lower leprosy risks . These findings point to a consistent relationship between leprosy and unfavorable economic circumstances and , thereby , underscore the pressing need of leprosy control policies to target socially vulnerable groups in high-burden countries .
Leprosy , a chronic infectious disease caused by Mycobacterium leprae , remains endemic in 13 low and middle-income countries worldwide [1] . While effective and affordable multidrug therapies have the potential to cure infections , failures in detection and treatment can lead to the development of stigmatizing leprosy-associated grade-2 disabilities ( G2Ds ) [1 , 2] . By recent estimates , 7% of the more than 200 , 000 new cases of leprosy detected each year occur in individuals who have already developed G2Ds by the time of diagnosis . To reduce the incidence of infection and prevent the onset of new G2Ds , the World Health Organization has advocated for targeted detection and intervention among higher risk groups within endemic countries [1 , 3] . However , defining and intervening with the target groups at a subnational level remains a challenge due to a lack of understanding regarding the epidemiological risk markers of leprosy . In recent years , there has been an increased recognition of the social determinants of health and of the potential of social interventions to enhance disease treatment and control strategies [4] . In the case of leprosy , existing evidence suggests that poor living conditions may be associated with increased risk , while the discrimination and fears associated with leprosy may lead to treatment delays , G2Ds , and decreases in individual economic productivity , thereby perpetuating poverty [5] . Recognizing this bidirectional association , several countries have made efforts to break the link between poverty and leprosy by incorporating poverty reduction efforts as a major component in health policies promoting leprosy control [6] . To better inform these health policies and to address residual gaps in knowledge related to the markers of leprosy risk , this systematic review aims to collate and appraise the published evidence on the effect of social , demographic , and economic factors and leprosy occurrence in high-burden settings .
The protocol for the systematic review has been registered in the International Prospective Register of Systematic Reviews ( PROSPERO ) as CRD42016051212 [7] . To identify studies reporting associations between socioeconomic variables and leprosy outcomes in high-burden countries , we searched MEDLINE , Embase , LILACS , and Web of Science up to 20th January 2017 using the strategy detailed in S1 Text and reviewed reference lists for additional relevant articles . No language restrictions were applied to the search; however , full text review was limited to articles published in English , Spanish , Portuguese , and French . Studies were eligible for inclusion if they: ( i ) were carried out in one of the 20 high-burden countries ( i . e . , defined as officially reporting more than 1 , 000 cases per year in at least five consecutive or non-consecutive years between 2006 and 2016 ( Fig 1 ) [8 , 9]; ( ii ) had a cohort , case-control , cross-sectional , or ecological study design; ( iii ) measured associations between one or more socioeconomic variables ( i . e . , age , sex , urban/rural residence , housing conditions/crowding , education/occupation , and social deprivation ) and diagnosed leprosy disease . Studies were excluded if they: ( i ) had a qualitative or review design , ( ii ) exclusively used Phenolic Glycolipid I ( PGL-1 ) positivity as a biomarker of leprosy exposure [10] , ( iii ) lacked a clear description of the study population , or ( iv ) exclusively analyzed sex and/or age as the sociodemographic variables . Four reviewers ( J . M . P , A . S . , K . A . , and L . M . S . ) worked in duplicate to appraise records , evaluate study quality using the Newcastle-Ottawa scale ( NOS ) for individual level studies [11] , and extract data using a standardized form ( S1 Table ) . We used the NOS form for cohorts to evaluate data quality for cross-sectional studies; however the quality score was limited to a maximum of 7 points as it was not possible to demonstrate that leprosy was not present at the start of the study and due to the lack of follow up . Specifically , the reviewers extracted data related to the study protocol ( i . e . , geographic location , baseline survey dates , study design , study population , number of participants , method of leprosy ascertainment , and number of leprosy cases ) and the measure of association ( i . e . , socioeconomic characteristics of leprosy cases and the comparison group , effect sizes , and statistical adjustment for potential confounders ) . Discrepancies were resolved by consensus . Individual level studies with data on different comparison groups ( i . e . , both cohort and case-controls in the same study ) were considered in only one study , but data were extracted for all groups . Methods and results are reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses ( PRISMA ) guidelines ( for checklist , see S2 Table ) [12] . The studies included in this review were summarized in two groups defined by whether the risk markers and leprosy outcomes were evaluated in individuals or at a population level . When estimates for a given risk marker was reported in at least three individualized studies , we estimated summary relative risks ( RR ) and its 95% Confidence Intervals ( 95% CI ) by pooling effect sizes using random-effects meta-analyses . As leprosy is a rare disease , odds ratios and hazard ratios were assumed to approximate the same RR [13] . Studies conducted only among household contacts of leprosy patients or those with insufficient information to calculate the point estimates and its 95% CIs were not included in the meta-analysis . We assessed heterogeneity in RR estimates using I2 statistics and Cochran’s Q test p-values . Data analysis was performed in Stata , version 15 . 0 , and R , version 3 . 4 . 0 .
Sex and/or age were investigated and/or adjusted for in 17 studies , including five cohorts [14 , 16–18 , 32] , four case-controls [23 , 24 , 26 , 27] , and eight cross-sectional studies [29 , 32–36 , 38 , 40] . Six out of 17 studies considered sex as a confounder in adjusted models , seven out of 13 considered age in the adjusted model , and five included both [20 , 23 , 26 , 27 , 33] . Fourteen studies analyzed the sex or age of the exposed and unexposed populations directly , one cross-sectional study examined the sex and age of family head [32] , one cohort study evaluated the sex and age of the both the index patient and their contact [20] , and one case-control study included sex and age only for adjustment without providing point estimates [26] . Out of 16 studies that investigated the association of leprosy with sex , four reported a higher prevalence of leprosy among males [14 , 16 , 17 , 29] , of which only one provided adjusted estimates . One study reported that contacts of male patients had higher leprosy incidence [20] , and the others did not report differences between males and females . Eleven studies were included in the meta-analysis of the association between male sex and leprosy . The crude overall RR for male sex was 1 . 33 ( 95% CI: 1 . 06 , 1 . 67 ) , with a substantial heterogeneity between the studies ( I2 = 64 . 2% ) ( Fig 3 ) . The effect decreased along the study years . The association between age and leprosy was assessed in 13 studies , of which six found a positive association with increasing age [18 , 24 , 32 , 34 , 36] . The association between education and leprosy was evaluated in one cohort [20] , three case-controls [23 , 24 , 26] , and four cross-sectional studies [32–34 , 40] . Different categorizations for education included family literacy [26] , having formal education [33] and level of schooling [20 , 23 , 24 , 32 , 34 , 40] . Three out of eight studies pointed to a higher number of leprosy cases among less educated individuals [23 , 32 , 33] , and the associations remained significant after controlling for confounders ( Table 3 ) . In the study by Sales and colleagues , the educational level of the index patient was negatively associated with other prevalent leprosy cases within the family , but not among incident cases [20] . Andrade and colleagues ( 1994 ) suggested that a lower level of education was associated with higher leprosy incidence among neighbours , but not among other random groups [32] . Occupation status was analyzed in two case-controls studies [23 , 27] and two cross-sectional studies [33 , 40] , most commonly by separating manual workers ( e . g . , factory , construction , or agriculture workers ) , from non-manual workers ( e . g . , traders or office workers ) [23 , 27 , 33 , 40]; unemployment as risk factor was also studied [40] . In the four studies included in the meta-analysis for occupation , there was a positive , but not statistically significant , association between leprosy and manual labor ( RR = 2 . 15 , 95% CI = 0 . 97–4 . 74; I2 = 92 . 6% ) ( Fig 3 ) . The relationship between income and leprosy was assessed in one cohort [20] , four case-controls [23 , 24 , 26 , 27] , and four cross-sectional studies [28 , 29 , 31 , 34] using per capita household income [20 , 26–29 , 31] or socioeconomic position defined by self-assessment [27] , assets score [24] or social score [34] ) . Three studies reported statistically significant associations between poverty and leprosy in univariate analysis [20 , 27 , 29] , but the associations attenuated after adjusting for potential mediators , such as age , sex or occupation . Poverty measures differed among the studies , making a meta-analysis not appropriate; however , the direction of the association was consistent across studies , providing evidence of an inverse association between socioeconomic position and leprosy risk . Factors related to food insecurity , an established correlate of poverty [53] , were studied as a risk factor for leprosy in three case-control studies , two of which were carried out in Bangladesh [24 , 27] and one in Brazil [23] . Food shortage in the past year was assessed twice [24 , 27] , ever food-shortage three times [23 , 24 , 27] , and food expenditure , score of food insecurity ( Household Food Insecurity Access Scale , HFIAS ) , Dietary Diversity Score ( DDS ) , and household food stocks were evaluated once each [27] . Low food diversity and low stocks of food were not associated with increased number of leprosy cases , while food expenditure and HFIAS were negatively associated with leprosy [27] . In the meta-analysis , ever food-shortage was significantly associated with higher leprosy risks ( RR = 1 . 39 , 95% CI = 1 . 05–1 . 85; I2 = 29 . 3% ) ( Fig 3 ) . Sharing a household with a current leprosy case was strongly associated with risk of developing the disease in all nine studies that investigated this factor ( five cohorts [14–18] , three case-controls [21 , 25 , 26] , and one cross-sectional study [40] ) . One study conducted by Feenstra and colleagues , which used a score of social interaction with a leprosy patient ( i . e . , in the household , within the neighborhood , and outside the neighborhood ) , found that contacts in the household and within the neighborhood shared similar risks of leprosy [25] . The meta-analysis of the other eight studies estimated a crude RR of 3 . 40 ( 95% CI = 2 . 24–5 . 18 ) associated with household sharing , with a substantial heterogeneity ( I2 = 95 . 9% ) ( Fig 3 ) . Six studies also evaluated the association between being a household or familial contact of a leprosy patient as opposed to any other type of contact , and all found that household or familial contacts had higher risk of leprosy than general contacts [16 , 20 , 22 , 36 , 37 , 39] . Household conditions were assessed in six studies , including three case-control and three cross-sectional studies , as house ownership [27] , habitation type ( i . e . , private accommodation ) [32] , house size ( i . e . , in square meters and number of rooms ) [24 , 27 , 32] , and building or floor material [23 , 31–33] . Neither owning the house [27] , residing in private accommodation [32] , nor house size [27] were significantly associated with leprosy after adjusting for factors such as education , work and household food stocks [27 , 32] . Only one of the four studies looking at building materials found an association in univariate analysis between poorer building material ( i . e . , floor or house walls made of materials different than cement/bricks ) and leprosy [31] . Crowding was measured as the number of residents in the household in four studies [17 , 20 , 32 , 40] and residents per room in three studies [23 , 24 , 34] . Although only one individual study found evidence that crowding was significantly associated with higher leprosy risks [17] , the pooled RR provides evidence that crowding , ( i . e . , ≥ five individuals living in the same household or ≥ four individuals sharing the same bedroom ) may be a significant risk marker for leprosy ( RR = 1 . 32 , 95% CI = 1 . 13–1 . 53; I2 = 0 . 0% ) ( Fig 3 ) . Of note , Kerr-Pontes and colleagues did not find an association between bed sharing and higher risk of leprosy [23] . Water and sanitation were investigated in one case-control [23] and in five cross-sectional studies [26 , 29 , 32 , 34 , 35] . Specifically , household access to clean water was assessed in three studies [23 , 32 , 34] , waste collection in one [26] , sanitation ( sewage system or the presence of a sanitary facility in the house ) in three studies , [23 , 29 , 35] and socio-sanitary score based on type of water supply and crowding in one [29] . Of the three studies investigating access to clean water , only the report by Andrade and colleagues found an association between clean water and a lower incidence of leprosy in adjusted estimates , when comparing households with leprosy with a random household , but not with a neighbouring household [32] . The presence of waste collection services [26] and good sanitary conditions score were associated with a lower prevalence of leprosy [29] . Cleanliness habits ( e . g . , sweeping the house , high frequency of changing bed linen ) [23 , 32] and household cleanliness ( i . e . , living in a dirty household or surroundings ) [33 , 35] were assessed in four studies , of which three found a negative association between cleanliness and leprosy [23 , 33 , 35] . Pooled statistics were calculated for lack of clean water in the household in three studies , including one with two comparisons group ( RR = 0 . 94; 95% CI = 0 . 65 , 1 . 35; I2 = 62 . 5% ) ( Fig 3 ) and provided no evidence that clean water correlates with lower leprosy incidence . The studies at the individual level investigated a range of other sociodemographic factors , including ethnic background , marital status , religion , urbanization , and migration status , but the overall evidence was limited . For example , in the one case-control study that examined ethnicity and marriage as correlates of leprosy , the authors report no difference between white and black/brown or unmarried and married individuals [23] . The relationship between religion and leprosy was evaluated in three studies , one held in Bangladesh [27] and two in India [31 , 33] , with higher leprosy prevalence among Muslims reported in one [31] . In addition , of the three studies evaluating urbanicity and leprosy [29 , 30 , 38] , two found that individuals living in urban ( versus rural areas ) [38] or in rural villages ( versus the rural surrounding areas ) have lower leprosy prevalence [30] . The distance from the household to health clinics , which can also be a measure of urbanization in mixed rural/urban areas , was evaluated by Fisher and colleagues ( 2008 ) in Bangladesh , but no relationship was found between leprosy detection rate and proximity to a clinic [19] . Recent migration ( i . e . , in the past 5 years ) was evaluated once and was positively associated with leprosy [26] . Ecological studies provide an important line of evidence on the relationship between socioeconomic and demographic factors and leprosy ( Tables 2 and 4 ) . Associations of leprosy with increased urbanization [41 , 45 , 47–50] , illiteracy/lower education [30 , 41 , 48–51] and unemployment [49–51] were consistently reported at the ecological level . Regions with a higher percentage of households with access to clean water [41 , 50 , 52] , waste collection services [50 , 51] , or sanitation ( i . e . , a sewage system or a sanitary facility ) [48 , 50–52] reported a lower number of leprosy cases in the all but one of the studies [44 , 48 , 50 , 52] . The mean number of individuals per household or per room was considered in seven studies [41 , 46–50 , 52] , five of which found it positively associated with leprosy [46–49 , 52] . Socioeconomic deprivation was measured as the percentage of people living in poverty or extreme poverty ( i . e . , according to a predefined threshold ) [30 , 41 , 49–51] , scores indicating poverty , socioeconomic groups , and social status ( including deprivation ) [43–45] . Half of these studies found a correlation between having better living conditions and lower leprosy burden [43–45 , 49] . Migration , evaluated as the percentage of people born in other regions , was positively associated with leprosy [47] . Ecological studies also provided evidence of a correlation between malnutrition and leprosy among children [30 , 51] . Ecological evidence also suggests that , in general , indicators of social development and policy interventions were negatively associated with leprosy burden . Inequality was measured using Gini Index or Theil’s L index in four studies [41 , 47–49] and as income ratio between the richest 20% and the poorest 20% ( 20–20 Income Ratio ) in one study [48] . Human Development Index ( HDI ) was assessed in another study [42] . Overall , the studies provided strong and consistent evidence of an association between increased inequality and/or lower socioeconomic development and higher leprosy risks [41 , 42 , 47–49] . On the other hand , the presence of specific campaigns and health services for leprosy detection were associated with higher leprosy incidence rates , potentially by enhancing the leprosy detection efficiency [50] . While higher coverage of primary health care in Brazil was associated with higher leprosy new case detection in two studies [48 , 49] , no associations with leprosy were found using other metrics for health care access , including: the number of general public health services [41] , number of physicians per 1 , 000 inhabitants [41] , vaccination coverage [51] and infant mortality rates [41] . In Brazil , an analysis of the impact of a conditional cash transfer program showed that increased coverage of the program benefits was associated with a reduction in leprosy new case detection rates [49] .
This systematic review points to a consistent relationship between leprosy and unfavorable socioeconomic circumstances . For individual level studies , meta-analyses provide evidence for increased risks of leprosy in individuals who are male , share homes with leprosy cases , live in crowded conditions , and have experienced food shortages in the past . In ecological level studies , point estimates for the associations between leprosy and sociodemographic risk markers of crowding , sanitation , and poverty remained largely consistent with individual level studies and across different geographic settings . Overall , males had a greater risk of leprosy . However , the effect diminished in studies that are more recent; the pattern is potentially attributable to higher detection of leprosy among women over time and/or to change in exposure level of different risk markers in men and women . In most studies , literacy and high levels of education were associated with lower leprosy rates , although pooled estimates for education were not possible due to incomparable categories . Better education , in both sexes , can increase health knowledge and healthy behaviors , foster access to better work conditions and resources and promote greater autonomy [54] , which could potentially reduce leprosy infection and transmission . The type of work performed by an individual reflects their socioeconomic status and conditions and can vary across time and both within and between countries , especially in large and multicultural ones ( e . g . , India and Brazil ) . Pooled estimates between work and leprosy showed high statistical heterogeneity across the different studies , which might suggest that performing manual or agriculture work might correspond with different levels of poverty and living conditions in the different study settings ( e . g . , India , Brazil , Bangladesh or Sri Lanka ) , resulting in differences in the levels of exposure to M . leprae or chances of developing symptomatic disease . Food shortage , an indicator of extreme poverty and undernourishment [27] also appeared to be a risk marker of leprosy . Food-shortage was assessed in places where seasonality can influence work , income , food prices , consequently reducing dietary diversity [23 , 24 , 27] . More studies are needed about other possible risk markers of poverty and education inequalities , such as ethnicity [55 , 56] , which was assessed only once [23] . Person-to-person contact inside the household is one of the most likely sources for leprosy transmission [57]; nevertheless , similarities of social , sanitary , and poverty conditions shared by families and neighbors , which can contribute to leprosy transmission , are poorly taken into account . The higher leprosy prevalence among crowded households in the meta-analysis support the hypothesis that crowding can both facilitate transmission and also be a general indicator of poverty . Additionally , the association between religion and higher risk of leprosy in the study of Chaturvedi ( 1988 ) was mainly attributed to increased household crowding in some religious group [31] , which also corroborates the idea that crowding may be associated with infection and/or disease development . Most studies characterized the study setting as rural or urban areas , but only ecological studies showed consistent correlations between urbanization and higher leprosy rates . Studies performed at the individual level , showed that household characteristics and basic socio-sanitary conditions were strongly related with leprosy burden . In 2015 , only 58% of the global population had access to clean water and 68% to adequate sanitation , with marked inequalities between rural/urban and rich/poor areas , including many high-burden countries for leprosy [58] . The absence of association between lack of access to clean water and leprosy in the meta-analysis might derive from high heterogeneity among the living conditions of those affected . Migration from a relatively higher-burden setting is an important risk factor for infectious diseases transmission and reactivation in lower-burden settings ( e . g . , as has been previously demonstrated for tuberculosis ) [59 , 60] . This result differs from the two studies that evaluated migration history as a potential risk factor for leprosy . Nevertheless , the origin of migrants or the incidence/prevalence in their country or region of origin was not described . The point estimates for the association between the socioeconomic or demographic characteristics ( i . e . , crowding , sanitation , and poverty ) and leprosy in both individualized and ecological studies followed the same direction , suggesting no ecological fallacy and strengthening the association between these risk markers and leprosy . Nevertheless , it is important to mention that few studies reported the potential for reverse causality in both cross-sectional and ecological investigations ( e . g . , leprosy → unemployment ) . Freitas and colleagues ( 2014 ) suggested that higher detection rates of leprosy in municipalities with greater Family Health Program coverage can also be attributed to preferential targeting of municipalities by their leprosy rates [48] . Also , there is a possible link between leprosy-associated stigma and loss of employment , which could further worsen living conditions . Some limitations of this systematic review include , first , the generalizability of the ecological findings as only one investigation was conducted outside of Brazil . Second , the findings presented here originate from studies carried out only in lower middle- and upper-middle economies , as we could not locate any relevant study carried out in a low-income country; the findings , although plausible , may be less applicable to low-income countries . Third , although we included a large number of social , demographic , and environmental factors as potential descriptors in the search strategy , some rare factors linked with leprosy burden might have missed . We selected all high burden countries for leprosy since 2001 , but endemic countries facing civil war in the last 10 years might not have been included in WHO statistics or , by consequence , in this review . Fourth , heterogeneity of social/cultural/economic structures between countries and within large countries such as Brazil and India prevented us from combining characteristics such as education in the meta-analysis . Fifth , although the majority of studies were published in the 21st century , the high-burden countries have experienced substantial economic growth in the past two decades , which has the potential to limit the generalizability of the meta-analysis estimates . Also , economic growth occurred in the past two decades , in which the majority of these studies have taken place could have contributed to higher heterogeneity in the effects between the studied social markers and leprosy . Despite these limitations , this review aggregated sparse evidence from diverse study settings , showing consistent associations between social determinants and leprosy across studies . Future research should prioritize investigations in low-income countries , address other markers of poverty ( e . g . , ethnicity , rural to urban migrants ) , explore heterogeneity between and within countries , and investigate the impact of recent poverty reduction programs . Leprosy has been gradually included in the portfolio of diseases associated with poverty and in countries , like Brazil , has been incorporated into social programs [61] . For instance , high leprosy burden was accounted for in the prioritization of Brazilian municipalities in social protection programs , such as “Plano Brasil sem Miséria” [6] . Despite these advances , the options for combining curative approaches with prevention efforts particularly designed to address social determinants have not been fully considered in the context of leprosy control programs in many countries . Social determinants of leprosy have been poorly studied to date and need to be particularly addressed in those countries where leprosy incidence is still high and human development remains low . In agreement with the WHO Global Leprosy Strategy 2016–2020 , which recommends the increase of inter-sectoral collaboration to further reduce the global and local leprosy burden , this review provides additional evidence that elimination of leprosy at the international level requires reduction of social inequalities , improving access of adequate housing and sanitation conditions and targeting social vulnerable groups and communities . In conclusion , this study underscores the many ways that poverty can create conditions that perpetuate leprosy risk . In addition , these findings call attention to persistent gaps in knowledge of the associations between leprosy and socioeconomic risk markers and highlight a lack of studies conducted in low-income countries . Thus , political commitment must prioritize investments in not only the diagnosis of leprosy , but also in research on the social determinants of this ancient disease , and in the integration of leprosy-specific programs into social policies aiming to eradicate poverty .
|
Many cases of leprosy still occur in low and middle-income countries , with a considerable proportion of them leading to permanent nerve damage and visible physical deformities . Disease elimination can be achieved with a better understanding of the sociodemographic characteristics of those most affected by the disease and by targeting those with greater risk within endemic countries . To address this question , we reviewed all published studies evaluating the social determinants of leprosy in countries endemic for leprosy . We found 39 studies , most of them conducted in Brazil ( i . e . , an upper-middle-income country ) , India or Bangladesh ( i . e . , lower-middle income countries ) , and none in low-income countries . Our review found strong evidence that males , household contacts of leprosy patients , individuals living in crowded households , and individuals who suffered food shortage in the past are more affected by leprosy . Evidence also exists that increasing age , poor sanitary and socioeconomic conditions , lower levels of education , and food insecurity are associated with a greater risk of leprosy . Our review underscores the importance of improving living conditions and decreasing inequality in low and middle-income countries to achieve leprosy elimination .
|
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2018
|
Socioeconomic risk markers of leprosy in high-burden countries: A systematic review and meta-analysis
|
Many pathogenic bacteria cause local infections but occasionally invade into the blood stream , often with fatal outcome . Very little is known about the mechanism underlying the switch from local to invasive infection . In the case of Neisseria gonorrhoeae , phase variable type 4 pili ( T4P ) stabilize local infection by mediating microcolony formation and inducing anti-invasive signals . Outer membrane porin PorBIA , in contrast , is associated with disseminated infection and facilitates the efficient invasion of gonococci into host cells . Here we demonstrate that loss of pili by natural pilus phase variation is a prerequisite for the transition from local to invasive infection . Unexpectedly , both T4P-mediated inhibition of invasion and PorBIA-triggered invasion utilize membrane rafts and signaling pathways that depend on caveolin-1-Y14 phosphorylation ( Cav1-pY14 ) . We identified p85 regulatory subunit of PI3 kinase ( PI3K ) and phospholipase Cγ1 as new , exclusive and essential interaction partners for Cav1-pY14 in the course of PorBIA-induced invasion . Active PI3K induces the uptake of gonococci via a new invasion pathway involving protein kinase D1 . Our data describe a novel route of bacterial entry into epithelial cells and offer the first mechanistic insight into the switch from local to invasive gonococcal infection .
The human-specific Gram-negative bacterium Neisseria gonorrhoeae is the cause of the sexually-transmitted disease gonorrhea . With more than 106 million infections per year ( source: WHO ) it presents a serious threat to world health . Moreover , the current global dramatic spread of multi-resistant gonococci and the predicted impact of untreatable gonorrhea on HIV transmission [1] has alarmed the WHO and initiated the release of a global action plan in 2012 to control the spread and impact of multi-resistant gonococci ( http://www . who . int/en ) . Besides causing local infections , gonococci may also spread within the host . These systemic disseminated gonococcal infections ( DGI ) lead to serious conditions such as dermatitis , sepsis , endocarditis , and arthritis [2] , [3] . N . gonorrhoeae strains possess the ability to form type IV pili ( T4P ) , which establish an initial contact and adherence to the host cell . Subsequently , gonococci utilize Opacity-associated ( opa ) proteins to intimately bind to and invade into host cells [4] , [5] , [6] , [7] . Within the 11 members of the Opa protein family , the Opa50 protein binds to heparan sulfate proteoglycans ( HSPG ) [8] , [9] or fibronectin and integrins [10] whereas all other Opa proteins ( Opa51-60 ) target members of the carcinoembryonic antigen-related cellular adhesion molecules ( CEACAM; for review see [11] ) . Another route to enter primary cervical epithelial cells requires the cooperative binding of the major outer membrane protein PorB , pili and lipooligosaccharide to the complement receptor type 3 [12] , [13] . Finally , entry of N . gonorrhoeae into non-professional phagocytes is mediated by PorB subtype A ( PorBIA ) . By contrast , the closely related subtype B ( PorBIB ) does not confer internalization [10] , [14] . This invasion mechanism is phosphate-sensitive and independent of pili and Opa-proteins . We recently resolved the structure of PorBIA and identified Arg/His92 as critical for phosphate binding and invasion [15] . Exchange of Arg/His92 highly conserved in PorBIA from DGI strains for Ser , the respective amino acid found invariantly in all PorBIB subtypes , leads to the loss of the invasive phenotype via this otherwise fully functional porin [15] . This high degree of structural conservation of invasive versus non-invasive PorB from strains associated with disseminated versus local infection , respectively , support a role of PorBIA in DGI . Low-phosphate conditions are found in the blood stream and may thus allow the unmasking of a receptor-interacting region in PorBIA [15] . While strains expressing PorBIB can occasionally disseminate , PorBIA-expressing gonococci are clearly overrepresented in systemic infections ( 20% in all versus 80% in DGI strains ) [16] , [17] , [18] . Our statistical analysis revealed a highly significant correlation of the presence of PorBIA with disseminated gonococcal infection in different countries ( see Data Set S1 ) . Although also other mechanism like serum resistance are of high importance during DGI [19] , [20] , the PorBIA-dependent invasion mechanism is suggested to be highly clinically relevant as a means to initiate invasive gonococcal diseases . We have previously shown that gonococci engage the scavenger receptor expressed by endothelial cells ( SREC-I ) to invade epithelial cells in a PorBIA-dependent manner [21] . Scavenger receptors ( SR ) represent a heterogenic group of membrane receptors belonging to the pattern recognition receptors . SREC-I , like other scavenger receptors , has been defined as receptor for modified lipoproteins including oxidized and acetylated low-density lipoprotein ( LDL ) but it recognizes also other proteins like Gp96 , calreticulin and heat shock protein 90 ( HSP90 ) that are no lipoproteins . The involvement of SREC-I in PorBIA-dependent neisserial host cell invasion is thus far the only example of a bacterial pathogen exploiting SREC-I . The signaling cascade leading to PorBIA-triggered bacterial uptake into epithelial cells involves Rho GTPases and actin but , in contrast to Opa-dependent invasion , not microtubules , acidic sphingomyelinase , myosin light chain kinase , and Src-kinases [14] . Very little is known about the cellular signaling underlying the switch from local to disseminated infection . Our recent data demonstrate that Vav2- and RhoA-dependent accumulation of actin at membrane rafts actively block invasion of piliated gonococci [22] . Here , we investigated the mechanism of the clinically important SREC-I/PorBIA-dependent invasion of gonococci under low phosphate conditions . To our surprise , the signaling that initiates invasion and anti-invasion of gonococci expressing PorBIA and pili both depend on the formation of membrane rafts and caveolin-1 phosphorylation . We identified the p85 regulatory subunit of PI3K/Akt as a new and critical interaction partner of phosphorylated caveolin-1 that is recruited to membrane rafts in a SREC-I-dependent manner during PorBIA-dependent invasion . This interaction leads to the activation of a novel bacterial invasion pathway involving the serine threonine kinase D1 ( PKD1/PKCμ ) . Thus , components identified in the present study might aid in identification of novel drug targets for invasive gonococcal diseases .
We previously demonstrated that PorBIA mediates invasion of gonococci independent of Opa adhesins and T4P by interacting with the SREC-I receptor [14] , [21] . Expression of SREC-I in naturally SREC-I-deficient Chinese hamster ovary ( CHO ) cells reconstitutes invasion of strain MS11 N927 ( PorBIA ) under low-phosphate conditions , whereas gonococcal strains expressing PorBIB are not internalized . Since internalization signals for scavenger receptors have not been identified so far , we speculated that the 388 amino acids long SREC-I cytoplasmic domain ( CD ) and phosphorylation of amino acid residues might play a role in signal transduction processes leading to the engulfment of PorBIA-expressing gonococci . Because kinase inhibitor studies revealed a role of the tyrosine kinase Abl1 in PorBIA-dependent invasion ( see below ) , we first searched for relevant motifs in the CD domain of SREC-I . A computational analysis using NetworkKIN ( http://networkin . info ) identified Tyr818 ( consensus sequence RXXEXXY818 ) as a potential Abl1 phosphorylation site . We generated different SREC-I expression constructs fused to GFP ( SREC-I-GFP ) for the transient expression in CHO cells . Surface exposition of SREC-I wt and truncated SREC-I constructs was similar as demonstrated by FACS-analysis ( Fig . S1A ) . Intracellular bacteria associated with the transfected cells were then quantified by immunofluorescence staining . A Y818A mutant of SREC-I-GFP was similarly efficient as the wildtype derivative in mediating invasion of strain N927 ( PorBIA , Opa− , P− ) into CHO cells in the absence of phosphate ( Fig . 1A , B ) , suggesting that Y818 phosphorylation is not required for PorBIA-mediated uptake of N927 . We then generated a SREC-I mutant lacking the entire cytoplasmic domain of SREC-I ranging from amino acids 453 to 830 ( SREC-IΔAA453-830; SRECIΔCD ) . To our surprise , invasion was not prevented in CHO cells expressing SREC-IΔCD ( Fig . 1A , B ) . Thus we conclude that the cytoplasmic domain of SREC-I is not required for PorBIA-mediated uptake of gonococci into epithelial cells . Receptors may transmit extracellular signals independent of their CD by associating with ordered assemblies of proteins and lipids called membrane rafts [23] . To test whether the association with membrane rafts is required for gonococcal invasion , CHO SREC-I and CHO SREC-IΔCD transfected cells were treated with the membrane raft-disrupting agent nystatin before infection with N927 ( PorBIA , P− , Opa− ) . Nystatin pretreatment led to a drastic ( Fig . 1C ) and dose-dependent ( Fig . S2B ) reduction of invasion compared to untreated control cells . These results were confirmed in Chang conjunctiva cells ( Fig . S2A ) . A similar strong inhibition of invasion could be achieved by depleting cholesterol from host cell membranes with methyl-β-cyclodextrin ( MβCD , Fig . S2D ) . Washout of MβCD and recovery of cholesterol [24] restored invasion of N927 ( PorBIA , P− , Opa− ) . Neither Nystatin nor MβCD affected bacterial ( not shown ) or cell viability ( Fig . S7 ) or SREC-I surface exposure ( Fig . S8 ) . Also , SREC-I co-localised with gonococci in infected Chang cells whereas disruption of lipid microdomains by nystatin treatment interfered with gonococci-SREC-I co-localisation ( Fig . S2C ) despite an increased surface exposure of SREC-I ( Fig . S8 ) . These data together demonstrated a role of membrane rafts for the uptake of PorBIA-expressing gonococci via SREC-I . Membrane raft-dependent internalization of cargo is frequently associated with caveolae , variably sized pits that form in the plasma membrane and are enriched in the major structural protein caveolin-1 ( Cav1 ) [25] . To elucidate the involvement of Cav1 in the uptake of N927 , the Cav1-negative gastric cancer cell line AGS and a corresponding Cav1-expressing AGS transgenic line ( AGS-Cav1 ) were infected with N927 ( PorBIA , Opa− , P− ) . Whereas N927 failed to efficiently invade AGS cells , we detected gonococci in AGS-Cav1 cells ( Fig . 2A ) , suggesting that Cav1 is involved in uptake of PorBIA gonococci . Caveolin phosphorylation at tyrosine 14 has previously been linked to raft internalization [26] . We therefore examined whether phosphorylation of Cav1 at Tyr14 is required for invasion via SREC receptors by the expression of the phosphorylation defective mutant Cav1-Y14F in AGS cells ( Fig . 2B ) . Uptake of N927 was inhibited in AGS cells expressing Cav1-Y14F ( Fig . 2C , 2D , S3A ) , demonstrating an important role for phosphorylation at Tyr14 for SREC-I-dependent invasion . An involvement of Cav1 in gonococcal invasion was unexpected since we recently showed that Cav1 is required to prevent uptake of piliated gonococci into epithelial cells [22] . In this case , Cav1 is recruited to attachment sites of piliated PorBIB gonococci , phosphorylated at Tyr14 and interacts with Vav2 and its substrate the small GTPase RhoA , which then leads to the establishment of stress fibers beneath the microcolonies thereby impeding bacterial uptake [22] . The finding that PorBIA or PorBIB/P+ gonococci in an isogenic background either induce or prevent their uptake via Cav1-dependent mechanism thus illustrates specific differences in Cav1-mediated signaling of both processes . In contrast to pilus-mediated adherence PorBIA-triggered invasion depends on a low-phosphate environment . Therefore we tested the influence of the invasion medium on the infection outcome and whether non-piliated PorBIA-expressing or piliated PorBIB-expressing gonococci interact with SREC-I under low phosphate conditions . Chang cells were infected with either N927 ( PorBIA , P− ) or N138 ( PorBIB , P+ ) , an isogenic strain possessing PorBIB instead of the SREC-I-interacting PorBIA porin . In SREC-I immunofluorescence studies SREC-I frequently co-localized with N927 ( 43% ) , but rarely with N138 ( 7% ) ( Fig . 2E ) , suggesting that SREC-I recruitment is exclusive for PorBIA-expressing strains . By gentamicin protection assays we compared the invasion efficiencies of either strain under phosphate free conditions . Contrary to N927 , N138 failed to invade Chang cells ( Fig . S3D ) . Further , we observed the formation of actin aggregates ( Fig . S3B ) and bacterial microcolonies ( Fig . S3C ) for N138 infection but not for N927 . This strongly suggests that not the low-phosphate conditions but rather the specific interaction with the SREC-I receptor trigger the uptake of gonococci . Recently , we identified several Cav1 pTyr14 interacting proteins , by screening microarrays of recombinant human SH2 and other phosphotyrosine binding ( PTB ) domains . High affinity interaction partners included Abl family kinases as well as phospholipase Cγ1 ( PLCγ1 ) and Vav2 [22] . Of these , only Vav2 turned out to play a major role in pilus-mediated inhibition of gonococcal invasion [22] . We therefore tested if either Abl1 or PLCγ1 are required for the invasion of PorBIA gonococci ( N927; PorBIA , P− ) . Imatinib , a selective inhibitor for the tyrosine kinase Abl1 , reduced internalization of N927 by more than 70% ( Fig . 3A ) whereas adherence was not affected suggesting that Abl1 is required for invasion . To test whether PLCγ1 is involved in N927 invasion , several PLCγ1 knock-down cell lines were tested for their ability to engulf N927 bacteria . Invasion was reduced in all these cell lines ( Fig . 3B , S4A ) . Furthermore the PLCγ1 inhibitor U73122 efficiently prevented the uptake of N927 , corroborating a role of PLCγ1 activity in the signaling cascade leading to uptake of gonococci via SREC-I ( Fig . 3C ) . We then tested whether PLCγ1 specifically interacts with Cav1 in cells infected with N927 . Pull-down experiments of endogenous proteins demonstrated that PLCγ1 interacted with Cav1 in cells either infected with N927 and non-infected cells , but not in cells infected with N138 ( Fig . 3D ) , suggesting that PLCy1 is a constitutive partner in Cav1-containing complexes . So far our data demonstrated that Abl1 and PLCγ1 are required for PorBIA-dependent invasion whereas at least PLCγ1 is dispensable for pili-induced prevention of bacterial uptake [22] . Since PLCγ1 seemed to be part of a constitutive protein complex with Cav1 we reasoned that an unknown host factor determines the specific fate of N927 ( PorBIA , P− ) and N138 ( PorBIB , P+ ) . Therefore , a biochemical assay was developed to identify native Cav1-pY14 signaling partners ( Text S1 ) . Biotin-labeled , phosphorylated or non-phosphorylated Cav1 peptides comprising amino acid residues 7–21 of Cav1 were used as bait in pull-down assays . Interacting proteins from Cav1-negative AGS cells were captured and subsequently analyzed by Maldi-MS/MS ( Fig . 4A; numbered arrows; Table 1 ) . Most interestingly , p85 , the regulatory subunit of the phosphoinositide 3-kinase ( PI3K-p85 ) , was identified as a novel interaction partner of the Cav1-pY14 phosphopeptide ( Figure 4A , arrow No . 7 ) . Additionally , myosin IB , myosin ID , non-muscle myosin heavy chain IIA , cytokeratin 1 , β-actin and splicing factor proline/glutamine rich ( SFPQ/PSF ) were identified as possible Cav1-pY14 binding partners ( Fig . 4A numbered arrows and Table 1 ) . Binding of PI3K-p85 and PLCγ1 to biotinylated Cav1-pY14 peptides was confirmed by streptavidin-agarose pull-down-assays from cell lysates and Western blot detection . PLCγ1 , p85 and also p110 , the catalytic subunit of PI3K , bound exclusively to the phosphorylated Cav1 peptide ( Fig . 4B ) , confirming these proteins as targets of phosphorylated Cav1 . To investigate the physiological relevance of PI3K for invasion of N927 ( PorBIA , P− ) , endogenous Cav1 was immunoprecipitated from infected and non-infected cells and co-immunoprecipitation of PI3K-p85 was demonstrated by Western blot analysis . Interestingly , a strong interaction was demonstrated in N927 , but not in N138 ( PorBIB , P− ) infected cells ( Fig . 4C , D ) . Interaction of Cav1 depended on PLCγ1 activity indicating that lipid second messengers generated by PLCγ1 are involved in PI3K-p85 recruitment to Cav1 ( Fig . 4 E , F ) . Pilus-mediated invasion inhibition depends on the interaction of Vav2 with Cav1-pY14 leading to RhoA activation and actin accumulation below the gonococcal microcolony [22] . In cells infected with N927 ( PorBIA , P− ) , however , less ( 50% ) Vav2 was present in endogenous caveolin-1 complexes than in cells infected with N138 ( 99% , PorBIB , P+ ) or even non-infected cells ( 100% , Fig . 4 G , H ) . Small amounts of Vav2 in pulldowns of Cav1 from the cells infected with N927 ( PorBIA , P− ) may thus originate from a small population of non-infected cells since not all cells are infected under the used conditions . A role of Vav2 for PorB-mediated invasion can unambiguously be excluded , since suppression of Vav2 expression in HeLa cells had no effect on invasion of N927 ( PorBIA , P− ) ( Fig . S4B , C ) . These data indicate that the displacement of Vav2 and the recruitment of PI3K-p85 shift the signaling cascade at the level of Cav1 from invasion inhibition to invasion . Recruitment of PI3K-p85 upon infection with N927 , but not with the isogenic strain N138 induced the activation of the kinase , as determined by Western blot analysis with an antibody that detects the active phosphorylated form of Akt ( Fig . 5A ) , a downstream target of PI3K . Activation of PI3K also depended on the presence of Cav1 since Akt phosphorylation was detected in AGS-Cav1 upon N927 infection , but not in AGS cells deficient in Cav-1 expression . In line with the selective recruitment of PI3K-p85 to Cav1 upon N927 infection , infection of AGS-Cav1 cells with N138 did not increase PI3K activity ( Fig . S5A , B ) . In addition PI3K was not activated upon infection with either N313 ( PorBIB , Opa57 ) , a gonococcal strain interacting with all CEACAM receptors [27] , or N931 ( PorBIB , Opa50 ) , which interacts with the HSPG receptor [8] , [9] ( Fig . S5C , D ) . This demonstrated that PI3K invasion signaling occurs specifically in PorBIA-expressing gonococcal infections . Inhibitor studies were performed to test whether PI3K is required for PorBIA-dependent invasion . The number of internalized bacteria was decreased in presence of the specific PI3K inhibitors LY294002 and wortmannin , whereas bacterial adherence was unchanged ( Fig . 5B ) , demonstrating a crucial role of PI3K for invasion of N927 . The specific activation of PI3K upon infection with N927 ( PorBIA , P− ) , but not with N138 ( PorBIB , P+ ) as well as the requirement of PI3K activity for N927 invasion were confirmed in End1 cells ( Fig . S5E , F ) , demonstrating the presence of this entry route for PorBIA gonococci in non-transformed cells . As shown above membrane raft domains are involved in PorBIA-triggered invasion processes . Therefore we speculated that bacterial uptake is initiated after accumulation of signaling molecules in these microdomains . We thus investigated whether SREC-I and PI3K are recruited to membrane rafts during infection . By sucrose gradient centrifugation SREC-I and PI3K-p85 were found to be enriched in Cav1- and flotillin-rich fractions of cells infected with N927 , but not in cells infected with N138 ( Fig . 5C ) , supporting the requirement of membrane rafts in SREC-I-dependent recruitment of PI3K . Treatment of CHO cells transfected with SREC-I expression constructs , but not control cells responded with the activation of PI3K either upon infection with PorBIA gonococci or upon stimulation with acLDL ( Fig . 5D ) , a known trigger of SREC-I endocytic uptake [28] . These data demonstrate that stimulation of SREC-I is sufficient to activate PI3K . The function of PLCγ1 in SREC-mediated invasion of N927 ( PorBIA , P− ) suggested the involvement protein kinase C family members ( PKC ) , since PLCγ1 generates lipid second messengers activating certain PKCs . PKCs are classified as conventional ( α , β1 , β2 , γ ) , novel ( δ , ε , η , θ , μ ) , and atypical ( ζ , λ ) isozymes . As inhibitor studies indicated a role for PKD1 ( PKCμ ) in PorBIA/SREC-I based invasion ( not shown ) , we conducted siRNA experiments thereby selectively knocking down PKD1 . Invasion was reduced by approx . 60% in PKD1 knock-down Chang cells when compared to cells treated with a control siRNA ( Fig . 6A , B ) . PKD1 activation is dependent on the phosphorylation of two activation loop sites at Ser744 and Ser748 [29] . As demonstrated with Western Blots using phospho-specific antibodies infection with N927 but not with N138 activated PKD1 ( Fig . 6C , D ) . This indicates that the activation of PKD1 is exclusive for PorBIA-gonococci infection . We have previously shown that a hitherto unidentified Rho GTPase family member is required for PorBIA-mediated invasion [14] . Here , we made use of NSC23766 , a cell-permeable pyrimidine compound that specifically inhibits Rac1 , without affecting Cdc42 and RhoA activation [30] . Inhibition of Rac1 in Chang epithelial cells completely blocked invasion of N927 ( PorBIA , P− ) , whereas adherence was unaltered as was determined by gentamicin protection assays ( Fig . 7A ) and differential immunostaining ( not shown ) . Hence Rac1 is the Rho GTPase involved in low phosphate dependent invasion . To delineate the hierarchy of the PorBIA/SREC-I invasion signaling pathway , we applied inhibitors of the identified components and assayed the activation of PI3K via Akt phosphorylation as well as Rac1 upon infection with N927 . Inhibition of Abl1 prevented Akt phosphorylation . This is in line with Abl1 being located upstream of PI3K in this signaling pathway . The pAkt signal completely disappeared after treatment with the PI3K inhibitor LY294002 ( Fig . 7B ) . Treatment of host cells with the PKD1 inhibitor , Gö6976 , had no effect on the infection-induced activation of PI3K , placing PKD1 downstream of PI3K ( Fig . 7B ) . Interestingly , PLCγ1 activity ( Fig . 7C , D ) was required for Akt phosphorylation , whereas even basal levels of active PI3K disappeared upon destruction of membrane rafts by Nystatin ( Fig . 7C ) . Since the inhibition of Rac1 by NSC failed to interfere with PI3K activation ( Fig . 7C ) and activation of Rac1 was prevented by all other inhibitors ( Fig . 7E ) , Rac1 must be localized at the end of the investigated signaling cascade . In its entirety , PorBIA/SREC-I-dependent gonococcal invasion in epithelial cells thus constitutes a novel invasion pathway involving caveolae ( Cav1 ) , Abl1 kinase , PLCγ1 , PI3K , PKD1 and Rac1 . The role of pilus phase variation during invasion and transcytosis of epithelial cells is still controversial [31] , [32] . Since pilus-mediated prevention of invasion and PorBIA-triggered invasion were dependent on highly similar signaling pathways , we asked whether PorBIA expression could override the anti-invasive signaling by pili and thus permit invasion of piliated bacteria into epithelial cells . We therefore generated N2009 ( P+ , PorBIA ) in strain MS11 and N2010 ( P− , PorBIA ) , a non-piliated derivative of N2009 and performed gentamicin protection assays under low phosphate conditions . Whereas adherence was similar between the piliated and non-piliated derivative under the chosen condition , invasion was reduced about 4-fold for the piliated strain N2009 ( Fig . 8A ) . Since pilus expression is phase variable , we monitored the pilus phenotype of the input strain and bacteria recovered after the gentamicin protection assay . Less than 5% of the colonies of the inoculum were non-piliated when viewed under a stereomicroscope or analyzed by electron microscopy ( Fig . S9A ) . By contrast 80 . 5% of the recovered bacteria had a P− phenotype ( Fig . 8B ) . We obtained a similar result with the immortalized endocervical cell line End1 , supporting a role of pili in the switch from an adherent to an invasive phenotype also in non-transformed cells ( Fig . S 9B ) . Our observation suggested that only non-piliated variants were able to invade host cells and that pilus expression effectively blocked PorBIA-dependent invasion . Expression of pili can be frequently switched ‘ON’ and ‘OFF’ by RecA-dependent recombination between silent and expression pil loci ( frequency about 10−3 ) or by less frequent ( frequency about 10−6 ) RecA-independent in- and out-of-frame switches in the pilus assembly gene pilC . To test the hypothesis that the non-piliated , invasive variants arise as a consequence of the natural switch-off in pilus expression a recA mutant defective in pilE recombination [33] was generated in a piliated PorBIA expressing strain . N2013 ( recA , P+ , PorBIA ) failed to invade Chang cells ( Fig . 8C ) consistent with a role of pilus phase variation in the transition from an anti-invasive to an invasive phenotype . In contrast , N2015 ( recA , P− , PorBIA ) , a non-piliated recA derivative , invaded into Chang cells ( data not shown ) , ruling out recombination events other than those leading to pilus variation to be involved in PorB-dependent invasion .
The initial interaction of gonococci with their host is mediated by the type 4 pili leading to highly efficient colonization and microcolony formation at the surface of the epithelium of the urogenital tract . We show here that both , pilus-dependent microcolony formation and PorBIA-dependent invasion , engage caveosomes although in an antagonistic fashion . Although the PorBIA-triggered invasion requires SREC-I , we were intrigued that the cytoplasmic C-terminal domain of the receptor is dispensable for invasion of strain N927 ( PorBIA , P− ) . This is reminiscent of endocytosis of modified LDL by SREC-I which is independent of the C-terminal domain [34] . Other receptors such as CEACAM-1 also trigger the uptake of bacteria in the absence of a cytoplasmic domain via membrane rafts [23] . We therefore hypothesize that SREC-I oligomerization following PorBIA-dependent binding of N . gonorrhoeae is sufficient for receptor recruitment to membrane rafts and initiation of invasion signaling . In the present work SREC-I was found to be selectively recruited to detergent-resistant microdomains in cells infected with PorBIA invasive gonococci . In line with the localisation of SREC-I to membrane rafts [35] , we found that intact membrane rafts and caveolin-1 are required for the uptake of gonococci via PorBIA and SREC-I . Although membrane rafts and caveolin-1 have been implicated in the invasion of bacteria before [36] , their role for PorBIA gonococci invasion was unexpected since we previously demonstrated that caveolin-1 aggregation at adhesion sites of piliated gonococci causes a block of invasion [22] . Intriguingly , AGS cells naturally defective for caveolin-1 expression [37] were only efficiently invaded by N927 ( exhibiting the PorBIA allele ) if caveolin-1 was expressed as a transgene ( Fig . 2 ) whereas isogenic piliated gonococci invaded AGS cells but not the isogenic AGS-Cav1 line [22] . The key question therefore arose how caveolin-1 exerts this dual function as an invasion promoter or an inhibitor . Since both , invasion promotion ( Fig . 2B–D ) and inhibition [22] , depended on the phosphorylation of caveolin at Tyr14 , we first tested Cav-Y14P interacting proteins [22] for a role in strain N927 ( PorBIA , P− ) invasion . PLCγ1 turned out to be a constitutive partner of endogenous Cav1 protein complexes in host cells . PLCγ1 activity , however , is required for the uptake of N927 whereas it is not involved in the anti-invasive signaling of piliated gonococci [22] . Interestingly , PLCγ1 activity was required for the recruitment of the p85 regulatory subunit of PI3K identified as a novel caveolin-interacting protein in the present work . Intriguingly , PI3K-p85 was selectively recruited to caveolin-1 complexes in cells infected with strain N927 leading to kinase activation and Akt phosphorylation . By contrast , Vav2 was depleted from these complexes ( Fig . 4 G , H ) . Since the Cav-pY14 interacting exchange factor Vav2 plays a critical role for the invasion block mediated by piliated gonococci [22] , our data strongly suggested that the selective recruitment of either Vav2 or PI3K-p85 determines the extra- or intracellular fate of gonococci , respectively . Using the A431 tumor cell line , Lee et al . have previously demonstrated a role of PI3K for the invasion of piliated gonococci [38] . Since we did not detect significant invasion of piliated gonococci in the two different tumor lines ( Chang , ME-180 ) and the non-transformed End1 cells it is possible that differences in the infection protocol and/or special features of the A431 line account for these contradictory observations . Additionally , our data clearly demonstrated the involvement of PKD1 in the invasion of N927 ( PorBIA , P− ) . This is , to our knowledge , the first report on an involvement of this novel PKC in the invasion of pathogenic bacteria . PKD1 has previously been implicated in the control of actin reorganisation and tumor cell migration [39] . Known activators for PKD1 are classical PKCs , phospholipase Cγ , diacylglycerol and PI3 kinase [40] . We ruled out an activation of PKD1 via classical PKCs because Gö6983 , a potent inhibitor of these PKCs , did not affect invasion at physiologically relevant concentrations ( not shown ) . It is very likely , that second messengers generated by PLCγ1 and PI3K are critically involved in the activation of PKD1 in the course of N927 infection . There are also indications that Abl1 can activate PKD1 , since Abl1 phosphorylates PKD1 at Tyr463 . This phosphorylation is known to facilitate the phosphorylation of Ser738/Ser742 , which in turn leads to the activation of PKD1 [41] . Activation of PKD1 , critical for the uptake of PorBIA-expressing gonococci , is not involved in invasion of Opa-expressing bacteria via CEACAM-receptors on Chang cells ( Fig . S6A , B ) and inhibition of PI3K activity did not prevent invasion via CEACAM3 [42] , [43] , indicating the usage of alternative infection routes by these phase-variable bacteria . Interestingly , PKD1 knockdown prevented the uptake of Opa50-positive gonococci via HSPG receptor ( Fig . S6A , B ) . A not further characterized PKC member has previously been linked to invasion via HSPG [44] . Thus , it is likely that PKD1 represents this protein kinase . Apart from the activity of PKD1 we ruled out further similarities between the SREC-I- and HSPG-mediated uptake processes [14] . Several studies have shown that formation of pili is necessary for efficient adherence of gonococci [4] , [7] and that PorBIA-expression is associated with serious systemic infections [17] , [18] . Our finding that the absence of pili in otherwise isogenic backgrounds is decisive for invasion thus has important implications for the development of invasive gonococcal disease . A pilus-dependent mechanism has recently been proposed for dissemination of N . meningitidis . In this case posttranslational modification of pilin determines whether meningococci colonize or invade host tissue [45] . Based on our data we propose that piliated gonococci expressing the PorBIA allele remain extracellular by stabilizing a caveolin-pY14-Vav2 complex followed by the activation of RhoA and the formation of actin aggregates underneath gonococcal microcolonies . Natural pilus phase variation leads to the formation of non-piliated variants . Loss of piliation favors the displacement of Vav2 and the recruitment of PI3K-p85 to the caveolin-pY14 in a SREC-I and PLCγ1-dependent manner which overrides the invasion inhibition . As a consequence , PI3K is activated and a unique signaling pathway leading to the activation of PKD1 and Rac1-dependent invasion is initiated . The concept emerging from our data suggests a role of lipid microdomains as signaling platforms for invasion inhibition and promotion dependent on the presence or absence of phase-variable pili . This unexpected mechanistic interdependence of local and invasive infection also suggests a new role of pilus phase variation in PorBIA-expressing gonococci as a stochastic event that controls the molecular switch to invasive gonococcal disease . Moreover , pilus expression in N . gonorrhoeae can irreversibly be lost [46] , [47] , a phenotype that – in the context of the PorBIA allele – would favor the occurrence of invasive gonococcal infection .
N . gonorrhoeae MS11 derivatives used in this study are listed in table 2 . N927 is a derivative of N138 with the porBIA gene of strain VP1 flanked by antibiotics resistance cassettes cat and ermC integrated into the porB locus . N927 in addition carries a deletion in the pilE1 expression locus ( non-revertible P−: Pn ) [21] . N2009 ( PorBIA , Opa− , P+ ) is a piliated variant of N138 expressing PorBIA from N927 . N2010 is a revertible non-piliated derivative of N2009 ( PorBIA , Opa− , P+ ) . N2013 was generated by transforming the PorBIA expression cassette from strain N927 [21] into strain N503 ( inducible recA , ermC ) . Gonococci were routinely grown on GC agar base plates ( Oxoid ) supplemented with 1% vitamin mix for 14–18 h at 37°C in 5% CO2 in a humidified atmosphere . Opa and pili negative phenotypes were monitored by colony morphology under a stereo microscope or by immunoblotting . 2×105 Chang cells were infected at an MOI 10 , all other cells at an MOI 50 to achieve similar infection efficiency . Gentamicin protection assay was conducted as described [14] . Briefly , Chang cells were infected with the gonococcal strains at a confluency of 80–90% . To quantify total cell associated bacteria , cells were lysed with 1% saponin for 7 min . Suitable dilutions were plated on GC agar plates and CFU were determined 24 h later . For quantification of intracellular viable bacteria monolayers were incubated with 50 µg/ml gentamicin in HEPES medium for 2 h at 37°C and 5% CO2 , prior to lysis in 1% saponin and plating . In general , 25–50% of the bacteria adhered to the cells ( about 0 . 5–1×105 gonococci per infection ) and 10 and 20% of adherent PorBIA-expressing gonococci invaded the cells ( 1–2×104 gonococci per infection ) . Active Rac1 was precipitated as described [48] . Briefly , cell lysates ( 5×106 cells/each sample ) were prepared in IP lysis buffer ( 50 mM Tris-HCl pH 7 . 5 , 150 mM NaCl , 1% ( v/v ) Triton-X-100 , 10% ( v/v ) glycerol , 2 mM EDTA , 25 mM NaF , and 2 mM NaH2PO4 ) containing protease and phosphatase inhibitor cocktails ( Roche ) . Samples were incubated with 500 ng of anti-active- Rac1 monoclonal antibody ( New East Bioscience ) over night . Protein G Agarose ( GE Healthcare ) was added for 2 h . Caveolin was precipitated from lysates prepared in cell lysis buffer ( 20 mM Tris , 150 mM NaCl , 1 mM EDTA , 1 mM EGTA , 1% ( v/v ) Triton-X-100 , 2 . 5 mM Sodiumpyrophosphate , 1 mM β-Glycerophosphate , pH 7 . 5 ) containing PhosStop Phosphatase Inhibitor and Complete Protease Inhibitor ( Roche ) . The lysates were incubated with 2 µg bait antibody ( anti-Caveolin , BD Transduction ) overnight . Protein G-magnetic beads ( Dynabeads , Invitrogen ) were subsequently added for 4 h to precipitate antigen-antibody complexes . After extensive washing , the precipitate was eluted by heating to 95°C in SDS loading buffer and the individual proteins separated by SDS-PAGE . Western blotting was used to assess the precipitate . Cell lysates were resolved by 8–12% sodium dodecyl sulfate ( SDS ) -polyacrylamide gel electrophoresis . Proteins were transferred to polyvinylidene difluoride membranes ( GE Healthcare ) and blocked with Tris-buffered saline containing 0 . 1% Tween 20 and 3% bovine serum albumin . The following primary antibodies were used: anti-Flotillin , anti-PI3K ( p85 ) , anti-PI3K ( p110 ) , anti-pAKT , anti-Akt , anti-HA anti-PKD1 , anti-pPKD1 , anti-Vav2 ( Cell Signaling ) , anti-Caveolin ( BD Transduction ) , anti-SREC-I , anti-GST , anti-GFP , anti-PLCy1 , anti-Tubulin ( Santa Cruz Bioscience ) and anti-Actin ( Sigma Aldrich ) . Proteins were detected with peroxidase-coupled secondary antibodies using the ECL system ( Pierce ) and a Intas Chem HR 16-3200 reader and quantified by ImageJ software . CHO cells or AGS cells were seeded onto cover slides , transfected with the indicated plasmids and 24 h after transfection infected under phosphate free conditions with N927 MOI 50 for 2 h . After extensive washing steps cells were fixed with 4% Paraformaldehyd for 15 min . For differentiating extra- from intracellular bacteria the staining method was used as described before [14] . Briefly , extracellular bacteria were detected with primary antibody , polyclonal rabbit anti-N . gonorrhoeae and subsequently samples were incubated with a Cy5 conjugated secondary anti-rabbit antibody . Cells were then permeabilized with 0 . 1% Triton-X-100 for 15 min . Staining of the extracellular and intracellular bacteria were subsequently performed as described above using a Cy3 conjugated secondary anti-rabbit antibody . Actin staining was carried out using Phalloidin-647 ( MFP ) 100 nM . Bacteria were stained before infection with the fluorescent dye SNARF ( Invitrogen ) for 20 min and washed several times with infection medium before infection .
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Neisseria gonorrhoeae is a human-specific bacterial pathogen causing gonorrhea . With over 100 million infections per year it is among the most prevalent sexually-transmitted diseases worldwide . Whereas most infections are localized , occasionally N . gonorrhoeae invades the blood stream . The resulting disseminated infections often lead to serious conditions such as dermatitis , sepsis , endocarditis , and arthritis . Gonorrhea causes particular concern due to the currently ongoing dramatic spread of multi-resistant bacteria , which might render the disease untreatable in the future . Here , we describe molecular events that lead to the switch from local to invasive gonococcal infections . Whereas pili constitute adhesive structures leading to localized infections , the natural loss of piliation unblocks a hitherto unidentified signaling cascade initiated by the interaction of an outer membrane porin and a eukaryotic scavenger receptor . We show that in both cases the different infection outcomes rely on distinct signaling molecules , which are either recruited to or displaced from caveolae . Furthermore , we unravel the signaling network which activates cytoskeletal rearrangements that ultimately lead to the porin/scavenger receptor-triggered invasion of the host cell .
|
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"Results",
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[
"medicine",
"infectious",
"diseases",
"gonorrhea",
"gram",
"negative",
"sexually",
"transmitted",
"diseases",
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"host-pathogen",
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] |
2013
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Pilus Phase Variation Switches Gonococcal Adherence to Invasion by Caveolin-1-Dependent Host Cell Signaling
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Leptospirosis is a neglected zoonosis affecting mainly tropical and subtropical regions worldwide , particularly South America and the Caribbean . As in many other countries , under-reporting of cases was suspected in the French West Indies because of inadequate access to diagnostic tests for the general population . In order to estimate the real incidence of leptospirosis in Guadeloupe and Martinique , a study was performed in 2011 using the three prevailing available biological tests for diagnosis: Microscopic Agglutination Test ( MAT ) , IgM ELISA and PCR . The study investigated inpatients and outpatients and used active case ascertainment from data provided by a general practitioners’ sentinel network . The epidemiology of the disease was also described in terms of severity and demographic characteristics . Leptospirosis incidence was estimated at 69 . 4 ( 95%CI 47 . 6–91 . 1 ) and 60 . 6 ( 95%CI 36 . 3–85 . 0 ) annual cases per 100 000 inhabitants in Guadeloupe and Martinique , respectively , which was 3 and 4 times higher than previous estimations . Inclusion of PCR and IgM ELISA tests for diagnosis of leptospirosis resulted in improved sensitivity in comparison with MAT alone . Our results highlighted the substantial health burden of the disease in these two territories and the importance of access to appropriate laboratory tests . Based on our results , PCR and IgM ELISA tests have now been included in the list of tests reimbursed by the national system of social security insurance in France . Our results also underline the relevance of implementing an integrated strategy for the surveillance , prevention and control of leptospirosis in the French West Indies .
Leptospirosis is a zoonotic bacterial disease which is particularly widespread in tropical and subtropical regions . It produces a wide array of clinical symptoms , ranging from an undifferentiated mild fever to severe multi-organ failure [1] . None of these symptoms is specific to the disease . Until recently , diagnosis was mostly based on serological tests , as antibodies are detectable in blood by the second week after the onset of symptoms . PCR-based methods are becoming more widely used for the detection of bacterial , in part because of their superior sensitivity and ability to establish an early diagnosis . The disease can usually be cured in humans within a few weeks without sequelae using appropriate antibiotic therapy [2] . However , it often requires hospitalization during the acute phase and complications related to the disease can be fatal . Leptospirosis disease burden estimates have been recently updated by Costa et al . [3] . The authors estimate that worldwide 1 . 03 million cases and 58 , 000 deaths occur annually . In addition , the social cost in years of potential life lost and hospital costs associated with leptospirosis are high when compared with the cost of early treatment and prevention of the infection [4 , 5] . However , leptospirosis is underdiagnosed worldwide , especially in low-resource tropical countries [6] . The bacteria Leptospira [1] are maintained in nature through the chronic renal infection of host animals , and are excreted in the hosts’ urine . Leptospirosis is transmitted through contact of abraded skin or mucous membranes either directly with infected urine or organs or indirectly with contaminated soil or water . Accordingly , activities which bring humans into contact with such contaminated environments increase the risk of contracting the disease . These include farming , gardening , building work , animal husbandry , hunting , fishing and water sports in fresh water environments . This study focused on the two French overseas territories of Guadeloupe and Martinique ( approximately 400 , 000 inhabitants in each ) which are located in the French West Indies ( hereafter FWI ) . Guadeloupe is an archipelago which includes two main islands , Grande-Terre and Basse-Terre ( hereafter jointly covered by the name “Guadeloupe” ) . Guadeloupe and Martinique have a similar tropical climate with a rainy season between July and December . Furthermore , the populations are quantitatively and qualitatively similar as far as ethnic and socio-economical characteristics are concerned The population of the FWI is mainly of African or mixed descent . There are also Europeans , Indians , Lebanese , Syrians , Chinese , and Amerindians ( remnants of the original pre-European population ) . Life expectancy at birth for males in 2013 was 76 and 79 years , respectively , in Guadeloupe and Martinique , and 85 for females in both territories . The service sector dominates the economy of the two territories while the public sector is the major employer accounting for 42% of total salaried workers . The economy is very dependent on France for subsidies and imports . In 2013 , unemployment rates were 25 . 5% and 22% , respectively , in Guadeloupe and Martinique , 2 . 5 times higher than in mainland France . Because of the warm climate in the FWI , outdoor activities are common throughout the year and are easier to undertake without the use of protection ( boots , gloves , etc . ) . The tropical climate promotes the survival of leptospires and their proliferation in wet environments . In addition , numerous Caribbean mammals are hosts to pathogenic Leptospira species including rodents ( most frequently ) , opossums , mongoose , bats , pigs , bovines , goats and dogs [7] . The Leptospira genus includes ten pathogenic species [8] . The most frequent serogroups in Guadeloupe and Martinique are Icterohaemorrhagiae , Canicola and Sejroe . The serogroup Ballum is also frequently reported in Guadeloupe [9] . Between 2002 and 2008 , the estimated annual incidence of leptospirosis in Guadeloupe ( 22 . 5 per 100 , 000 inhabitants ) and Martinique ( 13 . 9 per 100 , 000 inhabitants ) was much higher than that observed in mainland France ( 0 . 47 per 100 , 000 inhabitants ) [10] . At that time , biological diagnosis in Guadeloupe and Martinique was performed by sending blood samples to the National Reference Center for leptospirosis in Paris ( Pasteur Institute ) for microscopic agglutination test ( MAT ) . Because of these logistical and technical limitations , as well as a lack of epidemiological surveillance , it was expected that the disease was underdiagnosed and its public health burden underestimated in the FWI . In this context , an incidence study was performed in 2011: i ) to obtain reliable data and assess the “real” disease burden of leptospirosis in the FWI; ii ) to provide scientific evidence for the hypothesis that leptospirosis diagnosis should be reinforced by including tests capable of reliably confirming diagnosis immediately after the onset of the disease in the NABM ( Nomenclature des actes de biologie médicale , which is the list of clinical pathology tests which social security insurance in France covers ) and iii ) to contribute to the implementation of an integrated management strategy based on an epidemiological surveillance , warning and response system .
The study covered the entire population of Continental Guadeloupe and Martinique . Between January 1 , 2011 and December 31 , 2011 ( i . e . , the study period ) , the number of incident cases of leptospirosis was counted in both territories using two sources: i ) public hospitals ( 2 in Guadeloupe and 3 in Martinique ) and ii ) General Practitioners’ ( GPs ) sentinel surveillance networks ( one on each island ) . In these two networks , GPs are included on a voluntary basis , according to a sampling strategy based on geographical localization and population density . The activity ( annual number of consultations ) of the GPs participating to these sentinel surveillance networks represents 20 . 4% and 22 . 4% of the total activity of all GPs in Guadeloupe and Martinique , respectively . Patients were eligible for inclusion in the study if they had lived permanently for one year in Martinique or Continental Guadeloupe and had consulted either a sentinel GP or a healthcare professional in a public hospital for a suspected clinical case of leptospirosis , defined as the acute onset of fever ≥38°C which then continued for less than 14 days , without any other infectious diagnosis and with at least one of the following symptoms: headache , myalgia , arthralgia and lower back pain . The strategy used for the diagnosis of leptospirosis depended on the time of sampling , as illustrated in the accompanying supplementary document ( S1 Fig ) . Between the first and ninth day of illness ( acute phase ) , a real-time PCR test [11] was locally performed . If it tested negative the IgM ELISA test was performed ( also locally ) [12–14] and if the latter tested positive ( i . e . , single titer ≥1:400 ) , definitive confirmation was obtained using a Microscopic Agglutination Test ( MAT ) which included a panel of 17 antigens [15] . After the ninth day of illness ( immune phase ) , an IgM ELISA test was performed and , if it proved positive , confirmation was then obtained with a MAT . The MAT , which was performed at the National Reference Center for Leptospirosis ( Institut Pasteur , Paris , France ) , was considered positive when the titer was ≥ 1:400 for at least one antigen ( except antigen L biflexa serovar Patoc ) [16] . If the first blood sample tested negative for all the tests , a second blood sample was recommended two weeks after the first in order to repeat the IgM ELISA . If the latter tested positive , the MAT was repeated . Finally , leptospirosis was confirmed if the real-time PCR or IgM ELISA and MAT tests tested positive for at least one sample . Information on gender , date of birth , city of residence , date of the onset of symptoms , date of blood sampling , laboratory test results , and , if relevant , information on hospitalization duration and disease severity , was recorded for each patient , whether the case was confirmed or not , using a standardized form . A case was defined as severe if the person died or was admitted to an intensive care unit or underwent renal dialysis or mechanical ventilation , or when a combination of these criteria was met . The overall incidence of leptospirosis was estimated using a sampling approach , stratified on the two data sources—hospitals and GPs ( Fig 1 ) . All hospital confirmed cases ( inpatients and outpatients ) were taken into account for the overall incidence calculation . The number of cases confirmed outside of hospital was estimated from sentinel GPs figures , using a random two-level sampling method calculation as follows: 1 ) the number of cases reported by the sentinel GPs as eligible cases was extrapolated to the whole population over both territories , based on the total weekly activity rate of participating GPs ( first level ) ; 2 ) the number of blood samples collected by each sentinel GP for eligible cases was considered as a random sample ( second level ) and the positivity rate of these blood samples was applied to the previous extrapolated number . When a patient was diagnosed by a sentinel GP and then hospitalized , he/she was considered as an hospitalized case and therefore subtracted from the GPs cases numbers . The calculation of the confidence interval estimate took into account both the variance of the number of eligible cases reported by GPs , and the variance of the above positivity rate ( S1 Protocol . Calculations for the estimation of incidence ) . Differences between positivity rates according to the source of data and to the specific territory ( i . e . either Guadeloupe or Martinique ) were tested for their statistical significance with χ2 test . P values < 0 . 05 were considered significant . Statistical analyses were performed using Microsoft Office Excel 2003 and Intercooled Stata 08 . This study was part of national public health surveillance program of the Institute for Public Health Surveillance ( Institut de Veille Sanitaire , InVS ) , a governmental agency reporting to the French Ministry of Health . Therefore , consultation with ethics committee was not required . Information on leptospirosis was distributed and each participant agreed verbally an informed consent to participate as a volunteer in the study and could withdraw anytime without further obligation ( S1 Consent form ) . Diagnostic test results was provided free of charge to the participants . The study was approved by France's data protection commission ( CNIL ) under number DR-2011-96 ( S1 Authorization ) . All data used in the study was anonymized .
A total of 1 , 305 suspected cases were included in the study in both territories , 1 , 167 being recruited in hospitals and 138 through the sentinel networks . The total number of hospitalized-confirmed cases was 126 in Guadeloupe and 108 in Martinique ( Table 1 ) . By extrapolating the data reported by the sentinel networks , the total estimated number of cases was 267 and 240 in Guadeloupe and Martinique , respectively ( Table 2 ) . The corresponding overall incidence ( per 100 000 inhabitants ) was 69 . 4 and 60 . 6 in Guadeloupe and Martinique , respectively . In total , in both territories , more than a third of biological confirmations were obtained using PCR . In Guadeloupe , secondary blood sample testing—using IgM ELISA and MAT—confirmed 31% of diagnosed cases . The number of included patients ( i . e . suspected cases ) and the number of confirmed cases were higher in hospitals than in the sentinel networks ( the latter being samples of all the GPs of each territory ) . Positivity rates for biological diagnosis ranged from 13 to 22% according to the data source and territory . No difference was observed between positivity rates of sentinel GPs’ patients from Guadeloupe and Martinique ( 13% ) , or between positivity rates of hospital patients and sentinel GPs’ patients ( 15 vs 13% ) in Martinique ( p>0 . 05 ) . A moderate difference ( 22 vs 15% ) was observed between the positivity rates of hospital patients from Guadeloupe and Martinique ( p = 0 . 02 ) , and between the positivity rates of hospital and sentinel GPs’ patients ( 22 vs 13% ) in Guadeloupe ( p = 0 . 05 ) . In Table 2 , the estimated incidences in Guadeloupe and Martinique in 2011 are compared with the figures for the reference period 2002–2008 . In our study in 2011 , the overall estimated incidence of leptospirosis was three times higher for Guadeloupe and four times higher for Martinique compared with the reference period 2002–2008 . In addition , a difference of 12% was observed between Guadeloupe and Martinique in 2011 , compared with almost 40% for the reference period ( p < 0 . 01 ) . In both territories , the positivity rates was most commonly observed in adults aged 20–59 , than in the over-60 population ( Table 3 ) . The study also showed , for the first time in Guadeloupe and Martinique , that leptospirosis also occurs in children , with cases confirmed in persons younger than 10 . Men were six times more likely than women to be affected by leptospirosis in both territories , the sex ratios of confirmed cases being similar in Guadeloupe ( 6 . 4 ) and Martinique ( 6 . 2 ) . This trend was observed across all age groups , being statistically significant among adults and , in Guadeloupe , among people over 60 years . Disease severity indicators are displayed in Table 4 . In both Guadeloupe and Martinique , these indicators confirm the leptospirosis disease burden . The eight deaths which occurred in Guadeloupe were directly attributed to leptospirosis by hospital specialists in infectious diseases .
In 2011 , the estimated number of confirmed cases of leptospirosis in Guadeloupe was 267 , comprising 115 hospital cases and an estimated 152 GP cases . In Martinique , 240 cases were confirmed , comprising 101 hospital cases and an estimated 139 GP cases . The total estimated number of cases for each territory–approximately 250 cases per year—was close to that observed in mainland France , where the population is approximately 120 times greater [15] , indicating that the burden of leptospirosis is much higher in the FWI . The incidence in FWI remained high in the last few years ( 2012–2014 ) with a similar number of laboratory-confirmed cases in both territories . Approximately seventy and sixty cases were estimated per 100 , 000 inhabitants in 2011 , respectively , in Guadeloupe and Martinique . These estimates were respectively three and four times higher than each territory’s average incidence during the reference period 2002–2008 ( reported by the National Reference Center ) ( Table 2 ) . Our study shows that a significant higher number of cases was detected when both IgM ELISA and PCR tests are used , further indicating that the lack of adequate diagnostic tests contributes to under-reporting of cases [3] . However , some limitations of our study must be considered . First , sentinel GPs are not randomly selected . However , these n sentinel GPs networks have been widely used for a decade to estimate the numbers of suspected cases of other diseases in Guadeloupe and Martinique ( including dengue and , more recently , chikungunya ) and the estimates obtained from these networks were coherent with those obtained from other surveillance systems , as laboratory-based data or hospital emergency department data [17] . Second , 22% of eligible patients were not included in the study because sentinel GPs did not test them for leptospirosis . The reasons for non-inclusion were diverse ( GPs in holidays , misinterpretation of the case definition or inclusion criteria , etc . ) and may not introduce bias . We therefore considered that eligible patients were included at random to receive a prescription for biological testing . Leptospirosis belongs to the group of neglected tropical diseases [3] which comprises some of the most common infections in Latin America and Caribbean countries . The 7 . 5% rate of severe leptospirosis ( ratio of the number severe cases / total estimated number of cases ) observed in this study in 2011 is much higher than the 0 . 3% recorded for dengue during the most recent epidemic in 2010 [17–18] . The annual incidence of leptospirosis has been shown to be associated with climate and meteorological conditions . Thus , heavy rainfall results to increased survival of Leptospira in the environment and increased exposure of humans to water . In 2011 , both territories experienced heavy rainfalls , but no cyclone [19 , 20] . A long-term surveillance system would therefore be required to accurately describe annual variations in the disease incidence , for example during the El Niño Southern Oscillation periods [21] . The incidence of severe cases of leptospirosis was 5 . 2 in Continental Guadeloupe and 3 . 3 in Martinique per 100 , 000 inhabitants in 2011 . This reported incidence is similar to the one recorded in Réunion Island in 2011where the incidence of cases transferred to intensive care ( only ) was 2 per 100 , 000 inhabitants [22] . Case fatality rates ( CFR ) observed in Guadeloupe ( 3% ) and in Martinique ( 0% ) were also similar to that observed in Réunion Island for the period 2004–2008 , where it ranged from 0% to 7% depending on the year , except in 2006 when leptospirosis lethality reached 38% during the chikungunya epidemic [23] . Possibly due to the relatively higher income status of FWI and to access to early and free testing during our study period , estimated case fatality rates in FWI are lower than the global estimations of Costa et al . [3] . The demographic characteristics of the cases in our study match those described in the literature , albeit with an increased proportion of older persons in the FWI [24 , 25] . The predominance of male cases is generally attributed to the hypothesis that men are potentially more exposed than women due to more frequent at-risk activities . However , we also observed this ratio in the older age group where women are more numerous than men , and probably have similar daily activities as the latter , suggesting that other hypothesis needs to be evaluated . Improving patient care is a priority . Access to diagnosis is crucial , because treatment for leptospirosis patients is much more effective if antibiotics are administered as early as possible following the onset of disease [5 , 26] . Early diagnosis of acute leptospirosis by real-time PCR would prevent potential complications and limit periods of stays in hospital . This assay , which was not available in Guadeloupe before the study , is now routinely used in both territories . During the immune phase of the disease ( from the end of the first week ) , the IgM ELISA , which usually becomes positive earlier than MAT in the course of the illness , can also offer useful support to physicians to make good treatment decisions . The IgM ELISA is a simple and rapid method which is not requiring the use of sophisticated laboratory equipment or trained personnel . Partly because of the results of this study , in September 2014 the French social security insurance decided to reimbursed the cost of both PCR and ELISA for the diagnosis of leptospirosis in mainland France and French overseas territories . Implementation of an epidemiological surveillance system including the systematic collection and analysis of data should allow the public health community to respond more quickly to a given epidemiological situation ( clustered cases , seasonal outbreaks , evaluation of prevention and control measures , etc ) . These results advocate for an integrated surveillance , early warning and management strategy to reduce the incidence and severity of leptospirosis . The experience of dengue , which prompted the implementation of an integrated management strategy promoted by the WHO in the FWI , could serve as a model [18 , 27] .
|
Leptospirosis is a common disease in tropical regions around the world . It is caused by a bacteria excreted in environmental waters by mammals , especially rodents , through their urine . Leptospirosis has symptoms similar to other tropical diseases , including dengue fever , and early laboratory diagnosis is crucial to provide both appropriate treatments for patients and rapid control measures when an outbreak occurs . In 2011 , we undertook a study to determine the incidence of leptospirosis in two territories ( Guadeloupe and Martinique ) in the French West Indies by establishing a surveillance network and implementing new diagnostic assays in order to ensure an exhaustive diagnostic analysis . We concluded that leptospirosis was previously significantly under-reported in the French West Indies and we recommended: 1- access to these new diagnostic tests for the entire population for a better detection of leptospirosis patients and , 2- the implementation of an integrated surveillance , alert and prevention system for the disease in this region . Our findings had raised the awareness of this neglected disease in the French West Indies and , as another consequence; new diagnostic tests are now reimbursed by the social security insurance in France .
|
[
"Abstract",
"Introduction",
"Methods",
"Results",
"Discussion"
] |
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2016
|
Underestimation of Leptospirosis Incidence in the French West Indies
|
The formation of DNA double-strand breaks ( DSBs ) must take place during meiosis to ensure the formation of crossovers , which are required for accurate chromosome segregation , therefore avoiding aneuploidy . However , DSB formation must be tightly regulated to maintain genomic integrity . How this regulation operates in the context of different chromatin architectures and accessibility , and how it is linked to metabolic pathways , is not understood . We show here that global histone acetylation levels undergo changes throughout meiotic progression . Moreover , perturbations to global histone acetylation levels are accompanied by changes in the frequency of DSB formation in C . elegans . We provide evidence that the regulation of histone acetylation requires CRA-1 , a NatB domain-containing protein homologous to human NAA25 , which controls the levels of acetyl-Coenzyme A ( acetyl-CoA ) by antagonizing ACER-1 , a previously unknown and conserved acetyl-CoA hydrolase . CRA-1 is in turn negatively regulated by XND-1 , an AT-hook containing protein . We propose that this newly defined protein network links acetyl-CoA metabolism to meiotic DSB formation via modulation of global histone acetylation .
Achieving accurate chromosome segregation is a critical outcome for any cell division process . This is strikingly evident during the meiotic cell divisions leading to the formation of haploid gametes ( i . e . oocytes and sperm ) , where errors in chromosome segregation result in aneuploidy and therefore increased miscarriages , infertility , birth defects and tumorigenesis in humans [1 , 2] . A central mechanism set in place to promote faithful segregation during meiosis consists in the formation of programmed meiotic DNA double-strand breaks ( DSBs ) via a conserved topoisomerase-like protein , Spo11 , and its associated factors [3–8] . Meiotic chromosomes are organized into arrays of loops attached to chromosome axes , with studies in yeast and mice indicating that programmed DSBs form in loop sequences that are tethered to the axes [9–12] . A subset of these DSBs is repaired as crossovers via reciprocal exchange of genetic information between homologous chromosomes resulting in physical attachments ( chiasmata ) between homologs . Ensuring crossover formation therefore serves two critical purposes: to ensure genetic diversity and to hold homologs together so they can properly align at the metaphase plate and then accurately segregate away from each other at meiosis I . However , despite the critical importance of DSB formation to human reproductive health , the mechanisms regulating meiotic DSB frequency and distribution throughout the genome remain poorly understood . One plausible mode of DSB regulation is via alterations in chromatin accessibility . In that vein , post-translational modifications such as histone methylation have been recently implicated in this process by the analysis of DSB hotspots ( discrete regions with high frequencies of recombination initiation ) [13] . Studies in yeast have suggested that an open chromatin structure is required for Spo11 to access its DNA substrate to generate DSBs [14] . Recent mapping of meiotic recombination initiation sites in yeast and mice has suggested trimethylated lysine 4 on histone H3 ( H3K4me3 ) as a critical modification required for meiotic DSB formation [15 , 16] . However , H3K4me3 enrichment and DSB formation showed little correlation in these organisms [17–19] . Therefore , the chromatin state requirements promoting DSB formation remain a key unresolved question . Importantly , not much is known about the roles of histone acetylation in modulating DSB frequencies and/or distribution . Acetylated histone H3K9 has been found to be a major hotspot-associated modification in fission yeast , but it has only a mild , albeit significant , effect on DSB formation at hotspots [20] . This raises the possibility that changes to either a combination of histone acetylation marks or to global histone acetylation , instead of just to a specific acetylation mark , might be critical for DSB formation . Histone acetylation is generally associated with euchromatin , it promotes transcriptional activation , and therefore could be a critical level of control for genomic functions . Histone acetylation involves histone acetyltransferases ( HATs ) and histone deacetylases ( HDACs ) , which either respectively catalyze or reverse the transfer of an acetyl group from acetyl-Coenzyme A ( acetyl-CoA ) to lysine residues on histones . Recent studies have shown that regulation of acetyl-CoA levels by metabolic enzymes can affect global histone acetylation . In yeast , reducing acetyl-CoA carboxylase expression results in a global increase of histone acetylation [21] , and mutation of the acetyl-CoA synthetase Acs2 causes global histone deacetylation [22] , suggesting that the regulation of acetyl-CoA levels is another critical control of histone acetylation . However , little is known about how regulation of global histone acetylation is involved in specific cell activities or whether it may modulate biological pathways . The genetic tractability of the nematode C . elegans is coupled to various other features that make it an extremely advantageous model system for investigating the regulation of meiotic DSB formation . Nuclei are positioned in a temporal/spatial gradient within the germline , affording ease of simultaneous cytological analysis of all stages of meiotic prophase [23] . High-resolution microscopy utilizing antibodies to well-established markers of DSB repair , coupled with the use of mutants in which DSBs are formed but fail to repair , thereby trapping all DSBs , allows for analysis of DSB frequency and temporal distribution throughout meiotic progression [24–28] . Moreover , the recent identification of two new factors , XND-1 and CRA-1 , potentially modulating chromatin architecture and exerting differential control on either DSB or crossover formation between the transcriptionally silenced and highly heterochromatic X chromosome and the transcriptionally active and more euchromatic autosomes , offer a unique opportunity to identify the mechanisms linking changes in chromatin state to the regulation of the frequency and timing of DSB formation . XND-1 is an autosomally-enriched AT-hook domain containing protein proposed to be involved in regulating acetylation of lysine 5 on histone H2A ( H2AK5ac ) and the global distribution of crossovers , but it affects DSB formation only on the X chromosome [29] . CRA-1 is a NatB domain-containing protein conserved in yeast , worms , flies , zebrafish and mammals , previously shown to promote chromosome synapsis and crossover formation preferentially on the autosomes during meiosis in C . elegans [30] . NAA25 , the CRA-1 homolog in humans , has been suggested to be the non-catalytic subunit of the NatB N-terminal acetyltransferase complex [31] . However , the mechanisms of function for both XND-1 and CRA-1 remained to be determined . Here we identified changes in global histone acetylation levels throughout the C . elegans germline and provide evidence that this is mediated by CRA-1 , which maintains the levels of acetyl-CoA by associating with and antagonizing the activity of a previously uncharacterized and conserved acetyl-Coenzyme A hydrolase , ACER-1 . CRA-1 is autosomally enriched and exhibits an XND-1-dependent , tightly regulated pattern of expression within the germline , which contributes to the dynamic regulation of histone acetylation during meiotic prophase . The physiological significance of this tight regulation of histone acetylation in the germline is underscored by our findings that increased acetyl-CoA and histone acetylation levels are accompanied by increased DSB formation . High-resolution microscopy reveals that sites of early meiotic recombination events are located near chromosome axes , suggesting that DSB formation/repair may involve a tethered loop-axis mechanism in C . elegans . Taken together , XND-1 , CRA-1 and ACER-1 link metabolic functions , accurate chromosome segregation and genomic diversity via the regulation of acetyl-CoA levels , identifying a role for the regulation of global histone acetylation in modulating specific cell biological functions .
The presence of a NatB domain in CRA-1 prompted us to examine whether CRA-1 might regulate protein acetylation in the germline . Immunostaining of dissected gonads and Western blot analysis of whole worm lysates with a pan acetylation antibody revealed a decrease in protein lysine acetylation in cra-1 mutant germlines compared to wild type ( Fig . 1A-D ) . Given that histones comprise a large portion of the proteins that undergo acetylation in the cells , we proceeded to examine whether this decrease might also reflect changes in histone acetylation . Western blot analysis of whole worm lysates showed that acetylation of histones , assessed with a histone H3 pan-acetyl antibody , a histone H4 pan-acetyl antibody , and a H3K56ac specific antibody , is decreased by 25%-62% in cra-1 mutants compared to wild type ( Fig . 1D ) . This was further supported by the reduction in H3K56ac and H2AK5ac observed in whole mounted germlines of cra-1 mutants compared to wild type ( Fig . 1E-F ) . These observations suggest , first , that the pan acetylation antibody can reveal alterations in global histone acetylation , possibly because histones are a major component of the broader pool of lysine acetylated proteins identified by this reagent; and second , that CRA-1 has a role in modulating histone acetylation that is not restricted to a single lysine residue on histones , and instead affects global levels of histone acetylation . Use of the pan acetylation antibody also revealed two interesting features regarding histone acetylation in wild type . First , an enrichment for acetylation foci on autosomes compared to the X chromosomes occurs during early prophase ( transition zone to mid-pachytene; Fig . 1G ) . This suggests a higher level of histone acetylation on autosomes compared to the X chromosomes , which is consistent with both meiotic sex chromosome silencing during early prophase and the observations of autosomal enrichment for some specific histone acetylation markers [29 , 32 , 33] . Second , levels of histone acetylation change during germline progression ( Fig . 1C ) . An overall decrease in histone acetylation is observed upon meiotic entry ( transition zone ) , followed by a gradual increase as nuclei progress into the late pachytene stage . In cra-1 mutant germlines , histone acetylation is reduced from the premeiotic tip to the late pachytene stage , with the most severe decrease detected during transition zone and pachytene stages compared to wild type . Taken together , these data identify a role for CRA-1 in regulating levels of global histone acetylation in the germline . To further examine the link between changes in global histone acetylation and CRA-1 function we generated a transgenic line expressing a functional GFP tagged CRA-1 driven by a cra-1 promoter ( Figs . 2 , S1 , S2 ) . CRA-1::GFP is observed localizing in somatic and embryonic cells as well as to meiotic germline nuclei ( Figs . 2A-B , S2A and S2B ) , suggesting that the role of CRA-1 may not be limited to meiosis . This is consistent with the elevated levels of larval lethality ( 61% ) observed in cra-1 mutants [30] . The specificity of the observed CRA-1::GFP signal was confirmed by anti-GFP immunostaining of dissected gonads from transgenic worms depleted of CRA-1 by RNAi ( Fig . 2C ) , and by western blotting ( Fig . 2D ) . Analysis of CRA-1 localization during embryonic cell cycle progression does not show an obvious change of CRA-1 signal from interphase to mitotic prophase , although a signal reduction was observed from prometaphase to anaphase ( S2C Fig . ) . In the germline , CRA-1 signal is first detected in early prophase nuclei ( leptotene/zygotene stages ) , during which chromosomes reorganize spatially acquiring a crescent-shaped appearance ( Fig . 2A-B ) . CRA-1 signal increases as meiotic nuclei progress into the pachytene stage , where chromosomes are fully synapsed and crossover formation is completed . Therefore , the impact of a cra-1 mutation on histone acetylation along the gonad is consistent with the pattern of expression observed for CRA-1 , supporting the idea that CRA-1 may contribute to this dynamic acetylation . Moreover , the CRA-1::GFP transgene can also restore histone acetylation in cra-1 mutants ( S1E Fig . ) . Therefore , these data are consistent with a role for CRA-1 as a positive regulator of global histone acetylation . Immunostaining also revealed additional interesting features about CRA-1 localization . First , CRA-1 is enriched on autosomes , as indicated by the low levels of CRA-1::GFP signal on the X chromosome , identified via co-staining for H3K36me3 , a histone modification that is tightly associated with active transcription and therefore enriched on the autosomes , but mostly absent on the X chromosome during early and mid prophase [34] ( Fig . 2E ) . Second , although CRA-1 is required for SC assembly , it does not perform this function at the SC , since CRA-1::GFP does not localize to the interface of paired homologous chromosomes , where the SC is present , and instead exhibits a peri-chromosomal localization ( Fig . 2F ) . Therefore , CRA-1 is an autosomally enriched protein , not a component of the SC , and its presence in both germline and somatic nuclei suggest its roles may extend beyond meiosis . To test for a biological significance of regulating global histone acetylation in the germline , we examined the distribution of DSBs between the X chromosomes and autosomes given their different chromatin states . To do this , we analyzed RAD-51 foci in rad-54 mutants , where RAD-51 , a protein required for strand invasion/exchange during DSB repair , associates with DSB repair sites , but DSB repair is blocked and DSB-bound RAD-51 are “trapped” and can be easily scored [24 , 25 , 35] . Therefore , RAD-51 foci are used herein as a surrogate for DSBs , with the caveat that this may not represent the total number of DSBs since we cannot discard the possibility that not all DSBs , particularly in mutant backgrounds , may be processed to load RAD-51 . This analysis was coupled to co-immunostaining with the pan acetylation antibody , which allows for ease of identification of the X chromosomes , as they exhibit greatly decreased histone acetylation compared to the autosomes during early meiotic prophase ( Fig . 1G; [32] ) , and an antibody against the HORMA domain-containing protein HTP-3 to trace chromosome axes and distinguish between DSBs on different chromosomes ( Fig . 3A ) . We found that the X chromosomes exhibit lower levels of DSB formation compared to the autosomes ( Fig . 3Ai ) , with an average ratio of RAD-51 foci detected on the X chromosomes compared to the autosomes ( X/A ) of 1:9 ( Fig . 3B ) . There are five pairs of autosomes and one pair of X chromosomes in a hermaphrodite meiotic prophase nucleus . The length of the X chromosome ( ∼17% of the genome ) is close to the average length for each chromosome ( i . e . 16 . 67% of the genome ) . Therefore , approximately 44% fewer DSBs are formed on the X chromosome pair compared to the average levels of DSBs generated on an autosomal pair . Moreover , comparison of the levels of RAD-51 foci between X chromosomes and autosomes in wild type also revealed a lower level of RAD-51 foci on the X chromosomes ( 1:8 . 36 , Figs . 3D and S3ii ) . The low level of RAD-51 foci detected on the X chromosomes is not likely a result of an impenetrability to the antibody , since under the same fixation conditions , heterochromatin markers can be observed on the X chromosomes and on heterochromatic extrachromosomal arrays [36] . We extended this analysis to examine whether there are distinct time windows of DSB formation on the X chromosomes and autosomes . The window of gene expression silencing for the X chromosomes in hermaphrodite gonads was divided into four zones to score levels of RAD-51 foci on the X chromosomes and autosomes . This analysis revealed that there are no significant differences in the X/A ratios of RAD-51 foci from early to mid pachytene ( zones 2 to 4 ) ( Fig . 3C ) ( P = 0 . 5973 , by Extra sum-of-squares F test ) , during which most of the meiotic RAD-51 foci are detected in wild type . Taken together , this analysis revealed that , although DSBs form with similar kinetics across all chromosomes , the levels of DSBs are lower in the highly heterochromatic X chromosomes compared to the autosomes . The direct assessment of whether histone acetylation promotes DSB formation during meiosis in C . elegans will require the development of reagents and experimental approaches that are not currently available in this system . Specifically , recombination hotspots do not exist in this model , there are no SPO-11 antibodies or tagged SPO-11 lines , and approaches such as ChIP-Seq with the temporal resolution required for tracking early meiotic events in the multinuclear germline have not yet been established . However , to start to examine whether histone acetylation regulates DSB distribution between X chromosomes and autosomes , we utilized the triple immunostaining strategy described above and introduced the rad-54 mutation into the cra-1 mutant background where global histone acetylation is reduced . In rad-54; cra-1 double mutants , levels of RAD-51 foci on the X chromosomes are reduced and a X/A ratio of 1:12 is observed ( Figs . 3Aii , 3D , S3iii ) , corresponding to a 28% reduction in RAD-51 foci levels on the X chromosome compared to rad-54 single mutants . This suggests histone acetylation may contribute to achieving normal levels of DSB formation on the X chromosomes . This idea is further supported by the increased levels of RAD-51 foci observed on the X chromosomes following either injection of Trichostatin A ( TSA ) , an HDAC inhibitor that results in increased global histone acetylation ( 1:6 . 54; Figs . 3D , S3iv and S4A ) , or acetyl-CoA ( 1:6 . 47; Figs . 3D , S3vi and S4B ) . However , a comparison of the levels of RAD-51 foci detected on autosomes and X chromosomes revealed that while the effects of changes in global histone acetylation are more evident on the X chromosomes , the autosomes are also affected , albeit to a lesser degree ( S4C–S4D Fig . ) . This difference may reflect the inherently distinct thresholds of histone acetylation between the X chromosomes and the autosomes during early meiotic prophase . Importantly , changes in the ratios of RAD-51 foci between the X and the autosomes parallel the changes observed in histone acetylation . This is consistent with alterations in the substrate ( chromatin environment ) for DSBs instead of in the DSB formation/repair machinery per se , otherwise levels of RAD-51 foci would have been impaired to the same extent in both the X and the autosomes . Indeed , the expression levels of spo-11 , and of several other genes required for DSB repair and DNA damage response , are not altered in cra-1 mutants ( Fig . 3E ) . TSA or acetyl-CoA injections also did not affect the expression of spo-11 ( Fig . 3F ) . Moreover , histone acetylation on the generally silenced X chromosomes , assessed with the histone H4 pan-acetyl antibody , is further decreased in cra-1 mutants compared to wild type ( Fig . 3G-I ) . Taken together , our data suggests that histone acetylation may promote efficient DSB formation on both the X chromosomes and the autosomes . We next assessed how changes in global histone acetylation might affect the timing of meiotic DSB formation . Immunostaining of cra-1 mutant germlines had previously shown elevated levels of RAD-51 foci upon entrance into pachytene that remained elevated throughout late pachytene compared to wild type ( [30]; Fig . 4A-B ) . Analysis of xnd-1 mutants , where H2AK5ac is increased , revealed an increase in the levels of RAD-51 foci at transition zone and early pachytene ( zones 1 and 2 , respectively ) compared to wild type ( Fig . 4A-B ) . Moreover , levels of RAD-51 foci peaked at transition zone in xnd-1 compared to early- to mid-pachytene in wild type ( Fig . 4C ) . This observation suggests that DSBs might be formed earlier in xnd-1 mutants compared to wild type , consistent with [37] . However , elevated levels of RAD-51 foci are still observed during early meiotic prophase ( zones 1 and 2 ) in xnd-1;cra-1 ( RNAi ) mutants . Moreover , TSA treatment does not affect the kinetics of RAD-51 foci along meiotic prophase compared to the control ( Fig . 4B ) . These data indicate that changes in global histone acetylation do not affect the timing of DSB formation , which instead might be regulated by a separate function exerted by XND-1 . To assess how changes in global histone acetylation might affect the total number of DSBs formed during meiosis , a time course analysis of RAD-51 foci was performed for mutants in a rad-54 background ( Fig . 4D ) . Strikingly , DSB formation is significantly impaired in rad-54; xnd-1 mutants , with the levels of RAD-51 foci at the late pachytene stage reduced by 50% compared to rad-54 single mutants . The levels of RAD-51 foci are also reduced on the autosomes in xnd-1 mutants ( 35% reduction at mid-pachytene , zone 4 ) compared to wild type ( S4C Fig . ) . This data shows that XND-1 is required for efficient DSB formation on both the X chromosomes and the autosomes . Analysis of rad-54; cra-1 double mutants and rad-54; xnd-1; cra-1 ( RNAi ) triple mutants also revealed reductions in the levels of RAD-51 foci ( e . g . 17% and 28% , respectively , at the mid-pachytene stage; Fig . 4D ) . Moreover , TSA injection increased the levels of RAD-51 foci during mid pachytene by 15% ( P = 0 . 0026 ) and late pachytene by 8% ( P = 0 . 077 ) , compared to control rad-54 worms ( Fig . 4D ) . While the possibility that DSBs are shunted to a non-RAD-51-mediated repair pathway in these mutants cannot be completely eliminated , the simplest explanation for these data is that they indicate a change in DSB levels . Taken together , these data suggest that changes in global histone acetylation alter the levels , but not the timing , of meiotic DSB formation on both the X and the autosomes . Our findings that meiotic DSBs are generated on the X chromosomes at lower levels ( 56% ) compared to the autosomes , and that histone acetylation may promote DSB formation , are consistent with the idea that chromatin structure plays an important role in controlling DSB formation . Surprisingly , co-immunostaining of RAD-51 and acetylated lysine in rad-54 mutants shows that RAD-51 foci do not colocalize with the strong acetylation foci during meiosis ( Fig . 5A ) . Superimposition of 200 RAD-51 foci captured from different nuclei at early pachytene shows a greatly reduced AcK staining on the RAD-51 sites ( Fig . 5B ) . Interestingly , both acetylation signals and an active transcription marker , CTD ser2-phosphorylated RNA polymerase II ( pSer2 ) , exhibit perichromosomal enrichment , flanking the DAPI signal and away from chromosome axes marked by HTP-3 , suggesting that transcriptionally active genes may be localized at the tips of the chromatin loops ( Fig . 5C-D ) . Therefore , our observation that RAD-51 foci do not co-localize with AcK suggests that DSB formation and/or the early stages of meiotic DSB repair take place close to chromosome axes . This is further supported by measurements of the distances between RAD-51 foci and the axes on autosomes and X chromosomes during early meiotic prophase ( transition zone and early pachytene stages ) ( Fig . 5E ) . RAD-51 foci were observed in close proximity to chromosome axes during early meiotic prophase in both wild type and rad-54 mutants . A similar result was obtained measuring distances of replication protein A ( RPA ) , which binds to single-stranded DNA prior to RAD-51 following DSB end resection , in brc-2 mutants ( Fig . 5F ) . Importantly , this proximity to axes was not observed for RAD-51 foci resulting from γ-irradiation ( γ-IR ) induced DSBs in spo-11 mutants ( Fig . 5G ) , showing that the localization of RAD-51 foci close to chromosome axes is specific to endogenous DSB formation during early meiotic prophase . Furthermore , the distribution of DSBs generated by γ-IR on autosomes and X chromosomes , is also different from the distribution of endogenously produced DSBs . A X/A ratio of RAD-51 foci close to 1:5 is observed in irradiated-nuclei ( P = 0 . 72 ) ( Fig . 5H ) , indicating an even distribution of DSBs between the autosomes and X chromosomes . Taken together , these data suggest that meiotic DSBs and/or early stages of DSB repair take place in close proximity to meiotic chromosome axes . This can be ascribed either to a “tethering” of loops to chromosome axes for DSB formation/repair as proposed in mouse and yeast [10–12] or to the formation of DSBs directly in regions of the loops very proximal to chromosome axes . We favor the former possibility , and note that although the RAD-51 foci did not co-localize with highly acetylated/transcribed regions , which our analysis suggests is located at the tips of the loops , DSB formation per se has been shown to induce chromatin silencing locally [38] . In fact , the γ-IR induced DSBs in spo-11 mutants also exhibited reduced acetylation signal at the sites of RAD-51 foci ( Fig . 5B ) . Thus , our data suggest that the “tethering” model and DSB induced chromatin silencing explain the reduced acetylation signal at RAD-51 sites . Further assessment of these chromatin features will require the development of new technologies for a high-resolution and stage-specific assessment of chromatin architecture and organization in the germline . To understand the mechanism by which CRA-1 regulates global histone acetylation and meiotic DSB formation in C . elegans , we applied a proteomic approach to search for potential CRA-1 binding proteins . CRA-1::GFP was immunopurified from lysates of CRA-1::GFP transgenic worms and copurified proteins were indentified by mass spectrometry ( see S1 Text ) . A list of identified proteins was generated following subtraction of proteins found in the control purification ( S1 Table in S1 Text ) . While we did not identify a histone acetyltransferase by this approach , interestingly , we found ACER-1 ( ORF C44B7 . 10 ) as a CRA-1 interacting protein . ACER-1 is a protein with homologs present from bacteria to humans ( S5A Fig . ) and it is a putative acetyl-CoA hydrolase/transferase ( Fig . 6A ) . The interaction between CRA-1 and ACER-1 is further supported by co-expression in 293T cells , followed by immunoprecipitation and Western blot analysis ( Fig . 6B ) . Moreover , depletion by RNAi of ACER-1 in CRA-1::GFP transgenic worms results in the aggregation of CRA-1::GFP in the nucleus ( Figs . 6C , S5B ) , suggesting a direct relationship between CRA-1 and ACER-1 in C . elegans . To determine how ACER-1 and CRA-1 function together to regulate acetyl-CoA , histone acetylation , and DSB formation , we first examined the localization of ACER-1 . Immunostaining with an ACER-1-specific antibody revealed that ACER-1 localizes to both germline and somatic cells , but is germline-enriched ( S6A–S6C Fig . ) , consistent with the enrichment of its mRNA detected in the germline ( NextDB; http://nematode . lab . nig . ac . jp/db2/index . php ) . ACER-1 is present both in nuclei and the cytoplasm , showing a cytoplasmic enrichment at the premeiotic tip and early meiotic prophase , and an even distribution between the nucleus and the cytoplasm in late meiotic prophase and somatic cells ( Figs . 6D-E , S6C-S6D ) . The presence of both ACER-1 and CRA-1 in the nucleus is consistent with the idea that these two proteins may interact in the nucleus to regulate histone acetylation levels . We next measured the levels of acetyl-CoA in worm lysates from wild type , acer-1 and cra-1 mutants . We found that the level of acetyl-CoA is significantly increased in acer-1 mutants ( P = 0 . 006 by two-tailed unpaired t test; Fig . 6F ) , indicating that ACER-1 acts as an acetyl-CoA hydrolase . Moreover , levels of acetyl-CoA are significantly reduced in cra-1 mutants ( P = 0 . 037 ) , and are elevated in acer-1;cra-1 double mutants , indicating that CRA-1 may antagonize ACER-1 acetyl-CoA hydrolase activity . Western blot analysis and immunostaining , using either the pan acetylation or histone H4 pan-acetyl antibodies , revealed that levels of histone acetylation are increased in both acer-1 and acer-1;cra-1 mutants , and reduced in cra-1 mutants ( Fig . 6G-H ) . These data suggest that CRA-1 can maintain the levels of acetyl-CoA in C . elegans by antagonizing ACER-1 activity , thus promoting global histone acetylation . Finally , levels of RAD-51 foci are increased by 32% and 24% on the X chromosomes in either rad-54; acer-1 or rad-54; acer-1; cra-1 mutants , respectively , compared to rad-54 single mutants ( X/A ratio is approximately 1:6 and 1:7 , respectively; Fig . 3D , S3viii-x ) , consistent with the antagonistic functions of CRA-1 and ACER-1 in regulating histone acetylation . Importantly , increased histone acetylation is observed on the X chromosomes in acer-1 mutants compared to wild type ( Fig . 3I ) , and the increased levels of RAD-51 foci are not due to altered spo-11 expression in acer-1 mutants ( Fig . 3F ) . Moreover , the transcriptional silencing of the X chromosomes during early meiotic prophase is not affected by the altered histone acetylation in both cra-1 and acer-1 mutants , as assessed by immunostaining of RNA polymerase II and its transcriptionally active form ( pSer2 ) , and by quantitative RT-PCR analysis of six germline-specific genes located throughout different regions of the X chromosomes ( S7 Fig . ) . Therefore , these data further support a role for regulation of global histone acetylation in promoting DSB formation on the X chromosomes and uncover a link between metabolism and meiotic DSB formation . To further understand the mechanics by which CRA-1 , ACER-1 and XND-1 regulate histone acetylation , and potentially meiotic DSB formation , we took advantage of the power of genetic and cytological analysis that can be combined in C . elegans . First , immunostaining with an anti-XND-1 antibody revealed that XND-1 expression and localization are not affected in cra-1 mutants ( S8 Fig . ) . However , the reciprocal experiment revealed that CRA-1::GFP expression was remarkably increased in both premeiotic tip and transition zone nuclei in xnd-1 mutants compared to wild type ( Fig . 7A-B ) . Second , altered dynamics of histone acetylation were also observed correlating with the altered CRA-1::GFP expression pattern in xnd-1 mutants . While a reduction of acetylation levels was observed upon meiotic entry in wild type , no reduction was observed in xnd-1 mutants ( Fig . 7C ) . Quantification of acetylation foci showed that histone acetylation is increased in the xnd-1 mutant both in premeiotic tip and transition zone nuclei compared to wild type ( Fig . 7C-D ) ( P<0 . 0001 ) , consistent with the altered pattern of expression observed for CRA-1 in xnd-1 mutants . These data suggest that XND-1 acts upstream of CRA-1 to regulate histone acetylation . This is further confirmed by the analysis of H2AK5ac , previously shown to be increased in xnd-1 mutants compared to wild type [29] , but whose levels are reduced in both cra-1 ( RNAi ) and xnd-1; cra-1 ( RNAi ) worms ( Fig . 7E ) . Therefore , XND-1 is responsible for the suppression of CRA-1 expression at the premeiotic tip and early stage of meiotic prophase , and loss of this suppression is accompanied by increased histone acetylation upon meiotic entry . Interestingly , depletion of CRA-1 or ACER-1 in rad-54;xnd-1 double mutants can either further reduce ( X/A ratio of 1:72 ) or increase ( 1:31 ) the efficiency of DSB formation , respectively , on the X chromosome compared to rad-54;xnd-1 mutants ( 1:40 ) ( Figs . 3D , S3xi-xiii ) . These observations suggest that changes in global histone acetylation can still affect DSB formation on the X chromosomes in xnd-1 mutants , supporting the idea that histone acetylation and XND-1 regulate DSB formation in different ways . In fact , HIM-5 , a highly basic protein with no known orthologs outside C . elegans and no apparent effect on histone acetylation , is required for DSB formation on the X chromosomes , and chromosomal deposition of HIM-5 requires XND-1 activity suggesting that XND-1 might regulate DSB formation through HIM-5 , by means yet to be determined [37] . Thus , although mutation of xnd-1 promotes histone acetylation , it causes the loss of HIM-5 on the chromosomes , which results in a deficiency of DSB formation . Taken together , these studies place XND-1 as a negative regulator of CRA-1 , which interacts with and antagonizes ACER-1 , an acetyl-CoA hydrolase , thereby providing a mechanism for regulating histone acetylation via modulation of acetyl-CoA levels ( Fig . 7F ) .
Meiotic DSBs are not uniformly distributed along the genome . There are discrete regions called hotspots that are preferred for DSB formation . However , it is still not clear how these discrete regions become “hot” . Chromatin state has been considered an important factor affecting DSB formation . H3K4me3 , a modification that is enriched at gene promoter regions , has been suggested to play a conserved role in promoting DSB formation [12 , 15] . However , analysis of H3K4me3 enrichment and DSB formation in yeast showed little correlation [18] . In mice , levels of PRDM9-dependent H3K4me3 are much lower than PRDM9-independent H3K4me3 in promoter regions [17] . These observations suggest that additional modifications or factors might be required to direct DSB formation . The distribution of DSBs along the genome at high-resolution is not known in C . elegans . However , the fact that the X chromosomes present a repressive chromatin environment where transcription is silenced during meiotic prophase provides a great opportunity to study the relationship between chromatin architecture/state and DSB formation . It has been previously suggested that a specific chromatin state is required for DSB formation , and that the timing of DSB formation might be different for X chromosomes and autosomes [39] . Here our data indicates that the timing of DSB formation is generally the same between the X chromosomes and the autosomes . DSB formation does not occur earlier on the X chromosomes . Interestingly , a lower frequency of DSB formation was observed on the X chromosomes , suggesting that the chromatin structure on autosomes is preferred for DSB formation . However , although the X chromosomes are silenced , DSBs still form at 56% of the level observed on the autosomes in rad-54 mutants , indicating that the frequency of DSB formation does not highly correlate with the levels of transcription ( i . e . chromatin state ) . Moreover , changes in global histone acetylation remarkably parallel the altered frequency of DSB formation on the X chromosomes . These and other data presented in this study lead us to propose a “sentinel chromosome” model in which the effects of altered histone acetylation on DSB formation may be detected more robustly on the X chromosomes due to the inherently lower levels of histone acetylation they exhibit in early prophase when DSB formation takes place . The X chromosome therefore behaves as the proverbial canary in a coal mine serving as a barometer of the alterations that can result from perturbing the regulation of histone acetylation . We propose that the sentinel chromosome model works as follows: DSB formation requires a minimal threshold of histone acetylation on the chromatin , which is still present in the generally silenced X chromosomes . However , a global increase in histone acetylation can promote an increase in the number of DSB sites in both autosomes and the X chromosomes , but while these are already more prevalent on autosomes due to their euchromatic state , they are rare in the highly heterochromatic X chromosomes . Therefore , this provides a mechanism in which changes in histone acetylation can differentially affect the distribution of DSBs on X chromosomes versus autosomes ( Fig . 7F ) . A deficiency in DSB formation on the X chromosomes has been observed in the context of various different mutants [29 , 37 , 39 , 40] , which is consistent with the idea that the silenced X chromosomes face more challenges for DSB formation . The main barrier for DSB formation on the X chromosome might be the inaccessibility of its DNA for the recombination initiation machinery due to its highly heterochromatic architecture . Therefore , factors that promote an open chromatin state may be required for DSB formation on the X chromosomes more often than on the autosomes , due to the open property of the autosomes . Consistent with this , we found that an xnd-1 mutation results not only in the absence of DSBs on the X chromosomes , but also a reduction of DSBs on the autosomes , although it remains to be examined whether XND-1 directly or indirectly regulates DSB formation ( S4C–S4D Fig . ) . Acetyl-CoA is an important metabolic product and is used for the generation of citrate , an intermediate of the tricarboxylic acid ( TCA ) cycle , which generates energy from carbohydrates , fats and proteins in mitochondria . Because acetyl-CoA cannot be directly transferred through the membrane , there are distinct pools of acetyl-CoA in the cells: a mitochondrial pool and a nucleocytoplasmic pool . Acetylation of histones and many other proteins depends on the nucleocytoplasmic pool of acetyl-CoA . In mammals , acetyl-CoA can be transferred from the mitochondria to the cytoplasm in the form of citrate through ATP-citrate lyase ( ACL ) . It has been shown that under high nutrient conditions , acetyl-CoA produced by the ACL is the predominant source for histone acetylation [41] . In C . elegans , there are homologs of mammalian ACL , so the mitochondrial acetyl-CoA most likely can be transferred to the cytoplasm , albeit this remains to be shown . In this study , we detected the combined pools of acetyl-CoA , and we found that the total level of acetyl-CoA is altered in cra-1 and acer-1 mutants . Based on the observation that both CRA-1 and ACER-1 are present in the nucleus and that depletion of ACER-1 causes aggregation of CRA-1 in the nucleus , we would expect that CRA-1 and ACER-1 might directly regulate the nucleocytoplasmic pool of acetyl-CoA . Acetyl-CoA production is nutrient-dependent . In proliferating cells , glucose and glutamine are the primary carbon sources that contribute to the production of citrate . Excess citrate can be used for the generation of nucleocytoplasmic acetyl-CoA , which in turn promotes histone acetylation and gene transcription [42] . Acetyl-CoA thus acts as a key sensor of metabolic states and controls gene transcription . However , in multicellular organisms , cells are differentiated with specific functions , and metabolic state might not necessarily be associated with gene transcription . This requires fine regulation of the nucleocytoplasmic acetyl-CoA level . Therefore , the interaction between CRA-1 and ACER-1 may act as an important control of acetyl-CoA and histone acetylation . In this study we showed a link between the levels of acetyl-CoA , histone acetylation and DSB formation on chromosomes , with a remarkable impact on the X chromosomes . Although the effects are weaker on the autosomes , a shift of DSB-preferred sites might be induced . The increase and shift of DSB sites may provide more potential sites for COs , thus promoting genetic diversity among the corresponding offspring . This therefore supports a connection between metabolic state and the genetic diversity that stems from the DSB-dependent meiotic CO exchanges . Interestingly , it has been shown that under stress conditions , worms that normally reproduce through self-fertilization can increase the generation of male animals , as well as their outcrossing ability , to promote adaptation [43] . Thus , low frequencies of DSB formation on the X chromosomes under low nutrient conditions may result in increased male frequencies providing for the beneficial outcome of increased outcrossing . In summary , our study identified a pathway for the regulation of global histone acetylation by modulating the levels of acetyl-CoA during meiotic prophase in C . elegans . Moreover , we link elevated levels of acetyl-CoA and histone acetylation to the promotion of DSB formation especially on the X chromosomes . Thus , we uncovered the underpinnings of a key mechanism that we propose is set in place to bypass the highly heterochromatic state of the X chromosomes ensuring they successfully undergo DSBs during meiosis , and establishing a connection between acetyl-CoA metabolism and genomic diversity .
N2 Bristol was used as the wild type strain . To generate CRA-1::GFP transgenic worms , a CRA-1::GFP construct containing the cra-1 promoter , cra-1 genomic sequence and a gfp sequence was integrated by the MosSCI ( Mos1-mediated single copy gene insertion ) transposon-mediated insertion technique into the ttTi5605 site on chromosome II , as described in [44] . To confirm that the GFP fusion construct is functional and its localization reflects endogenous CRA-1 localization , we introduced the CRA-1::GFP transgene into cra-1 mutants . The CRA-1::GFP transgene rescued the synapsis defects of cra-1 mutants , and COs were restored given that six bivalents ( pairs of DAPI-stained chromosomes attached through chiasmata ) were observed instead of the 11 to 12 univalents characteristic of cra-1 mutants ( S1A–S1B Fig . ) . Moreover , brood size and embryonic lethality were significantly , albeit not completely , restored in the rescued line ( S1C–S1D Fig . ) . This partial rescue suggests that the GFP tag may affect other functions played by CRA-1 . acer-1 ( rj15 ) mutants were generated via CRISPR-Cas9 technology as described in [45] . The guide RNA sequence targets a site near the start codon: ATGCTTTCTCGGCTTACATCTCGCTCCCTCGGAACCTCGGCTGCGTGCTCAAGA . The underlined sequence is deleted in the acer-1 mutant resulting in an out-of-frame deletion . All worms were cultured at 20°C under standard conditions as described in Brenner ( 1974 ) . The following mutations and chromosome rearrangements were used: LG I: rad-54 ( ok615 ) ; LG II: acer-1 ( rj15 ) ; LG III: brc-2 ( tm1086 ) , xnd-1 ( ok709 ) , cra-1 ( tm2144 ) ; LG IV: spo-11 ( ok79 ) ; and LG V: syp-1 ( me17 ) [8 , 25 , 29 , 30 , 46] . Transgenes: opIs263[Prpa-1::RPA-1-YFP::3’-URR] [47] . Immunostaining was performed as in [30] . Antibodies were used at the following dilutions: rabbit α-SYP-1 ( 1:200; [46] ) , guinea pig α-HIM-8 ( 1:200; [48] ) , rabbit α-ACER-1 ( 1:10000 ) , guinea pig α-HTP-3 ( 1:300; [49] ) , guinea pig α-XND-1 ( 1:500; [29] ) , rabbit α-RAD-51 ( SDIX , 1:20000 ) , mouse α-pSer2 ( H5 ) ( Covance , 1:100 ) , rabbit α-AcK ( Cell Signaling Technology , 1:10000 ) , mouse α-AcK ( Cell Signaling Technology , 1:1000 ) , rabbit α-H3K36me3 ( 1:300 ) , rabbit α-H3K56ac ( Millipore , 1:500 ) , rabbit α-H2AK5ac ( Cell Signaling Technology , 1:400 ) , chicken α-GFP ( Abcam , 1:400 ) , rabbit α-H4ac ( Active motif , 1:1000 ) , and mouse α-tubulin ( Sigma , 1:500 ) . The pan acetylation antibody was utilized both at a high ( 1:10 , 000 ) and low ( 1:1 , 000 ) dilutions . While the latter exhibited a broad distribution over chromatin , it did not allow for precise quantification of changes in signal intensity , possibly due to saturation . The 1:10 , 000 dilution was therefore utilized for quantification . The following peptide sequence was utilized for ACER-1 antibody production in rabbits: CGKSPKVVSLAEATRDIKSGDN , and antibody specificity was confirmed by immunostaining and western blot analysis ( S6A–S6D Fig . ) . The following secondary antibodies from Jackson ImmunoResearch Laboratories were used at a 1:200 dilution: α-chicken FITC , α-rabbit Cy3 , α-rabbit FITC , α-mouse FITC , α-mouse Cy5 and α-guinea pig FITC . DAPI ( Sigma , 1 μg/ml ) was used to counterstain DNA . Images presented are either partial or whole projections through 3D data stacks of nuclei . Immunofluorescence images were captured through whole nuclei at 0 . 2 μm or 0 . 05 μm intervals with an IX-70 microscope ( Olympus ) and a cooled CCD camera ( CH350; Roper Scientific ) under the control of the DeltaVision system with SoftWoRx software ( Applied Precision ) and deconvolved using a conservative algorithm with 15 iterations . Whole worm lysates were prepared from young adult worms ( 24 hours post-L4 ) by freezing and then boiling the worms in Laemmli sample buffer . Proteins were resolved by SDS-PAGE and then transferred onto polyvinylidene difluoride membranes ( Millipore , 0 . 2 μm pore size ) in transfer buffer ( 25 mM Tris-HCl pH 8 . 3 , 192 mM glycine , 20% ( v/v ) methanol and 0 . 1% ( w/v ) SDS ) . The presence of SDS ensures histones can be completely transferred onto the membrane . Antibodies were used at the following dilutions: chicken α-GFP ( Abcam , 1:2000 ) , mouse α-HA ( Sigma , 1:2000 ) , rabbit α-AcK ( 1:3000 ) , rabbit α-H3ac ( Active motif , 1:3000 ) , rabbit α-H4ac ( Active motif , 1:3000 ) , rabbit α-H3 ( Cell signaling , 1:5000 ) , rabbit α-H3K56ac ( Millipore , 1:3000 ) , mouse α-tubulin ( Sigma , 1:2000 ) , rabbit α-ACER-1 ( 1:3000 ) . Horseradish peroxidase-conjugated secondary antibodies from Jackson ImmunoResearch Laboratories were used at a 1:5000 dilution . Specific proteins were visualized with Pierce ECL Western Blotting Substrate . The relative level of acetylated histones was determined by densitometric analysis of the western blot bands ( AcK relative to H3 ) using ImageJ software . To compare the number of acetylation foci between germlines from different genotypes , gonads from every two genotypes were fixed on a single slide , either mixed together if the changes in chromosome morphology permit ease of identification of the genotypes , or fixed in separate areas of the same slide when chromosome morphology is indistinguishable between genotypes . Immunostaining was performed as described above . Images were captured and processed under the same exact conditions . At least five gonads were quantified per genotype . The average number of nuclei scored per zone for a given genotype ± standard deviation were as follow: mitotic tip , n = 56 ± 11; transition zone , n = 51 ± 8; late pachytene , n = 67 ± 13 . Quantification of RAD-51 foci along the germline was performed as in [24] . Immunostaining of C . elegans germlines was performed in age-matched ( 24 h post-L4 ) animals as described above . At least four gonads were quantified per genotype . The average number of nuclei scored per zone for a given genotype ± standard deviation were as follow: mitotic tip , n = 112 ± 13; meiotic prophase zone 1 ( transition zone ) , n = 92 ± 10; zone 2 ( early pachytene ) , n = 108 ± 19; zone 3 ( mid-pachytene ) , n = 78 ± 9; zone 4 ( mid-pachytene ) , n = 70 ± 5; zone 5 ( late pachytene ) , n = 63 ± 4 . Worms were injected at 6 h post-L4 stage , and the injected worms were fixed and immunostained at 18 hours post injection . For Trichostatin A ( TSA , Sigma ) treatment , a concentration of 10 μM TSA was used and 0 . 2% DMSO ( v/v ) was injected as control . For Acetyl-CoA ( Sigma ) injection , a concentration of 100 μM Acetyl-CoA was used , and H2O was injected as control . CRA-1::GFP transgenic worms and N2 worms were lysed by vortexing in 15ml conical tubes containing lysis buffer ( 50 mM HEPES pH7 . 4; 1 mM EGTA; 3 mM MgCl2; 300 mM KCl; 10% Glycerol; 1% NP-40; 1 mM DTT; protease inhibitor mixture ( Roche ) ) and small shards of broken glass cover slips . CRA-1::GFP and its binding proteins were immunopurified from the CRA-1::GFP worm lysates by using anti-GFP agarose beads ( MBL International ) with 2 hours incubation at 4°C . After being washed with lysis buffer , the beads were eluted with Glycine buffer ( 0 . 1 M pH2 . 5 ) . The control purification was performed by using N2 worm lysates and the same anti-GFP agarose beads . Proteins eluted from the beads were precipitated with 20% trichloroacetic acid ( TCA ) , and the resulting pellet was washed once with 10% TCA and four times with cold acetone . Worms were washed twice in M9 buffer and three times in worm lysis buffer ( 50 mM KCl; 10 mM Tris pH5; 2 . 5 mM MgCl2; 0 . 45% NP-40; 0 . 45% Tween 20; 0 . 01% gelatin; 0 . 2 μg/μl proteinase K ) , and then frozen in liquid nitrogen . Worms were then incubated at 60°C for 2 hours . Proteinase K was inactivated with heating at 95°C for 20 minutes . The lysates were centrifuged for 20 minutes at 12 , 000 rpm and supernatants were used for measuring Acetyl-CoA and either protein or peptide concentration . Levels of Acetyl-CoA were measured with the PicoProbe Acetyl-CoA Assay Kit ( BioVision ) following the manufacturer’s instructions . RNAi by microinjection was performed to deplete ACER-1 . Double stranded RNA was produced by in vitro transcription ( Ambion ) and was injected into the gonads of young adult worms ( 12 h post-L4 ) at a concentration of 1 μg/μl . The acer-1 ( RNAi ) worms examined consisted of young adults ( 24h post-L4 ) from the F1 generation . Single worm RT-PCR detection shows that acer-1 expression is only partially suppressed following RNAi depletion ( S5B Fig . ) . Feeding RNAi was used for depletion of CRA-1 as described in [50] . CRA-1 cDNA was cloned into the pL4440 feeding vector . Control RNAi was performed by feeding worms with HT115 bacteria carrying the empty pL4440 vector .
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Achieving accurate chromosome segregation is a critical outcome for any cell division process . Programmed DNA double-strand break formation is a central mechanism set in place to promote faithful chromosome segregation during meiosis . A subset of these DSBs is repaired as crossovers via reciprocal exchange of genetic information between homologous chromosomes resulting in physical attachments ( chiasmata ) between homologs , which ensure proper chromosome alignment at the metaphase plate at meiosis I , and also promote genetic diversity . How this regulation operates in the context of different chromatin architectures and accessibility , and how it is linked to metabolic pathways , is not understood . In this study , we found that CRA-1 , a NatB domain-containing protein , promotes histone acetylation by maintaining the levels of acetyl-Coenzyme A ( acetyl-CoA ) through antagonizing ACER-1 , a previously unknown and conserved acetyl-CoA hydrolase . CRA-1 is in turn negatively regulated by XND-1 , an AT-hook containing protein . We leveraged this discovery to find a connection between the levels of acetyl-CoA , histone acetylation and DSB formation . We identified a novel protein network that links the regulation of DSB formation to the modulation of global levels of histone acetylation , and revealed a link between metabolism and the regulation of DSB formation .
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[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2015
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NatB Domain-Containing CRA-1 Antagonizes Hydrolase ACER-1 Linking Acetyl-CoA Metabolism to the Initiation of Recombination during C. elegans Meiosis
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Chromosomal abnormalities , such as structural and numerical abnormalities , are a common occurrence in cancer . The close association of homologous chromosomes during interphase , a phenomenon termed somatic chromosome pairing , has been observed in cancerous cells , but the functional consequences of somatic pairing have not been established . Gene expression profiling studies revealed that somatic pairing of chromosome 19 is a recurrent chromosomal abnormality in renal oncocytoma , a neoplasia of the adult kidney . Somatic pairing was associated with significant disruption of gene expression within the paired regions and resulted in the deregulation of the prolyl-hydroxylase ELGN2 , a key protein that regulates the oxygen-dependent degradation of hypoxia-inducible factor ( HIF ) . Overexpression of ELGN2 in renal oncocytoma increased ubiquitin-mediated destruction of HIF and concomitantly suppressed the expression of several HIF-target genes , including the pro-death BNIP3L gene . The transcriptional changes that are associated with somatic pairing of chromosome 19 mimic the transcriptional changes that occur following DNA amplification . Therefore , in addition to numerical and structural chromosomal abnormalities , alterations in chromosomal spatial dynamics should be considered as genomic events that are associated with tumorigenesis . The identification of EGLN2 as a significantly deregulated gene that maps within the paired chromosome region directly implicates defects in the oxygen-sensing network to the biology of renal oncocytoma .
Cellular adaptation to changes in oxygen tension is vital for the integrity , maintenance and survival of cells . Hypoxia-inducible factor ( HIF ) , the major transcription factor of the ubiquitous oxygen-sensing pathway , is a heterodimer composed of α and β subunits [1] . While HIFβ is constitutively expressed and stable , HIFα is oxygen-labile by the virtue of the oxygen-dependent degradation ( ODD ) domain , which undergoes rapid oxygen-dependent ubiquitin-mediated destruction [2]–[5] . Thus , the stability of HIFα dictates the transcriptional activity of HIF [6] . Critical regulators of HIFα stability are the prolyl-hydroxylase domain-containing enzymes ( PHD/EGLNs ) that hydroxylate HIFα on conserved prolines within the ODD domain in the presence of oxygen [7] , [8] . Hydroxylated HIFα is recognized by the von Hippel-Lindau ( VHL ) protein . VHL is the substrate-conferring component of an E3 ubiquitin ligase called ECV ( Elongins/Cul2/VHL ) that specifically polyubiquitinates prolyl-hydroxylated HIFα for subsequent destruction via the 26S proteasome . Deregulation of HIFα regulatory proteins has been strongly associated with cancer development . Germline inheritance of a faulty VHL allele on chromosome 3p25 is the cause of VHL disease , characterized by a high frequency of clear cell renal cell carcinoma ( RCC ) , cerebellar hemangioblastoma , pheochromocytoma , and retinal angioma [9] . Inactivation of the remaining wild-type VHL allele in a susceptible cell leads to tumor formation . Somatic biallelic inactivation of VHL is also responsible for the development of sporadic clear-cell RCCs , the predominant form of adult kidney cancer [10]–[12] . Cells that are devoid of functional VHL show elevated expression of numerous hypoxia-inducible genes due to a failure to degrade HIFα . In addition to VHL , deregulation of the PHD/EGLN family of prolyl-hydroxylases have also been associated with abnormal cell growth . Development of erythrocytosis , characterized by an excess of erythrocytes , has been associated with inactivating germline mutations in PHD2/EGLN1 [13] , [14] . Pheochromocytoma , a neuroendocrine tumor of the medulla of the adrenal glands , is linked with deregulation of PHD3/EGLN3 [15] . While biallelic inactivation of VHL is found in the majority of clear cell RCCs , kidney cancer is a heterogeneous disease that can be divided into several subtypes based on morphological and cytogenetic features [16] , [17] . Chromophobe RCC and renal oncocytoma are two related kidney tumors that together account for approximately 10% of all renal masses . In contrast to clear cell RCC , VHL mutations and/or increased expression of hypoxia-inducible genes are not found in these tumor subtypes and molecular genetic defects that are associated with tumor development remain unclear . Identification of molecular genetic defects in renal oncocytoma is particularly challenging as these cells are often described as karyotypically normal and the presence of cytogenetically abnormal regions in which to search for tumor modifying genes is rare in this tumor subtype . To identify molecular defects associated with renal tumor development , we analyzed gene expression data from a variety of kidney tumors . This analysis revealed that renal oncocytoma and chromophobe RCC have a striking transcriptional disruption along chromosome 19 . While in chromophobe RCC the disruption reflected a chromosome 19 amplification , in the renal oncocytoma cells the disruption reflected the close association , or pairing , of chromosome 19q in interphase . EGLN2 located within the paired region was dramatically overexpressed in renal oncocytoma cells and was associated with the deregulation of numerous hypoxia-inducible genes including a pro-death BNIP3L . Thus , chromosome 19q pairing in renal oncocytoma unveils a unique mechanism of disrupting oxygen homeostasis via altering the expression of EGLN2 .
Gene expression profiling data derived from renal oncocytomas and chromophobe RCCs was scanned for regional increases or decreases in RNA production , which often indicate the presence of chromosomal amplifications or deletions [18]–[24] . Consistent with previous cytogenetic studies , the renal oncocytoma cells were largely devoid of transcriptional abnormalities that would reflect a DNA amplification or deletion . In contrast , losses of chromosomes 1 , 2 , 6 , 10 , and 17 are frequently found in chromophobe RCC . In our chromophobe RCC samples , these well-established chromosomal losses were strongly reflected in the gene expression profiling data ( Figure 1A ) . In addition , a transcriptional abnormality involving genes mapping to chr 19 was frequently identified in both the renal oncocytomas and the chromophobe RCCs but not other subtypes of RCC ( Figure 1A and Figure S1 ) . In renal oncocytomas , the transcriptional abnormality primarily involved the q arm of chromosome 19 , while in chromophobe RCC the abnormality involved the entire chromosome ( Figure 1A , B ) . Regional increases in overall RNA production often indicate the presence of an underlying DNA amplification . As gain of chromosome 19 has not been previously reported as a recurrent abnormality in either renal oncocytoma or chromophobe RCC , DNA copy number analysis was performed on a subset of these samples using high-density single nucleotide polymorphism ( SNP ) arrays . From the SNP data , an amplification of the entirety of chromosome 19 was detected in the chromophobe RCC samples ( Figure 1C , D ) . This whole-chromosome amplification was confirmed by fluorescence in-situ hybridization ( FISH ) using locus-specific probes that mapped to the p and q arms of chromosome 19 ( Table S1 ) . In contrast , no change in DNA copy number was detected in the renal oncocytoma samples ( Figure 1C , D ) . As a positive control for the DNA copy number analysis , only oncocytoma ( ON ) samples derived from female patients were examined , and a relative gain of the X chromosome was clearly detected in these samples ( Figure 1C ) . To determine the status of chromosome 19 in more detail in the renal oncocytoma cells , this chromosome was evaluated further using a panel of FISH probes . Two distinct and well-separated FISH signals , typical of diploid cells in interphase , were frequently observed when probes specific to the chr 19p arm were used ( Figure 2 and Table S2 ) . In contrast , a single , large FISH signal ( singlet ) or two FISH signals that were in close proximity ( proximal doublet ) were frequently observed when probes specific to the chr 19q arm were used . Approximately 35% of cells examined contained the singlet signal , while an additional 18% of cells contained proximal doublets ( Table S2 and data not shown ) . Semi-quantitative image analysis was used to examine the characteristics of the large FISH singlet ( Figure 2B ) . This analysis demonstrated that the size of the singlet FISH signal was on average 1 . 5-fold larger than the size of two well-separated 19q FISH signals ( P = 0 . 02 ) . This large signal was observed using multiple probes directed against the q arm of the chromosome , including centromeric and telomeric probes ( Figure 2C , E ) . The large FISH singlet had striking similarities to the FISH signals observed in studies of somatically paired chromosomes [25]–[27] . Somatic pairing refers to the close association of homologous chromosomes and is typically associated with chromosomes in meiotic prophase . However , somatic pairing has also been observed in interphase in normal human cells and some tumor cells [26] , [28]–[32] . The presence of a large FISH singlet reflects the overlapping FISH signals generated from two chromosomal regions in very close proximity [26] , [27] . The lack of evidence for a DNA copy number change coupled with the presence of large FISH singlets and proximal doublets using multiple locus-specific probes , suggested that chr 19q was somatically paired . To confirm that the q arms of chr 19 were somatically paired in the renal oncocytoma cells , the p and q arms of chr 19 were visualized simultaneously using whole-arm chromosome painting ( WCP ) . Using this approach , two distinct p arms , typical of diploid cells in interphase , were frequently observed in renal oncocytoma cells ( Figure 2G , H and Table S2 ) . However , the majority of cells contained a single q-arm signal that was located proximal to the two p-arm signals . While the diffuse nature of the WCP prevented the quantification the fluorescence signal , this pattern is consistent with the locus-specific FISH analysis and further indicates that the q arms of the chromosomes are in close proximity or are paired in these cells . The changes in gene expression that accompanied the somatic pairing suggested that deregulation of a gene , or multiple genes , associated with tumor development mapped within the paired chr 19q region . As deregulation of the oxygen-sensing network is a common event in other types of sporadic renal cell carcinomas , genes associated with HIF regulation and that mapped to chr 19q were identified from the Entrez Gene database and tested for expression defects ( see Materials and Methods ) . We also identified additional genes that were related to kidney-cancer via additional literature searching ( Table S3 ) . Both analyses identified EGLN2/PHD1 as a possible candidate gene in this region . To verify that the prolyl-hydroxylase EGLN2/PHD1 was significantly deregulated in renal oncocytoma cells , the level of EGLN2 protein was evaluated in these tumors ( Figure 3A , B ) . Analysis of matched oncocytoma-normal tissue pairs revealed a dramatic increase in the level of EGLN2 in the oncocytoma tumors versus the level observed in corresponding normal tissue . Higher expression of EGLN2 was also observed in 2 of 3 chromophobe RCCs examined ( Figure S2 ) . These results are in contrast to the EGLN2 levels found in clear cell RCC . Consistent with the gene expression data , virtually no EGLN2 protein was detected in patient-derived clear cell RCC samples , while low basal amounts of EGLN2 were visualized by Western blot analysis in the matched normal samples ( Figure 3 A , C ) . EGLN2 is one of three prolyl-hydroxylases known to post-translationally modify HIFα , which is required for VHL-mediated destruction of HIFα . To address whether increased expression of EGLN2 influenced the binding and ubiquitination of HIF-1αODD via VHL , in vitro translated 35S-labeled HA-VHL and in vitro translated unlabeled Gal4-HA-HIF-1αODD were mixed in extracts in which EGLN2 was enriched ( see Materials and Methods ) . Enrichment of EGLN2 led to an increased association of VHL to the wild-type ODD , but not to a mutated ODD in which a proline residue critical for VHL binding was changed to an alanine ( P546A ) ( Figure 3D ) . In addition , an in vitro HIF-1αODD ubiquitination assay was performed to determine whether the increased VHL-HIF-1αODD association led to increased HIF-1αODD ubiquitination . Increased levels of EGLN2 resulted in a dose-dependent increase in VHL-mediated HIF-1αODD ubiquitination ( Figure 3E ) . These results suggest that overexpression of EGLN2 in oncocytoma could further decrease the level of HIFα below the level observed in normal tissue . In clear cell RCC , an increase in HIFα due to functional inactivation of VHL induces a transcriptional program that mimics cellular exposure to hypoxic conditions . In contrast , in the renal oncocytoma , the functional effects of increased expression of EGLN2 would be to decrease HIFα levels . To examine the cellular effects of decreased HIFα , we re-evaluated previously published data that measured HIF-1 DNA-binding activity , HIF-1α protein levels , and HIF-1β protein levels in cells exposed to hypo- and hyper-oxygenated conditions [6] . Normoxic conditions in the kidney cortex is estimated to be 3–5% oxygen [6] . Induction of a hypo-oxygenated condition was associated with a significant increase in HIFα and HIF activity levels ( Figure 4A ) . Specifically , a six-fold decrease in oxygen concentration ( 3% to 0 . 5% oxygen ) resulted in approximately a four-fold increase in HIF-1α levels ( 2 . 5 to 9 . 8 densitometry units ) . Further , we noted that HIF-1α levels change in an analogous manner upon induction of hyper-oxygenated conditions: a six-fold increase in oxygen concentration ( 3% to 18% oxygen ) results in greater than a three-fold decrease in HIF-1α levels ( 2 . 5 to 0 . 75 densitometry units ) . The association between decreased HIF-1α and hyper-oxygenated conditions is easier to evaluate if the HIF dose-response data is plotted on a log-log scale rather than a linear-linear scale ( Figure 4B ) . The log-log transformed data follow a straight line , indicating that HIFα level and oxygen concentration follow a power-law relationship ( i . e . , f ( x ) = axk ) , rather than an exponential relationship ( i . e . , f ( x ) = kax ) . The biological implications of the power-law relationship is that an n-fold change in oxygen concentration leads to a proportional n-fold change in HIF-1α levels and HIF activity ( Figure S3 ) . Moreover , these results demonstrate that while increases in HIF-1α are associated with hypo-oxygenated conditions , decreases in HIF-1α are associated with hyper-oxygenated conditions . To determine whether EGLN2 overexpression is inducing a HIF-mediated hyperoxic cell response in the renal oncocytoma cells , the expression pattern of several known HIF target genes were examined in the renal oncocytoma cells and , for comparison , in clear cell RCC [33] . Consistent with VHL defects present in the clear cell RCC , gene set enrichment analysis revealed a significant up-regulation of the HIF-1 target genes in clear cell RCC ( P = 0 . 0001; Figure 4C ) . Notable up-regulated genes included carbonic anhydrase IX ( CA9 ) , ferroxidase ( CP ) , vascular endothelial growth factor A ( VEGFA ) , and glucose transporter ( GLUT1 ) . However , in the renal oncocytoma cells , a distinct population of HIF-target genes were significantly down-regulated ( P = 0 . 01; Figure 4D ) . Specifically , the HIF-target genes heme oxygenase 1 ( HMOX1 ) , enolase 1 ( ENO1 ) , and Cbp/p300-interacting transactivator ( CITED2 ) were significantly down-regulated , but genes such as CA9 , VEGFA , and GLUT1 were not . In addition , the recently identified tumor suppressor BNIP3L is downregulated three-fold in the renal oncocytoma cells ( Figure 4E ) . BNIP3L is an oxygen-regulated member of the Bcl-2 family ( Figure S4 ) . BNIP3L is a pro-death gene ( induces features of apoptosis , necrosis and autophagy ) and knockdown of this gene is sufficient to convert non-tumorigenic cell lines into tumorigenic lines in xenograft studies [34]–[36] . In support , while hypoxia mimetic treatment significantly induced the expression of BNIP3L , HMOX1 , ENO1 , and CITED2 ( Figure 5A , right panel and Figure S5 ) , ectopic transient expression of EGLN2 under physiologic hypoxia ( cyclical 0–7% oxygen exposure [37] ) was associated with reduced level of expression of these genes in comparison to cells transfected with empty plasmid ( Figure 5 and Figure S5 ) . These results demonstrate that over expression of EGLN2 can downregulate HIF1 responsive factors , such as BNIP3L . Moreover , while up-regulation of HIF-target genes such as VEGFA are associated with the development of clear cell RCC , these results suggest that down-regulation of distinct subset of HIF-target genes are associated with the development of renal oncocytomas .
A proper oxygen-sensing response is vital to the maintenance of normal cellular functions . Deregulation of HIF , the principal driver of the adaptive response to hypoxia , is associated with the pathogenesis of several diseases , including cancer . While the hypoxic tumor microenvironment - by the virtue of the ubiquitous oxygen-sensing pathway - results in modulation of HIF activity , loss-of-function mutations in a growing list of tumor suppressor genes also can affect HIF function . Mutations in PTEN , PML , TSC , and VHL have been identified in tumor cells that result in the deregulation of HIF via multiple distinct mechanisms involving Akt/PI3K , mTOR and the ubiquitin pathway . Emerging evidence now implicates cancer-causing mutations that directly impinge on EGLNs . For example , mutations in succinate dehydrogenase ( SDH ) result in the cytosolic accumulation of succinate , which inhibits EGLNs , leading to the stabilization and activation of HIF-1α [38] , [39] . Inactivating germline mutations in EGLN1 have been identified to cause erythrocytosis [13] , [14] and deregulation of EGLN3 has been linked to the development of pheochromocytoma , a neuroendocrine tumor of the adrenal glands [15] . In this study , we reveal somatic pairing of chr 19q as a recurrent cytogenetic abnormality in renal oncocytoma that results in dramatic changes in transcription within the paired region . The functional consequence of chromosome joining is formally unknown but it is may disrupt chromatin structure causing the juxtaposition of cis and trans regulatory regions that modulate the transcription of a large set of genes . The identification of EGLN2 as a significantly deregulated gene that maps within the paired chr 19q region directly implicates defects in the oxygen-sensing network to the pathobiology of renal oncocytoma . These results suggest that in addition to numerical and structural chromosomal abnormalities , somatic pairing should be considered as a chromosomal event that associates with tumorigenesis . Although the loss of EGLN2 does not lead to decreased HIF1α accumulation , perhaps due to the compensatory activity of EGLN3 , the data from this study suggest that overexpression of ELGN2 leads to decreased HIF1 levels . More recently , an E3 ubiquitin ligase called Siah2 was identified to target EGLN2 for ubiquitin-mediated destruction and thereby revealing another level of HIF regulation [40] . The activity of Siah2 is induced under physiologic hypoxia ( <10% oxygen ) , resulting in reduced levels of EGLN2 and stabilization of HIF-1α . The present findings suggest that the overexpression of EGLN2 via somatic pairing is sufficient to counteract the suppressive activity of Siah2 under physiologic hypoxia . Under hyper-oxygenated conditions ( 21% oxygen; frequently used as experimental normoxia ) , Siah2 activity is attenuated via a yet-defined mechanism , resulting in the increased abundance of EGLN2 and concomitant reduction in the level of HIF-1α [40] . The ectopic expression of EGLN2 under 21% oxygen did not result in further diminution of HIF-target gene expression ( data not shown ) , which is likely due to the fact that endogenous EGLN2 is highly abundant or that every available EGLN2 is already activated under hyper-oxygenated conditions . HIF-regulated genes are involved in many physiological processes including angiogenesis , metabolism , cell proliferation , survival , and apoptosis . As such , disruption in the regulation of HIF may affect several regulatory pathways that contribute to the transformation of normal cells into cancer cells . Evasion of apoptosis is one of the hallmark features of cancer cells and represents a key oncogenic event . BNIP3L is a regulator of p53-dependent apoptosis and silencing of BNIP3L has been associated with enhanced tumorgenicity and reduced apoptotic response [36] . We show here that BNIP3L is one of several HIF-responsive genes governed , in part , by EGLN2 . Therefore , we propose that the downregulation of BNIP3L is the result of chromosome-pairing induced upregulation of EGLN2 and that downregulation of BNIP3L contributes to the inhibition of apoptosis to facilitate oncocytoma cell survival and growth . The disruption of HIF activity has been associated with kidney cancer related to VHL disease , sporadic clear cell RCC , and hereditary papillary RCC [38] , [41] , [42] . The present study reveals deregulation of the oxygen-sensing response in renal oncocytoma , as well as chromophobe RCCs ( which display DNA amplification mediated up-regulation of EGLN2 ) and thereby supporting the dysfunction of HIF pathway as a common and perhaps central theme in the pathogenesis of kidney cancer .
Single-color expression profiles were generated using the HG-U133 Plus 2 . 0™ chipset ( Affymetrix , Santa Clara , CA ) from renal oncocytoma ( n = 10 ) , chromophobe RCC ( n = 10 ) , and nondiseased kidney ( n = 12 ) samples as described [43] . The gene expression data can be obtained at the Gene Expression Omnibus ( GSE8271 and GSE7023 ) . Analysis was performed using BioConductor version 2 . 0 software . Data preprocessing was performed using the RMA method as implemented in the affy package and using updated probe set mappings such that a single probe set describes each gene [44] , [45] , [46] . Chromosomal abnormalities were predicted using the comparative genomic microarray analysis ( CGMA ) method as implemented in the reb package [47] . Briefly , for each measured gene , the gene expression value was normalized such that the average gene expression value in the nondiseased samples was subtracted from the tumor-derived gene expression value . A Welsh's t-test was applied to the relative gene expression values that mapped to each chromosome arm . For the smoothing curve , the normalized expression values derived from genes mapping to chromosome 19 were replaced by a summary score that comprised a running two-sided t-test statistic using window sizes of 61 , 245 , and 611 ( representing 5% , 20% , and 50% of the length of the chromosome ) . The results of the three smoothing curves were averaged . To identify HIF-interacting genes , the Entrez Gene database ( http://www . ncbi . nlm . nih . gov/sites/entrez ) was searched using the search string ‘ ( “HIF” or “VHL” ) and “19”[chr] and “homo sapiens”[orgn]’ . Differentially expressed genes were identified using a two-sided t-test . For HIF target gene analysis , 36 known HIF-responsive genes identified in Maynard et al . were isolated [33] . Enrichment of up- and down-regulated genes in the HIF target gene set was performed by comparing differences in the expression level ranks between HIF target gene set to the results of 10 , 000 randomly generated 36-gene sets . Ranks were based on tumor versus normal expression comparisons as implemented in the limma package [48] . SNP allele calls were generated using the GeneChip Mapping 100 K Set™ ( Affymetrix , Santa Clara , CA ) according to the manufacturer's supplied protocol . Image quantification was performed with a GeneChip Scanner 3000 and the resulting data was processed using GCOS 1 . 4 ( Affymetrix , Santa Clara , CA ) with default analysis settings . Allele calls were generated using GTYPE 4 . 0 ( Affymetrix , Santa Clara , CA ) with a confidence threshold set at 0 . 25 . Raw copy numbers in log2-transformed format ( non-paired reference and test samples ) were exported from the CNAG version 2 . 0 ( Affymetrix , Santa Clara , CA ) software using normal references downloaded from Affymetrix ( http://www . affymetrix . comccnt_reference_data ) . DNA copy number changes were visualized by data smoothing in which raw copy number values were replaced by a summary score that comprised a running 1-sided t-test statistic with window size set to 31 , where each SNP probe along with 15 5′ SNPs and 15 3′ SNPs were included in the window . DNA copy number data can be obtained at the Gene Expression Omnibus ( GSE8271 ) . Bacterial artificial chromosomes ( BACs ) RP11-157B13 ( 19p12 ) , RP11-1137G4 ( 19p13 . 3 ) , RP11-15A1 ( 19q13 . 31 ) were obtained from the Children's Hospital Oakland Research Institute ( http://bacpac . chori . org ) and BAC CTC-429C10 ( 19q13 . 41 ) was purchased from Invitrogen ( Invitrogen Corporation , Carlsbad , CA ) . These clones were labeled with either SpectrumGreen or SpectrumOrange ( Abbott Molecular Inc , Des Plaines , IL ) by nick translation and applied to tissue touch preps of oncocytoma samples as described [49] , with the exception that slides were counterstained with VECTASHIELD ( Vector Laboratories , Inc . Burlingame , CA ) anti-fade 4′ , 6-diamidino-2-phenylindole ( DAPI ) . Telomere-specific DNA probes , the chr 1 , 5 , 19 alpha satellite probe , and the arm-specific paints were purchased from Q-BIOgene ( MP Biomedicals , Solon , OH ) . FISH was performed using these probes according to the manufacturer's supplied protocol . As the alpha satellite probe cross-hybridizes to chromosome 1 and chromosome 5 , in all studies chromosome 19 was co-labeled with a probe that maps distal to the centromere , RP11-157B13 ( 19p12 ) . In addition , analysis of the centromeric probe on the metaphase spreads of control cells revealed that hybridization to chromosome 1 resulted in a significantly brighter signal ( data not shown ) . These hybridization characteristics allowed the discrimination between chr 1 and 5 cross-hybridization . For image quantification , three separate photomicrographs containing five , six , and three cells , respectively , in which the 19q31 . 31 FISH signals were in the same image plane were obtained . Photomicrographs were processed using the rtiff package for the R environment [50] . The fluorescent FISH signals were automatically segmented from background using the method of Ridler and Calvard [51] , individual spots were identified using the connected component algorithm [52] , and the number of pixels per feature were calculated . Twelve doublet FISH signals and eight singlet FISH signals were compared . Differences in size were evaluated using a one-sided Student's t-test . U2OS osteosarcoma cell and CAKI renal clear-cell carcinoma cell lines were obtained from the American Type Culture Collection ( Rockville , MD ) and maintained in Dulbecco's modified Eagle's medium supplemented with 10% heat-inactivated fetal bovine serum ( Sigma , Milwaukee , WI ) at 37°C in a humidified 5% CO2 atmosphere . Cyclic hypoxia treatment of cells were performed in humidified chambers at 37°C and flushed with 5% CO2 balance N2 for 30 min , followed by 5% CO2 and 7% O2 balance N2 for 30 min as one cycle . Cells were grown in these chambers for 16 hours [53] . Polyclonal anti-EGLN2 and anti-BNIP3L antibodies were obtained from Bethyl Laboratories ( Montgomery , TX ) and Sigma ( Milwaukee , WI ) , respectively . Polyclonal HIF1α and monoclonal HIF2α antibodies were obtained from BD Biosciences ( San Jose , CA ) and Novus ( Littleton , CO ) , respectively . Monoclonal anti-vinculin antibody was obtained from Abcam ( Cambridge , MA ) . Mammalian expression plasmids pcEglN2 was generated by PCR from Flag-EglN2 , a kind gift from Dr . Mircea Ivan , using primers 5′-GACGACGGATCCATGGACAGCCCGTGCCAGC-3′ and 5′-GACGACGAATTCCTAGGTGGGCGTAGGCGGC -3′ . The PCR product was then ligated into the BamHI and EcoRI sites in pcDNA3 ( + ) . Plasmid was confirmed by direct DNA sequencing . Western blotting were performed as described previously [54] . For first-strand cDNA synthesis , 1 µl of oligo ( dT ) 23 primer ( Sigma ) was incubated with 5 µg of RNA and distilled H2O ( total reaction volume of 20 µl ) for 10 min at 70°C in a thermal cycler ( MJ Research , Boston , MA ) . The mixture was cooled to 4°C , at which time 4 µl of 5× first-strand reaction buffer , 2 µl of 0 . 1 M DTT , 1 µl of a 10 mM concentration of each deoxynucleoside triphosphate , and 1 µl of Superscript II reverse transcriptase ( Invitrogen ) were added . cDNA synthesis was performed for 1 . 5 h at 42°C , followed by 15 min at 70°C in the thermal cycler . Human genomic DNA standards ( human genomic DNA was obtained from Roche , Mannheim , Germany ) or cDNA equivalent to 20 ng of total RNA were added to the quantitative PCR ( qPCR ) reaction mixture in a final volume of 10 µl containing 1× PCR buffer ( without MgCl2 ) , 3 mM MgCl2 , 0 . 25 units of Platinum Taq DNA polymerase , a 0 . 2 mM concentration of each deoxynucleoside triphosphate , 0 . 3 µl of SYBR Green I , 0 . 2 µl of ROX reference dye , and a 0 . 5 µM concentration of each primer ( Invitrogen ) . Amplification conditions were as follows: 95°C ( 3 min ) , 40 cycles of 95°C ( 10 s ) , 65°C ( 15 s ) , 72°C ( 20 s ) , and 95°C ( 15 s ) . qPCR was performed using the ABI Prism 7900HT Sequence Detection System ( Applied Biosystems , Foster City , CA ) . Gene-specific oligonucleotide primers designed using Primer Express ( Applied Biosystems ) were as follows: BNIP3L primer set ( 5′- CTGCACAAACTTGCACATTG-3′ and 5′- TAATTTCCACAACGGGTTCA-3′ ) , HMOX1 primer set ( 5′-GAATTCTCTTGGCTGGCTTC-3′ and 5′- TCCTTCCTCCTTTCCAGAGA-3′ ) , ENO1 primer set ( 5′- CAGCTCTAGCTTTGCAGTCG-3′ and 5′-GACACGAGGCTCACATGACT-3′ ) , CITED2 primer set ( 5′-ACTGCACAAACTGCCATCTC-3′ and 5′-CAGCCAACTTGAAAGTGAACA-3′ ) , beta-actin primer set ( 5′- GGATCGGCGGCTCCAT-3′ and 5′- CATACTCCTGCTTGCTGATCCA-3′ ) , GLUT-1 primer set ( 5′- CACCACCTCACTCCTGTTACTT-3′ and 5′-CAAGCATTTCAAAACCATGTTTCTA-3′ ) . SYBR Green I fluoresces during each cycle of the qPCR by an amount proportional to the quantity of amplified cDNA ( the amplicon ) present at that time . The point at which the fluorescent signal is statistically significant above background is defined as the cycle threshold ( CT ) . Expression levels of the various transcripts were determined by taking the average CT value for each cDNA sample performed in triplicate and measured against a standard plot of CT values from amplification of serially diluted human genomic DNA standards . Since the CT value is inversely proportional to the log of the initial copy number , the copy number of an experimental mRNA can be obtained from linear regression of the standard curve . A measure of the relative difference in copy number was determined for each mRNA . Values were normalized to expression of beta-actin mRNA and represented as the mean value experiments performed in triplicate±standard deviations . Extracts containing enriched EGLN2 were purified from rabbit reticulocyte lysate as previously described [8] . Briefly , approximately 1 L of rabbit reticulocyte lysate ( Green Hectares , Oregon , WI ) was diluted to 5 L in 50 mM Tris-HCl ( pH 7 . 4 ) , 0 . 1 M KCl , and 5% ( vol/vol ) glycerol and then was precipitated with 0 . 213 g/ml ( NH4 ) 2SO4 . After centrifugation at 16 , 000×g for 45 min at 4°C , the resulting supernatant was precipitated with an additional 0 . 153 g/ml ( NH4 ) 2SO4 . After centrifugation at 16 , 000×g for 45 min at 4°C , the pellet was resuspended in Buffer A ( 40 mM HEPES-NaOH [pH 7 . 4] and 5% ( vol/vol ) glycerol ) , dialyzed against Buffer A to a conductivity equivalent to Buffer A containing 0 . 2 M KCl , and applied at 0 . 5 L/h to a 0 . 5 L phosphocellulose ( Whatman , P11 ) column equilibrated in Buffer A containing 0 . 2 M KCl . The phosphocellulose column was eluted stepwise at 1 L/h with Buffer A containing 0 . 5 M KCl , and 100-ml fractions were collected . Proteins eluting in the phosphocellulose 0 . 5 KCl step were pooled and precipitated with 0 . 4 g/ml ( NH4 ) 2SO4 . After centrifugation at 16 , 000×g for 45 min at 4°C , the pellet was resuspended in 4 ml of Buffer A . Following centrifugation at 35 , 000×g for 30 min at 4°C , the resulting supernatant was applied at 2 ml/min to a TSK SW3000 HPLC column ( Toso-Haas , Montgomeryville , PA; 21 . 5×600 mm ) equilibrated in Buffer A containing 0 . 15 M KCl . The SW3000 column was eluted at 2 ml/min , and 4 ml fractions containing enriched EGLN2 were collected . An in vitro binding assay was performed as described previously [3] . TNT reticulocyte lysate ( Promega ) translation products were synthesized in the presence or absence of 35S-methionine . HIF1α- ( ODD ) translation products were incubated with cellular extract fractions containing enriched EGLN2 , where indicated , for 30 min at 37°C . Gal4-HA-HIF-1α ( 10 µl ) and HA-VHL ( 10 µl ) translation products were incubated with the indicated antibodies and protein A-Sepharose in 750 µl of EBC buffer ( 50 mM Tris [pH 8] , 120 mM NaCl , 0 . 5% Nonidet P-40 ) . After five washes with NETN buffer ( 20 mM Tris ( pH 8 ) , 100 mM NaCl , 0 . 5% Nonidet P-40 , 1 mM EDTA ) , the bound proteins were resolved on SDS-PAGE and detected by autoradiography . An in vitro ubiquitylation assay was performed as described previously [3] . [35S]Methionine-labeled reticulocyte lysate Gal4-HA-HIF1α ( ODD ) ( 4 µl ) were incubated in RCC 786-O S100 extracts ( 100–150 µg ) . Reactions were supplemented with an increasing titration of EGLN2-enriched cellular fraction where indicated . Additional reaction supplements include 8 µg/µl ubiquitin ( Sigma ) , 100 ng/µl ubiquitin-aldehyde ( BostonBiochem , Inc . , Cambridge , MA ) , and an ATP-regenerating system ( 20 mM Tris [pH 7 . 4] , 2 mM ATP , 5 mM MgCl2 , 40 mM creatine phosphate , 0 . 5 µg/µl of creatine kinase ) in a reaction volume of 20–30 µl for 1 . 5 h at 30°C . Figure 5B from the Jiang et al . article [6] was obtained in Portable Document Format ( PDF , Adobe Systems ) , imported into Canvas 9 ( ACD Systems ) , and the x- and y-graphic device coordinates of each data point , the x-axis ticks ( oxygen concentration ) , and the y-axis ticks ( densitometry ) were extracted . Linear interpolation was used to convert the graphic device coordinates to protein densitometry measurements and oxygen concentrations . Based on comparisons between the extracted oxygen concentrations ( 0 . 5 , 1 . 9 , 2 . 9 , 3 . 9 , 4 . 8 , 5 . 8 , 7 . 9 , 9 . 9 , 11 . 9 , 13 . 9 , 19 . 9 ) and the actual oxygen concentrations ( 0 . 5 , 2 , 3 , 4 , 5 , 6 , 8 , 10 , 12 , 14 , 20 ) , the extracted data varied on average less than 2% from the original data . The densitometry and oxygen concentration data were log2-transformed and linear model fit to the transformed data . The best-fit power-law equation is HIF1α = 22 . 61O−0 . 85 , where HIF1α represents HIF-1α protein levels and O represent oxygen concentration .
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Together , renal oncocytoma and chromophobe renal cell carcinoma ( RCC ) account for approximately 10% of masses that are resected from the kidney . However , the molecular defects that are associated with the development of these neoplasias are not clear . Here , we take advantage of recent advances in genetics and computational analysis to screen for chromosomal abnormalities that are present in both renal oncocytoma and chromophobe RCC . We show that while chromophobe RCC cells contain an extra copy of chromosome 19 , the renal oncoctyoma cells contain a rarely reported chromosomal abnormality . Both of these chromosomal abnormalities result in transcriptional disruptions of EGLN2 , a gene that is located on chromosome 19 and is critical for the cellular response to changes in oxygen levels . Defects in oxygen sensing are found in other types of kidney tumors , and the identification of EGLN2 directly implicates defects in the oxygen-sensing network in these neoplasias as well . These findings are important because the chromosomal defect present in renal oncocytomas may also be present in other tumor cells . In addition , deregulation of EGLN2 reveals a unique way in which perturbations in oxygen-sensing are associated with disease .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"urology/renal",
"cancer",
"genetics",
"and",
"genomics/cancer",
"genetics",
"genetics",
"and",
"genomics/bioinformatics",
"cell",
"biology/cell",
"signaling"
] |
2008
|
Somatic Pairing of Chromosome 19 in Renal Oncocytoma Is Associated with Deregulated ELGN2-Mediated Oxygen-Sensing Response
|
The accumulation of heteroplasmic mitochondrial DNA ( mtDNA ) deletions and single nucleotide variants ( SNVs ) is a well-accepted facet of the biology of aging , yet comprehensive mutation spectra have not been described . To address this , we have used next generation sequencing of mtDNA-enriched libraries ( Mito-Seq ) to investigate mtDNA mutation spectra of putamen from young and aged donors . Frequencies of the “common” deletion and other “major arc” deletions were significantly increased in the aged cohort with the fold increase in the frequency of the common deletion exceeding that of major arc deletions . SNVs also increased with age with the highest rate of accumulation in the non-coding control region which contains elements necessary for translation and replication . Examination of predicted amino acid changes revealed a skew towards pathogenic SNVs in the coding region driven by mutation bias . Levels of the pathogenic m . 3243A>G tRNA mutation were also found to increase with age . Novel multimeric tandem duplications that resemble murine control region multimers and yeast ρ− mtDNAs , were identified in both young and aged specimens . Clonal ∼50 bp deletions in the control region were found at high frequencies in aged specimens . Our results reveal the complex manner in which the mitochondrial genome alters with age and provides a foundation for studies of other tissues and disease states .
The accumulation of heteroplasmic mitochondrial DNA ( mtDNA ) mutations is a well-accepted facet of the biology of aging [1] . Heteroplasmic single nucleotide variants ( SNVs ) which are predominately transitions have been identified in all regions of mtDNA in aged tissues . Heteroplasmic deletions that fall in the major arc between the origins of replication also accumulate with age . These so-called “major arc” deletions are generally associated with pairs of direct repeats that flank the deleted region [2] . At the tissue level major arc deletions tend to be heterogeneous and of low clonality . A key exception is the clonal “common” deletion [3] , that occurs between two 13 bp direct repeats and is tightly associated with aging in brain [4] . mtDNA deletions accumulate to higher levels in post-mitotic tissues such as brain , heart and muscle [5] . Within brain the distribution of somatic deletions [6] appears to correlate with regional differences in mitochondrial oxidative phosphorylation activity [7] . At the cellular level , somatic mtDNA mutations accumulate stochastically to very high levels in a minority of cells [8] , [9] through clonal expansion [10] of both de novo and inherited variants [11] , [12] . These mechanisms dictate that specific mtDNA variants are present at very low levels within a tissue [13] . As a result , most of our understanding of somatic mtDNA mutation has come from the investigation of single mutations or single classes of mutation . To provide a more comprehensive picture of somatic changes to mtDNA , we have used next generation sequencing ( NGS ) of mtDNA-enriched DNA ( Mito-Seq ) to investigate mtDNA from putamen of young and aged donors at high coverage ( Sample details provided in Table S1 ) .
Breakpoints indicative of mtDNA rearrangements such as deletions , were detected by BLAST alignment . The common deletion , m . 8483_13459del4977 , was easily identifiable as a pair of clonal breakpoints in the coding region of aged samples ( Figs . 1A–B ) . In agreement with other studies [6] , [14] , frequencies were significantly higher in the aged cohort than in the young cohort ( P = 0 . 0087 , Fig . 2A ) and ranged from 8 . 4×10−4 to 3 . 6×10−3 mtDNA−1 . The eldest specimen in the young cohort , Y12 ( 34 yrs ) , carried the deletion at 1 . 2×10−3 mtDNA−1 in line with observations that some individuals accumulate deletions from the third decade of life [6] , [15] . Additional clonal and non-clonal deletions in the major arc between the mtDNA origins of replication are also associated with aging [16] . Dot-plots revealed “major arc” deletions as a consistent cloud of canonical breakpoints in aged specimens ( Figs . 1D–E ) . The distribution of breakpoints in each sample matched pooled data from multiple studies of clonal deletions [17] , demonstrating the extreme heterogeneity of breakpoints within individual tissue specimens . Cumulative frequencies were significantly higher in aged putamen than young ( P = 0 . 0152 ) and ranged from 0 . 8×10−2 to 2 . 6×10−2 mtDNA−1 ( Fig . 2B ) . As with the common deletion , Y12 carried levels of major arc deletions within the aged cohort range . Assuming a simple linear model for the accumulation of deletions , our data showed that major arc deletions accumulated faster than the common deletion ( Table 1 ) . Levels of the common deletion increased 12 . 5-fold and major arc deletions 3 . 6-fold between 25 and 80 years of age . There appeared to be a close relationship between the frequencies of the common deletion and other major arc deletions ( Fig . 2E ) . The proportion of total major arc deletion load accounted for by the common deletion appeared to be biphasic , increasing to a plateau at about age 40 and then increasing again beyond age 80 ( Fig . 2F ) . It is possible this pattern reflects differences in the contribution of non-clonal de novo deletion and clonal expansion of the common deletion , to the total mtDNA deletion load with age . Two novel rearrangements were detected in the control region . The first , m . ( 16508_16544 ) _ ( 16565_57 ) dup ( Fig . 1C ) was present at up to 2 . 5×10−2 mtDNA−1 , a similar range to that of major arc deletions , although there was not a significant difference in frequency between cohorts nor association between frequency and age ( Fig . 2C ) . These breakpoints resemble mtDNA control region multimers ( CRMs ) we previously identified in brain and heart of the progeroid PolgD257A/D257A mtDNA mutator mouse [18] . CRMs are large species composed of multimeric tandem duplications of part of the control region with very little or no other mtDNA sequence . This sequence structure , composed of multiple short tandem repeats , is very similar to that of ρ− mtDNAs in yeast [19] . We speculate that given their large size , any potential pathology associated with CRMs would likely be due to perturbation of nucleoid distribution . In PolgD257A/D257A mice , CRM repeat units have a mean length of 566 bp and a range from ∼200–800 bp . Repeat units in human putamen were shorter with a mean length of 81 bp and ranging from 44–87 bp with the most prominent form being m . 16509_22dup . Similar to CRMs in PolgD257A/D257A mice , direct repeats of 3 bp or larger were present in only 4% of CRM breakpoints suggesting they arise through a form of non-homologous end joining ( NHEJ ) . This contrasts with major arc deletions which predominantly occur between direct repeats , inferring a role for homologous recombination [20] , [21] , and the present study where 83% of canonical breakpoints in the coding region involved direct repeats of 3 bp or longer . The presence of CRMs in our original putamen DNA samples was verified using inverted primer PCR and as seen in PolgD257A/D257A mice [18] this resulted in large heterogeneous amplicons ( Fig . 1F ) . Applying the same PCR to DNA from cerebellum of the cases under study , we were unable to amplify CRM products ( Fig . S2 ) . Thus CRMs may be localized to regions that are sensitive to mitochondrial dysfunction [7] and accumulate higher levels of mtDNA damage [14] . The physiological impact of CRMs remains to be determined . Levels in putamen are ∼200-fold lower than in brain from PolgD257A/D257A mice [18] where CRMs were associated with a 45% depletion in mtDNA and an increase in mtDNA-encoded mRNAs of ∼3-fold . We also identified a cluster of clonal control region deletions ( CRDs ) , m . ( 244_309 ) _ ( 311_489 ) del , present at frequencies of up to 1 . 3×10−2 mtDNA−1 , similar to that of major arc deletions in aged samples ( Figs . 1B–C ) . These deletions disrupt conserved sequence block II ( CSBII ) involved in mtDNA replication and transcription termination [22] . Differences in the frequency of CRDs between the young and aged cohorts was significant ( P = 0 . 0043 , Fig . 2D ) . 90% of CRDs were 50 bp long and the most abundant form was m . 307_356del50 which occurred between a pair of 9 bp direct repeats . The 5′ and 3′ flanking direct repeats and the resulting breakpoint encompass copies of an 11 bp degenerate sequence motif recently found to be over represented within 5 bp of mtDNA deletion breakpoints , including the flanking direct repeats of the common deletion [17] . The biological basis for the association of this motif with deletion breakpoints remains undetermined . The m . 307_356del50 deletion has been reported as a somatic mutation in cancers [23] and at high levels in the saliva , blood and hair follicles from a healthy Chinese family where it was shown to elicit no effect on mtDNA levels in blood [24] . PCR of original DNA samples verified the presence of CRDs in putamen ( Fig . 1F ) . Unlike CRMs , PCR of DNA from cerebellum revealed CRDs in three aged specimens , one of which did not carry the deletion in putamen ( Fig . S2 ) . These findings define CRDs as both transmissible and somatic mtDNA mutations and indicate that they are a more prevalent feature of mtDNA mutation spectra than previously recognized . We did not find any relationship between the levels of CRMs and CRDs , nor either of these species with the levels of major arc or common deletions . Both CRMs and CRDs appear to be distinct from previously reported tandem duplications in the control region [25] , [26] . To focus on somatic variation and reduce the confounding effects of inherited high frequency heteroplasmy , only SNVs with frequencies <0 . 01 bp−1 were considered for analysis ( Fig . S5 ) . Analysis of errors in NGS has revealed nucleotide incorporation errors during library synthesis create false SNV calls that cannot be screened using quality filtering [27] . In particular , frequencies of G>T and C>A transversions are erroneously increased due to the presence of endogenous and/or exogenously-generated 8-oxoguanine . While inherent error limited the accuracy of absolute quantitation , the high level of sequencing coverage attained in our study ( mean coverage 126 , 538 Table S1 ) enabled examination of differences in SNV frequencies and rates of SNV accumulation . In line with the consensus in the field ( reviewed in [1] and [5] ) , the average frequency of total SNVs called in each alignment was significantly higher in mtDNA from aged putamen than young ( P = 0 . 0079; Fig . 3A ) . Assuming simple linear models for SNV accumulation , we determined rates of accumulation for SNVs ( Table 1 ) . Total SNVs accumulated at 4 . 02±1 . 81×10−7 per base pair per year ( bp−1yr−1; ±95% CI ) , corresponding to an increase of 2 . 6-fold between 25 and 80 years of age when adjusted to a baseline SNV load of 0 . 37±0 . 09×10−5 at less than one year of age determined for human forebrain [28] . Transitions account for about 90% of heteroplasmic SNVs [9] and are subject to significantly lower levels of NGS library-error than transversions [27] . Correspondingly , data for transitions were tighter than for total SNVs ( Fig . 3B ) , providing a much more accurate picture of the SNV spectrum . In our putamen samples , transitions accumulated at a rate of 2 . 22±0 . 42×10−7 bp−1yr−1 across the entire mitochondrial genome , corresponding to a 2 . 3-fold increase from 25 to 80 years of age . These rates place the SNV loads for human putamen at 80 years of age ( Table 1 ) in good agreement with published values for aged forebrain [27] , [28] and human colonic crypts [9] which range from ∼2 . 2–3 . 5×10−5 bp−1 . Both of the above somatic SNV mutation rates are an order of magnitude higher than mutation rates for germline mtDNA haplotypes calculated from phylogenic studies [29] , [30] , likely reflecting the influence of purifying selection on the fixation of germline variants [31] . As seen in phylogenic [29] , [30] and pedigree studies [32] of germline mtDNA , and in somatic SNV analysis [27] the most abundant SNVs clustered in the control region in both young and aged samples ( data not shown ) . Although the average frequency of control region transitions remained significantly higher in the aged cohort than the young ( P = 0 . 0079 ) . A plausible explanation for the clustering of SNVs in the control region is that a significant proportion of variance in this region is inherited . In addition , given the role of the control region in mtDNA replication and maintenance [33] , expansion of variant mtDNA clones may drive increased somatic variance in this region as opposed to de novo mutation . Alternatively , as the control region is the most variable region of mtDNA [34] and this may simply reflect tolerance of sequence variation in this region . The rate of accumulation of transitions in the control region was 5-fold higher than the rate in the coding region ( 8 . 82±3 . 5×10−7 bp−1yr−1 and 1 . 77±0 . 4×10−7 bp−1yr−1 respectively , Fig . 3F ) . Again both values are an order of magnitude higher than germline mutation rates for these regions calculated from phylogenic data [29] , [30] . However , in alignment with the determination of more rapid substitution rates when calculated over shorter timescales [35] , they are very close to germline mutation rates calculated from pedigree analysis by Howell and coworkers [32] . In this study analysis of blood from multi-generational pedigrees combined with information from similar studies revealed mutation rates of 9 . 5×10−7 bp−1yr−1 in the control region and 1 . 5×10−7 bp−1yr−1 in the coding region ( 5 . 3–15 . 7×10−7 bp−1yr−1 and 0 . 2–4 . 9×10−7 bp−1yr−1 respectively at 99 . 5% CI ) . While more work is necessary , this raises the intriguing possibility that apparent mtDNA SNV mutation rates may be similar in somatic and germline tissues . As there is clear evidence for purifying selection of germline mtDNA [36] , which should lower the germline mutation rate , the similarity may reflect the antagonistic effect of the rapid expansion of permissive germline variants at replication bottlenecks during germ cell development [37] . When corrected for the difference in size of the coding and control regions , the rate of accumulation of mutations within each of these regions per mtDNA was 2 . 7-fold higher for the coding region than the control region . This demonstrates that coding region mutations still constitute the major burden of somatic variance per mtDNA despite lower rates of accumulation per base pair ( Fig . 3F ) . Within the coding region there was no notable difference in the rates of accumulation of transitions between RNA and protein coding genes ( Fig . 3G ) . There did not appear to be any relationship between levels of SNVs and mtDNA rearrangements that could not be accounted for by corresponding relationships to age . The heteroplasmic transition m . 3243A>G in the MT-TL1 tRNA gene is likely the most prevalent pathogenic mtDNA mutation [38] and is primarily associated with MELAS and MIDD syndromes [39] . The region surrounding m . 3243 is an etiologic hotspot for mutations [40] although there have been conflicting reports as to whether m . 3243A>G accumulates in normal aging [41] , [42] . We observed a distinct hotspot of SNV abundance spanning m . 3243 in aged samples ( Figs . 3D–E ) with a significantly higher average frequency for SNVs through m . 3242_3244 than young samples ( P = 0 . 0079 for each , <0 . 0001 overall ) . In all aged samples the most abundant SNVs called at m . 3242_3244 were the transitions , m . 3242G>A , m . 3243A>G and m . 3244G>A , all of which have been associated with mitochondrial diseases [39] , [43] . There is some evidence of association between detectable levels of m . 3243A>G in hair follicle DNA and age related hearing loss [38] , implying this finding may have consequences for the biology of aging . Applying duplex sequencing to mtDNA from human forebrain , Kennedy and coworkers have recently described a novel strand bias for somatic transitions in the mtDNA coding region , detected as increased G>A versus C>T and T>C versus A>G transitions in the reference strand ( L-strand ) [28] . The G>A versus C>T mutation bias is proposed to be caused by cytosine deamination ( C>U ) on the H-strand , potentially occurring during replication while the H-strand is exposed . As the mtDNA reference strand is the opposing L-strand the bias is manifest as an increase in the frequencies of G>A relative to C>T transitions . Dissection of SNV spectra replicated this finding in our putamen specimens . Significant differences in the frequencies of both G>A versus C>T , and T>C versus A>G transitions were observed in the coding region of young and aged samples ( P = 0 . 0079 for each , Fig . 4A ) . The median bias in the G>A and C>T frequencies ( [G>A]-[C>T] ) in the coding region was 2 . 56×10−5 bp−1 in the young cohort and 5 . 18×10−5 bp−1 in the aged cohort , with a significant difference between cohorts ( P = 0 . 0079 , Fig . 4C ) . For [T>C]-[A>G] bias in the coding region , median magnitudes were lower ( Fig . 4D ) and differences between cohorts were not significant ( P = 0 . 0556 ) . In the control region , G>A versus C>T frequencies showed a similar difference in young samples as seen in the coding region ( P = 0 . 0079 , Fig . 4B ) . However , in aged samples no difference in G>A versus C>T frequencies was observed . In addition , significantly lower magnitudes of [G>A]-[C>T] bias were seen within samples compared to the young cohort ( P = 0 . 0079 , Fig . 4E ) , indicative of an age-related switch in [G>A]-[C>T] bias , contrary to the coding region where bias appears to increase with age . Examination of SNV frequencies revealed troughs in [G>A]-[C>T] bias in the control region ( Fig . S7 ) driven by the accumulation of high levels of m . 64C>T and m . 16148C>T in all aged samples ( Fig . 4G ) . Exclusion of these variants from analysis did not recapitulate the positive [G>A]-[T>C] bias seen in the coding region in the aged cohort . Both variants occur as haplotype polymorphisms [30] ( MitoMaster GenBank frequencies 2 . 6% and 1 . 7% respectively [44] ) and m . 64C>T has previously been noted in aged brain specimens [45] . Predicting either the consequence or origin of the accumulation of these variants is difficult as neither falls within a known mtDNA control element . While their accumulation may reflect the expansion of low consequence variants under a lack of mutational bias , it may also be that they represent unknown functional elements in the control region . Bias in control region T>C versus A>G frequencies shifted in the same direction as the coding region and was significant in both young and aged samples ( P = 0 . 0159 and 0 . 0079 respectively ) although differences in the magnitude of [T>C]-[A>G] bias between cohorts were not significant and the relationship with age was weak ( Fig . 4F ) . As mentioned above , high G>T and C>A transversion frequencies , stemming from library synthesis base incorporation errors at 8-oxoguanine [27] , were noted in the coding and control regions of all samples ( Fig . 4A–B ) . Recent work has confirmed that in vivo there is no evidence for accumulation of G>T and C>A transversions with age in brain [28] . Analysis of “Mutpred” predicted pathogenicity scores [46] for germline mtDNA variants has demonstrated that variants with high predicted pathogenicity scores ( >0 . 6 ) , are selected against [31] . In contrast , we found that transitions with high pathogenicity scores had higher average frequencies than those with lower ones in both the young and aged cohorts ( Fig . 3C ) , in agreement with studies of single cells from colonic crypts of aged donors [9] . The skew in pathogenicity most likely reflects the combination of mutational strand bias described above and skewed base distribution at different pathogenicity scores due codon composition ( Fig . S8 ) . However , the apparently localized increases in frequencies of SNVs at m . 3242_3244 , m . 64C>T and m . 16148C>T ( Figs . 3D–E & 4G ) suggests there may also be some modification of SNV spectra beyond strand bias . Transitions with pathogenicity scores >0 . 667 accounted for 37% of the increase in transition SNV burden at protein coding bases and 20% across all bases . At an SNV load of 0 . 15 mtDNA−1 at 25 years of age ( Table 1 ) these percentages translate to pathogenic SNV burdens of 0 . 03–0 . 06 mtDNA−1 , raising to 0 . 07–0 . 13 mtDNA−1 at age 80 . Pathogenic mtDNA mutations have threshold mutation loads in tissues of 0 . 80–0 . 90 mtDNA−1 [47] . While it is uncertain whether heterogeneous mutations can have additive effects , this indicates that the steady-state pathogenic somatic mtDNA burden in normal putamen at 80 years of age is about 6–12-fold lower than that of a patient with a mitochondrial disorder . Nevertheless , as the etiology of the stoichiometric accumulation of somatic mtDNA mutations in aging is distinct from the inheritance of mtDNA mutations in patients with mitochondrial disorders [13] , these estimates may still reflect a considerable stress . To examine the influence of nuclear mtDNA sequences ( numts ) [48] on our analysis we carried out identical sequencing of total DNA from human 143B . 206 ρ0 cells that do not have mtDNA [49] and subjected the resulting “pseudo”-mtDNA alignment to identical analysis ( For alignment details see Table S1 ) . Only 20 breakpoints were called in this alignment compared to the 4 , 183–13 , 877 identified in putamen specimens ( median 11 , 009 ) . These 20 included a single call for the common deletion and a single call for a CRM . Given the hundreds of hits for verifiable species like the common deletion and CRMs in our mtDNA-enriched samples , we determined that numts had negligible influence on analysis of rearrangements . In turn , the identification of the common deletion and a CRM breakpoint in an ostensibly nuclear DNA sample implies these are evolutionarily persistent mutations . With respect to SNVs the influence of numts in determining control region clustering can be excluded as this was not observed in our ρ0 alignment ( Fig . S9C ) . In addition , no SNVs were reported in our ρ0 cell alignment between m . 3100 and m . 3300 , ruling out an influence of numts in relation to increased SNV frequencies spanning m . 3243 ( Fig . S9C ) . Interestingly an opposing skew in pathogenicity , towards higher average frequencies for SNVs with low predicted pathogenicity , was seen in the ρ0 alignment ( Fig . S9B ) . This skew matches that seen in phylogenic studies of pathogenicity and higher mutation rates for 3rd base positions in studies of mtDNA haplotype variation [29] , [30] . As the ρ0 alignment represents numts , this skew is in agreement with the concept of nuclear transfer of evolutionarily stable mtDNA variants that predominantly have low pathogenicity scores [31] . The data presented above represent the steady state somatic mutation spectra of tissue specimens . They are likely the product of opposing biological forces that act to increase or decrease mutation loads and result in the maintenance of somatic mutation burdens at tolerable levels . Dissecting the contribution of specific factors such as de novo mutation or clonal expansion is not possible from this data . Considering the relatively small samples size , the similarities between the mutation spectra in each cohort underlines the consistency which the mitochondrial genome alters with age in putamen . Of note , the rearrangements identified in the control region warrant further study given their frequency and undetermined biological impact . It is hoped these data will provide useful comparative benchmarks for studies of somatic mtDNA mutation in other tissues and in disease states .
DNA extraction and sequencing . mtDNA-enriched total DNA extraction was based on our previously described approach [18] with minor alterations . Putamen samples were obtained from neurologically normal fresh frozen specimens at the University of Miami brain Endowment Bank ( Table S1 ) . All donors were Caucasian males . 0 . 20–0 . 35 g tissue punches were rapidly thawed at room temperature in 4 ml of homogenization buffer ( 200 mM mannitol , 50 mM sucrose , 10 mM HEPES ( pH 7 . 0 ) , 1 mM ETDA ) and homogenized using 30 strokes of a Teflon-glass Dounce homogenizer on ice . Crude mitochondrial fractions were harvested from homogenates by differential centrifugation at 600 g to clear debris and 9000 g to collect mitochondrial pellets . mtDNA-enriched DNA was obtained by resuspension in 1 mL extraction buffer ( 33 mM TRIS pH 8 . 3 , 10 mM EDTA , 10 mM NaCl ) . To which SDS was added to 1% w/v and 3 mAU proteinase K solution ( Qiagen ) was added followed by incubation at 56°C for 4 hrs . Total nucleic acids were extracted twice using 25∶24∶1 phenol∶cholorfom∶isoamyl-alcohol ( v/v/v ) followed by two extractions with 24∶1 cholorfom∶isoamyl-alcohol ( v/v ) . Nucleic acids were precipitated by ethanol/NaAc precipitation and resuspended in 55 uL 10 mM TRIS pH 8 . 5 . RNA was then digested with 0 . 07 U RNAse A ( Qiagen ) and 300–500 ng dsDNA by Qubit ( Invitrogen ) analysis submitted for library synthesis . Libraries were prepared using Illumina Truseq PE V3-cBot-HS cluster kits and sequenced on the Illumina HiSeq 2000 platform at 5–8 libraries per lane with image processing using CASAVA V1 . 7/1 . 8 as 2×100 bp paired-end reads . Each sequencing run contained specimens from both young and aged cohorts with similar age distributions ( Table S1 ) . Alignment . Bioinformatic analysis was done with Genomics Work Bench V4 . 7-5 . 5 . 2 ( CLCBio ) . Reads were quality trimmed with an average post-trim read length >95 bp . Initial alignments were made against the revised Cambridge reference sequence ( CRS ) mtDNA reference sequence ( NC_012920 ) , using low stringency local alignment with a cutoff of 80% similarity over 50% length to collect mtDNA-like reads and reduce datasets . Aligned reads and sample-specific consensus sequences were extracted from these assemblies . Reads were then assembled back against respective sample-specific consensus sequences using high stringency local alignment with a cutoff of 90% similarity over 95% length . Reads that aligned at low stringency but not high stringency ( generally <0 . 7% aligned reads ) were collected for detection of rearrangement breakpoints . mtDNA haplotyping was done using MitoTool 1 . 1a [50] . Analysis of rearrangements . Breakpoints were identified using BLAST to align reads against NC_012920 with alternate “murine” numbering to provide a contiguous control region and a first base position at the start of TRNF ( m . 577 ) . This alternate reference sequence enabled detailed examination of recombination involving the control region , in particular rearrangement spanning m . 16569_1 . To streamline output , a word length of 15 was used with open gap cost of 5 and extension cost 2 . Data was parsed to collect reads with two segments in the same sense and collectively extending the full length of the read , neither of which was fully internal . Between 4 , 183–13 , 877 breakpoints were sequenced per alignment . The common deletion was quantified by counting: m . ( 8477_8483 ) _ ( 13262_13452 ) del; the cumulative burden of major arc deletions was determined by counting m . ( 5576_15976 ) del>320 excluding the common deletion and corrected for putative chimeras by subtracting m . ( 15976_5576 ) del>320 ( Fig . S1C ) ; CRMs were quantified by counting m . ( 16492_59 ) _ ( 16492_59 ) del>137; and CRDs m . ( 244_494 ) del ( each described here with CRS numbering ) . The frequency of each type of rearrangement per mtDNA equivalent ( mtDNA−1 ) was determined by normalizing to average coverage and assumes a single rearrangement per full length mtDNA . Data used for breakpoint dot-plots was corrected for coverage by reducing the volume of data plotted by the ratio of the average coverage of the alignment to the lowest coverage alignment , using cluster coordinates as a means to randomly shuffle reads . To reduce over interpretation of outliers and to provide conservative estimates of significance all tests of significance are two tailed Mann-Whitney rank tests . Analysis of SNVs . High stringency assemblies were used for SNV detection using CLCBio quality-based SNP detection algorithm . The algorithm filters variant calls on the basis of quality scores for the central base ( >Q33 ) , the average quality of neighborhood window ( radius ±5 bp , >Q30 ) and the presence of other mismatches or gaps ( < = 2 ) within the window . Significance filtering , i . e . limits on coverage or absolute counts , were not applied as a very low counts are biologically valid in a genetically heterogeneous system especially when considering the sampling effect of cluster generation . To exclude reads from chimeric fragments [51] , all reads in broken pairs were excluded from analysis . Taken together these parameters excluded a significant amount of sequencing data and reduce effective sequencing coverage by 25–30% for SNV detection . SNV tables recorded frequencies for all four possible alleles at each base . To focus on somatic variation and avoid confounding effects of inherited high frequency heteroplasmy which is common , only SNVs with frequencies <0 . 01 bp−1 were considered for analysis . Data from two sequencing runs were normalized by correcting linearly for the difference between mean SNV frequency of each run ( Fig . S6 ) . At the levels of coverage attained , the highly consistent nature of NGS sequencing error enabled detailed analysis of relative SNV frequencies but over-estimated absolute SNV levels due to incorporation errors . For examination of different classes of SNV frequencies , data was normalized by correcting linearly for the difference between means for each specific base change within the coding region across all samples between runs . These tables were used for calculation of mutation rates within different mtDNA coding and control regions , Mutpred-grouped protein coding bases and the m . 3242_3244 triplet . For analysis of coding regions , SNV data from all alignments was put in phase by aligning to m . 577G , there were no insertions/deletions in the coding regions of any consensus sequence . Control region length varied from −1 to +5 bp of CRS . Mutpred data tables for transitions in the CRS were taken from Pereira et al [31] . Fourteen codons in MT-ATP6 where bases overlap MT-ATP8 were considered only for MT-ATP8 . Predicted pathogenicity scores for transitions were split into three groups , those with scores of >0 . 667 and non-sense mutations ( 3468 bp ) , those with scores of 0 . 666-0 . 100 ( 3465 bp ) and synonymous mutations plus the small number of bases with scores of <0 . 100 ( 4386 bp ) . Transition frequencies for each base position were determined against sample-specific consensus sequences and aligned with mutpred scores calculated from CRS sequence for each base . The maximum sequence divergence between sample coding region sequence and CRS coding region sequence was 28 bp out of 15 , 447 bp . To reduce over interpretation of outliers and to provide conservative estimates of significance all tests of significance are two tailed Mann-Whitney rank tests . PCR . For detection of CRMs , m . ( 16508_16544 ) _ ( 16565_57 ) dup , inverted primer PCR was carried out using Kapa HiFi 2× master mix ( Kapa Biosciences ) containing a proofreading polymerase under manufacturers standard reaction conditions with a Tm of 62 . 5°C and 60 s extension time . CRM Primers: 16562-F: TCACGATGGATCACAGGTCTAT 16540-B GTGGGCTATTTAGGCTTTATGACC For detection of the m . 307_356del50 CRD we used touchdown PCR with standard non-proofreading Taq polymerase ( Bioline ) , reaction buffer ( Bioline Mango ) and conditions with Tm dropping 68-61°C over the first 10 cycles , followed by another 30 cycles at a Tm of 61°C and an extension time 90 s throughout . CRD Primers: CRD1-F: AAAAATTTCCACCAAACCCCAAAA CRD2-F: AAAAATTTTCACCAAACCCCAAAA 1421-B CACCTTCGACCCTTAAGTTTCATA . CRD forward primers span the m . 307_356del50 breakpoint . CRD2-F contains the m . 295C>T polymorphism and was used for the Haplogroup J samples ( Y03 and A19 , Table S1 ) .
|
Mitochondria are unique among animal organelles in that they contain their own multi-copy genome ( mtDNA ) . For the past 20 years it has been known that tissues like brain and muscle accumulate somatic mtDNA mutations with age . Because individual mtDNA mutations are present at very low levels , few details are known about the spectrum of mutations associated with aging . Advances in sequencing technology now permit the examination of mtDNA mutations at high resolution . We have examined the spectrum of mtDNA mutations present in putamen , a brain region prone to the accumulation of somatic mtDNA mutations . We were able to quantify the accumulation of clonal and non-clonal deletions in the mtDNA coding region which are known to have a strong association with aging . Partial deletions and novel duplications of the mtDNA control region were also identified , and appear to be more prevalent than previously recognized , but levels showed weaker associations with age than coding region deletions . Single nucleotide variants accumulate fastest in the control region , with a skew towards the accumulation of pathogenic mutations in the coding region . Understanding how the mitochondrial genome alters with age provides a benchmark for studies of somatic mtDNA mutations and dissection of the role they play in normal aging and degenerative diseases .
|
[
"Abstract",
"Introduction",
"Results",
"and",
"Discussion",
"Materials",
"and",
"Methods"
] |
[] |
2013
|
Somatic mtDNA Mutation Spectra in the Aging Human Putamen
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Fe-S clusters are ubiquitous cofactors of proteins involved in a variety of essential cellular processes . The biogenesis of Fe-S clusters in the cytosol and their insertion into proteins is accomplished through the cytosolic iron-sulphur protein assembly ( CIA ) machinery . The early- and middle-acting modules of the CIA pathway concerned with the assembly and trafficking of Fe-S clusters have been previously characterised in the parasitic protist Trypanosoma brucei . In this study , we applied proteomic and genetic approaches to gain insights into the network of protein-protein interactions of the late-acting CIA targeting complex in T . brucei . All components of the canonical CIA machinery are present in T . brucei including , as in humans , two distinct CIA2 homologues TbCIA2A and TbCIA2B . These two proteins are found interacting with TbCIA1 , yet the interaction is mutually exclusive , as determined by mass spectrometry . Ablation of most of the components of the CIA targeting complex by RNAi led to impaired cell growth in vitro , with the exception of TbCIA2A in procyclic form ( PCF ) trypanosomes . Depletion of the CIA-targeting complex was accompanied by reduced levels of protein-bound cytosolic iron and decreased activity of an Fe-S dependent enzyme in PCF trypanosomes . We demonstrate that the C-terminal domain of TbMMS19 acts as a docking site for TbCIA2B and TbCIA1 , forming a trimeric complex that also interacts with target Fe-S apo-proteins and the middle-acting CIA component TbNAR1 .
Iron-sulphur ( Fe-S ) clusters are simple and versatile cofactors involved in a plethora of cellular processes from bacteria to humans and theorised to have formed the ancient surfaces upon which prebiotic chemical reactions took place , laying the ground for the origin of life itself [1 , 2] . Biogenesis of Fe-S clusters and their subsequent incorporation into polypeptide chains are intricate processes involving dedicated compartmentalised pathways that comprise dozens of proteins [3 , 4] . At least three such pathways are conserved in eukaryotes , namely the cytosolic Fe-S protein assembly ( CIA ) machinery , the mitochondrial Fe-S cluster assembly ( ISC ) system and the plastidial sulphur mobilisation ( SUF ) system [4–6] . A cytosolic pathway for maturation of Fe-S proteins was first described in the early 2000’s when a genetic screen aimed at the reconstitution of the [4Fe-4S] cluster on human IRP1 , also known as cytosolic aconitase , identified the cytosolic P-loop NTPase Cfd1 as essential for the maturation of IRP1 and other cytosolic , but not mitochondrial Fe-S proteins [7] . Since then , at least eight additional proteins ( nine in yeast ) have been associated with the CIA machinery , which has been implicated in the maturation of a growing list of cytosolic and nuclear Fe-S proteins [4] . The biogenesis of Fe-S proteins can be conveniently simplified in two discrete yet concerted steps: one for assembly of the clusters into a protein scaffold and another for their trafficking/insertion into client proteins . Functional studies have shown that the CIA machinery is highly conserved from yeast to man , and is organised into several sub-complexes that support different stages of the process [8] , allowing the components of this pathway to be grouped in a modular fashion as follows: ( i ) an early-acting module encompassing proteins of the electron transfer chain Tah18 and Dre2 [9] , and a heterotetrameric protein scaffold formed by Cfd1 and Nbp35 , in which [4Fe-4S] clusters are initially assembled [10 , 11]; ( ii ) a middle-acting module , represented by Nar1 [11 , 12] and concerned with the transfer and trafficking of the pre-formed Fe-S clusters to ( iii ) the late-acting or targeting module that facilitates the target-specific insertion of clusters into client proteins [13 , 14] . In yeast , the CIA targeting complex ( CTC ) is composed of Mms19 , Cia1 , and Cia2 [15] , while human cells possess two isoforms of Cia2 , labelled CIA2A and CIA2B , with the former displaying a notable specificity for the maturation of a subset of client proteins implicated in cellular iron homeostasis , while the latter is involved in canonical Fe-S cluster assembly . Trypanosoma and Leishmania species are causative agents of human diseases that threaten hundreds of millions of people mostly in developing countries , as well as of major economically important veterinary diseases [16–19] . T . brucei is the best-studied member of the supergroup Excavata [20] serving as a model organism due to its genetic tractability [21–24] . The early- and middle-acting modules of the CIA pathway have been previously characterised in this parasite [25] , however , the components of the late-acting part had yet to be studied . In addition to this , the Fe-S proteome of this divergent protist remains vastly unexplored , thus providing an excellent opportunity to study these two biological questions . In this work , we demonstrate that the late-acting module of the CIA machinery is essential for the survival of this parasite in vitro , but not in vivo . TbCIA2B and TbCIA1 assemble at the C-terminal domain of TbMMS19 to form the canonical ternary targeting complex . Moreover , in both procyclic ( PCF ) and bloodstream stages ( BSF ) of T . brucei , binary configurations reminiscent of those observed in human cells were also present . Members of the CTC interacted with client Fe-S proteins and TbNAR1 , while depletion of CTC components impaired cell growth and led to decreased protein-bound cytosolic iron levels and aconitase activity .
Four proteins , termed TbCIA1 ( Tb927 . 8 . 3860 ) , TbCIA2A ( Tb927 . 9 . 10360 ) , TbCIA2B ( Tb927 . 8 . 720 ) and TbMMS19 ( Tb927 . 8 . 3920 , Tb927 . 8 . 3500 ) , were previously identified in T . brucei on the basis of their similarity to yeast and human CTC components [26 , 27] . Only TbCIA1 has been characterized to date [25] . T . brucei encodes two different MMS19 proteins , sharing 99 . 6% amino acid identity . As in humans , two genes encoding homologues of yeast Cia2 protein were found in T . brucei . The phylogenetic position of these proteins , designated TbCIA2A and TbCIA2B , has been analysed elsewhere [28] . We determined the subcellular localisation of TbCIA2A , TbCIA2B , and TbMMS19 by indirect immunofluorescence , crude digitonin fractionation and selective permeabilisation with digitonin . Cell lines expressing in situ C-terminally V5- or HA-tagged CIA proteins were produced ( see Materials and Methods ) . Fixed parasites were probed with anti-V5 and anti-enolase antibodies ( TbENO ) [29] to detect the fusion proteins and the cytosolic marker , respectively . The co-localisation of all V5-tagged proteins with TbENO suggests their cytosolic localisation ( Fig 1A ) . To further confirm this finding , the subcellular distribution of the CIA pathway components was analysed by a fractionation with digitonin . For this , we incubated the cells with a concentration of digitonin that liberates the cytosol , separating it from the mitochondrial fraction ( Fig 1B ) . The signal for all of the CTC components co-localizes with that of the cytosolic marker ( Cyt ) , TbENO . The mitochondrial marker , TbmtHSP70 , is only present in the mitochondrial ( M ) fraction . The pellet ( P ) denotes the insoluble fraction after solubilizing the mitochondrial fraction , which exhibits proteins that are membrane-bound , such as part of TbmtHSP70 . A parallel corroboration was performed by selective permeabilisation with digitonin . In this experiment , equal numbers of cells were incubated with increasing concentrations of the detergent , causing progressive cell membrane permeabilisation and sequential release of the cytosolic and organellar fractions . TbCIA2B-HA and TbMMS19-HA were co-released with the cytosolic control phospholipase A1 ( TbPLA1 ) [30] , while the trypanosome alternative oxidase ( TbTAO ) , which served as a mitochondrial marker [31] , was released only at higher detergent concentrations ( Fig 1C ) . Taken together , immunofluorescence and detergent-based cell fractionation identified TbCIA1 , TbCIA2A , TbCIA2B and TbMMS19 as cytosolic proteins . Analysis of the function of the putative CTC members was carried out in uninduced and RNAi-induced PCF and BSF cell lines . The efficiency of the RNAi knockdowns was monitored for up to 8 days in the PCF and 6 days in the BSF and was further assessed by Western blot analysis ( Fig 2A–2F and 2J–2O ) . While the growth rate of TbMMS19 RNAi in the BSF was mostly unaffected upon depletion , the same downregulation in the PCF exhibited considerable growth impairment ( Fig 2A and 2J ) . On the other hand , the BSF TbCIA2A RNAi cell line showed a mild growth phenotype ( Fig 2K ) , whereas in PCF this downregulation does not affect the growth rate ( Fig 2B ) . Two days after the downregulation of TbCIA2B , a decrease in the growth of the PCF was observed ( Fig 2C ) , but this effect was less pronounced in the BSF ( Fig 2L ) . We have previously shown that depletion of the scaffold proteins TbCFD1 and TbNBP35 caused mild to severe growth impairment in PCF and BSF , but knocking down the expression of individual components upstream of the CTC did not affect the growth rate [25] . However , stringent pairwise knockdowns of the early-acting components of this pathway ( e . g . TbTAH18 and TbDRE2 ) caused marked growth defects [25] , suggesting an interaction of CIA factors , which only becomes critical upon simultaneous RNAi knockdown of more than one of them . We sought to employ this phenomenon by silencing the expression of two CTC members simultaneously . However , the observed phenotype of double knockdowns in PCF ( TbCIA1-TbCIA2B and TbCIA2A-TbCIA2B ) was no more pronounced than the phenotype observed following the depletion of TbCIA2B alone ( S1 Fig ) . To assess the role of the CTC components on the pathogenicity of T . brucei , we infected mice with BSF RNAi cell lines of TbMMS19 and TbCIA2B . As shown in S2 Fig , these infection experiments suggest that neither protein is essential in BSF , in agreement with the mild in vitro growth phenotypes described above , as well as with the initial observation of the double-knockdown cell lines in this life stage , in which at least two components of the pathway had to be ablated in order to obtain a clearer growth phenotype ( S1 Fig ) [25] . We next asked whether depleting the cells of individual CTC members would impact the activity of known Fe-S proteins . Aconitase ( TbACO ) , a Fe-S enzyme that catalyses the reversible isomerisation of citrate to isocitrate , is encoded by a single gene and has a dual subcellular localisation , being ca . 70% in the cytosol and 30% in the mitochondrion [32 , 33] . These features qualify it as a suitable surrogate for Fe-S cluster-dependent enzymatic activity in these two cellular compartments [27] . As shown in Fig 2G and 2I , cytosolic TbACO activity was reduced in 60% and 40% , when TbMMS19 and TbCIA2B were knocked down , respectively , whereas the mitochondrial activity remained unchanged . Furthermore , the depletion of TbCIA2A did not affect aconitase activity in these cellular compartments ( Fig 2H ) . Hence , TbMMS19 and TbCIA2B seem to be required for the maturation of this Fe-S protein , providing a functional link between the CTC and the transfer of Fe-S clusters to target proteins . Several lines of evidence have linked the pathways for Fe-S cluster biogenesis to DNA repair processes in humans , yeast , and plants [13 , 14 , 34–37] . Surprisingly , even efficient depletion of the CTC members did not affect the ability of T . brucei to cope with DNA damage caused by various genotoxic agents as determined by Alamar blue assays , and in some cases the EC50 was in fact higher for the CIA-depleted parasites ( S1 Table ) . Since TbMMS19 and TbCIA2B exhibited essentiality in T . brucei ( Fig 2A and 2C ) , we addressed the influence of these CTC components on the iron metabolism of the parasite . For this purpose , we used deferoxamine ( DFO ) , a siderophore that chelates Fe3+ but has no effect on iron bound to either haem or transferrin [38] and which starves the cells by sequestering the labile iron pool [39–41] . TbCIA2B RNAi cell lines were induced with tetracycline ( Tet ) for 24 hours and then challenged with different concentrations of DFO for 2 or 3 days ( PCF and BSF , respectively ) , when cell proliferation was measured . When depleted of TbCIA2B , both life stages were significantly more susceptible to DFO compared to those with normal levels of this protein ( Fig 3A and 3C ) , suggesting a decrease in the pool of available intracellular iron . A similar effect was observed upon TbMMS19 knock down in PCF cells ( Fig 3E ) . Additionally , WT PCF cells grown in the presence of Tet and treated with DFO under the same conditions displayed identical EC50 values as those grown in the absence of the antibiotics ( Fig 3I ) , confirming that this result was specifically due to TbCIA2B or TbMMS19 knockdown and not to the synergistic effects of DFO and Tet , which is also a chelator of polyvalent metal cations [42] . In agreement with this finding , no DFO toxicity was observed when TbCIA2B or TbMMS19 RNAi parasites were treated with drug pre-saturated with an excess of Fe3+ ( Fig 3B , 3D , 3F and 3H ) , strongly indicating that the enhanced sensitivity can be specifically attributed to iron depletion and not to off-target effects of DFO . Furthermore , ferene assays suggested that the content of iron bound to proteins in the cytosolic lysates of TbCIA2B knockdowns was lower than that found in uninduced cells , whereas protein-bound iron levels did not change in the organellar fractions ( Fig 3J ) . For functional complementation assays , TbCIA2A , TbCIA2B and TbMMS19 were PCR-amplified from genomic DNA and cloned into yeast expression vectors under the control of the TDH3 or MET25 promoters of Saccharomyces cerevisiae [25 , 43] . Plasmids without insert or plasmids encoding endogenous yeast CIA genes were used as controls . Subsequently , these constructs were transformed into regulatable yeast Gal-CIA mutants , in which the expression of the cognate CIA gene is induced in the presence of galactose and repressed by the presence of glucose as described elsewhere [25] . The growth defect of Mms19-depleted cells on glucose-containing medium was not restored by TbMMS19 expression , even when TbMMS19 was co-expressed with either TbCIA2A or TbCIA2B ( Fig 4A ) . Expression of TbCIA2B partially rescued the growth of Cia2-depleted cells , but TbCIA2A failed to do so ( Fig 4B ) . For both TbCIA2 proteins , co-expression with TbMMS19 not only failed to enhance the rescue , but exhibited a dominant negative phenotype ( Fig 4 ) . Interestingly , when TbMMS19 is co-expressed with Cia2 from S . cerevisiae ( Fig 4B ) , the same dominant negative-like effect is observed . These findings show that TbCIA2B can partially take over the role of its yeast counterpart , suggesting that it performs an orthologous function . Individual interactions of the CTC proteins had only been mapped in detail for a few representatives of the eukaryotic supergroup Opisthokonta [44] . Moreover , the progress made in the field of Fe-S biology in the past decade suggests Fe-S proteins are diverse and abundant in a typical eukaryotic cell , but remained overlooked due to the difficulties related to their instability under aerobic conditions . To the best of our knowledge , the dynamics of protein-protein interactions of the CTC had not been studied in any Excavata , with only a few examples of identification and functional studies of CTC components [45]; despite ~0 . 6% of the annotated proteins of T . brucei being predicted to contain Fe-S clusters [46–48] , its Fe-S proteome remains largely unexplored . One of the most valuable tools that contributed to expanding the list of mammalian Fe-S proteins was the use of mass spectrometry ( MS ) and affinity purifications to detect potential Fe-S proteins interacting with the human CIA targeting complex [13 , 14 , 49] . Therefore , aiming to gain insight into the composition of the T . brucei CTC and its interactions with cytosolic and nuclear Fe-S proteins , three complementary strategies for affinity purification/MS were devised: ( i ) in situ PTP-tagged CTC members in PCF were affinity purified by a two-step approach [50] , ( ii ) V5-tagged CTC members in PCF and iii ) V5-tagged CTC members in BSF were immunoaffinity purified using a technique suited for the detection of transient and/or weak interactions [51] . In all cases , MS detected proteins co-purifying with the tagged baits . Tandem affinity purifications were performed using PTP-TbCIA1 , PTP-TbCIA2B , or TbMMS19-PTP as baits , and mock purifications with wild type PCF parasites served as negative controls . Unfortunately , we were not able to purify PTP-TbCIA2A complexes by this method for reasons that remain unclear , but may be related to the PTP tag ( ~19 kDa ) being larger than TbCIA2A , which is a protein of ~17 kDa . SYPRO Ruby stained SDS-PAGE gels of the final PTP elutions are shown in Fig 5A–5D . This exercise revealed that PTP-tagged CTC components can be reciprocally co-purified , proving the existence of the canonical ternary complex ( TbCIA1-TbMMS19-TbCIA2B ) . In TbCIA2A-V5 pull-down assays ( S3 and S4 Tables ) , TbCIA1 , but not TbCIA2B or TbMMS19 , was found interacting with the bait protein , in a configuration reminiscent of that described for the human CTC [13–15] . Abundant proteins such as tubulins and the eukaryotic elongation factor 1α ( EF1α ) were present in control PTP purifications , but no detectable levels of CIA proteins were seen in these samples ( Fig 5A ) . A summary of the PTP/MS data for the co-purified CTC members is shown in S2 Table . In addition to this , affinity pull-downs with V5-tagged TbCIA1 , TbCIA2A , TbCIA2B , and TbMMS19 in PCF and BSF confirmed the reciprocal nature of the interactions and the presence of the similar complex configurations in both life stages ( S3 and S4 Tables ) , and also showed in PCF that TbCIA1 , TbCIA2A and TbMMS19 , but not TbCIA2B , co-immunoprecipitated with TbNAR1 , the upstream CIA component that mediates the transfer of Fe-S clusters from the early-acting part of the pathway to the CTC ( S3 Table ) . Moreover , co-IP performed with lysates of a double-tagged strain of PCF parasites co-expressing PTP-TbNAR1 and TbMMS19-HA further validated this interaction ( Fig 5E ) . Next , in an attempt to identify potential target Fe-S proteins , we investigated other proteins co-eluting with members of the CTC . The combined TAP/MS and co-IP experiments identified over 200 such proteins , most of them in association with TbMMS19 and/or TbCIA1 ( S5 Table ) . To inquire if these proteins were known or could be predicted to contain Fe-S clusters , the amino acid sequences of the hits were retrieved from the TriTryp database [46] and analysed with MetalPredator [48] , a tool to predict Fe-S clusters in polypeptide chains based on the presence of known Fe-S domains and metal-binding motifs . This analysis returned three positive hits: the catalytic subunit of Pol δ ( TbPOLD1 , Tb927 . 2 . 1800 ) , the class I cytosolic fumarate hydratase ( TbFHc , Tb927 . 3 . 4500 ) , and a putative radical SAM tRNA modification enzyme ( Tb927 . 6 . 3510 ) ( S5 Table ) . In order to further examine the interactions of the CTC with Fe-S proteins , the amino- or carboxy-terminal domains of TbMMS19 ( respectively , recombinant ( r ) TbMMS19-NTD and rTbMMS19-CTD ) were expressed in E . coli as GST-Strep-Tag II fusion proteins . Equimolar amounts of purified recombinant proteins or glutathione S-transferase ( GST ) , used as a negative control , were coupled to glutathione Sepharose 4B beads , incubated with soluble extracts of PCF parasites expressing HA-tagged TbFHc , and the interactions were assessed by Western blotting . This pull-down confirmed the interaction detected by TAP/MS and further showed that TbFHc was able to interact with rTbMMS19-NTD , but not rTbMMS19-CTD or GST alone ( Fig 5F ) . Also , a relatively low number of proteins were detected in PCF TAP/MS or V5 co-IP/MS experiments with TbCIA2B ( S5 Table ) . In order to verify possible protein-protein interactions of TbCIA2B that could not be detected by other methods , this protein was expressed in E . coli as a fusion with an N-terminal Strep-Tag II and a C-terminal hexahistidine tag ( rTbCIA2B ) , then immobilised to Strep-Tactin Sepharose and incubated with extracts of parasites expressing either tagged aconitase ( TbACO-HA ) or the DNA helicase XPD ( Xeroderma pigmentosum group D homologue , TbXPD-HA ) . As shown in Fig 5G , TbXPD interacts with rTbCIA2B . Moreover , TbACO also interacted with rTbCIA2B ( Fig 5G ) . This is in accordance with the results depicted in Fig 2G and 2I , where the silencing of TbCIA2B led to decreased cytosolic activity of TbACO . These results validate the position of the CTC as the late-acting module of the CIA machinery at the interface between the upstream TbNAR1 and the client Fe-S proteins . A summary of interactions detected by TAP/MS experiments and additionally confirmed by co-IPs is depicted in Fig 5H . The interaction with TbNAR1 was not observed in the V5-tagged co-immunoprecipitations performed in BSF trypanosomes ( S4 Table ) . This difference may reflect stage-specific requirement of the CIA pathway . Importantly , mutual interactions of the CTC members are the same in the BSF and PCF cells , although the sets of their targets differ from one another and require further analysis to determine their capability to bear an Fe-S cluster . Interestingly , several proteins captured by the PCF TAP/MS methodology show multiple clustered cysteine residues , including Cys-Pro dipeptide sequences ( Tb927 . 3 . 4360 , Tb927 . 7 . 4390 , Tb927 . 2 . 5130 and Tb927 . 8 . 4890 ) . We are currently pursuing the possibility that these proteins harbour an Fe-S cluster by heterologous expression and purification . Aiming to better understand the dynamics of the interactions amongst the CTC members , it was of interest to identify the site through which members of this complex interact . However , the amino acid sequence of TbMMS19 is poorly conserved when compared to its human or yeast homologues [52] , and no crystal structures of this protein have been elucidated so far . Nevertheless , in silico homology modelling of the tertiary structure of TbMMS19 using the Phyre2 server [53 , 54] suggested the overall architecture of an Armadillo-like protein that contains α-helical HEAT repeat motifs throughout its sequence [55 , 56] , as previously predicted for MMS19 in higher eukaryotes [57] . In human cells , CIA2B and CIAO1 interact with the tightly spaced HEAT repeats at a region of the MMS19 C-terminal domain [58] , whereas in TbMMS19 these motifs seem to be more loosely distributed and are more numerous ( S3 Fig ) . We hypothesised that the binding site for the CIA proteins would be different in trypanosomes , given the divergent amino acid composition and apparent different distribution of the repeats . To clarify this question , TbCIA2B-HA cell lines of PCF parasites were transfected with constructs derived from the pLEW82 vector that integrates into the non-transcribed spacer of the rDNA locus and allows strong ectopic overexpression of proteins in the presence of Tet [21] . We engineered cell lines in which the PTP-tagged full-length TbMMS19 ( PTP-TbMMS19 ) , its N-terminal ( PTP-TbMMS19-NTD ) or C-terminal domain ( PTP-TbMMS19-CTD ) can be conditionally overexpressed . Both the overexpression of the control PTP tag alone and PTP-TbMMS19 did not impact the cell growth ( Fig 6A and 6B , respectively ) . However , overexpression of PTP-TbMMS19-NTD caused a mild cell growth delay ( Fig 6C ) , whereas a prominent dominant-negative phenotype developed when excess PTP-TbMMS19-CTD was produced , causing near-arrest of the cell growth after two days in culture ( Fig 6D ) . Interestingly , overexpressing PTP-TbMMS19 or PTP-TbMMS19-CTD for two days resulted in ~2 and ~3-fold higher levels of TbCIA2B-HA , respectively , and this effect was sustained after 4 days of overexpression ( Fig 6E , 6G and 6H ) . Conversely , in cells induced to overexpress PTP-TbMMS19-NTD , the levels of TbCIA2B-HA remained unaltered ( Fig 6F and 6H ) . To investigate if the up-regulation of TbCIA2B by TbMMS19 or its C-terminal domain was dependent on their interaction , we performed co-IPs with extracts of parasites induced overnight to overexpress PTP-tagged TbMMS19 , TbMMS19-NTD or TbMMS19-CTD . Cells overexpressing only the PTP-tag or those not transfected with pLEW82 constructs were used as controls . Although lower levels of PTP-TbMMS19-NTD were observed when compared to those achieved for PTP-TbMMS19 or PTP-TbMMS19-CTD after induction , co-IP assays indicated that TbCIA2B-HA was able to bind PTP-TbMMS19 and PTP-TbMMS19-CTD , but not PTP-TbMMS19–NTD ( Fig 7B ) . Additional TAP/MS assays revealed that TbCIA1 was only detected in eluates from PTP-TbMMS19 and PTP-TbMMS19-CTD , but not PTP-TbMMS19-NTD ( S4 Fig ) , which implicates that TbMMS19-CTD acts as a docking site for the assembly of the ternary complex . To pinpoint the binding site of TbCIA2B within TbMMS19-CTD , we used recombinant fragments of the latter ( named C1-C5 , Fig 7A ) , expressed as GST-Strep-Tag II fusions . Equimolar amounts of C1-C5 or GST were bound to glutathione Sepharose 4B beads and incubated with cellular extracts of T . brucei expressing HA-tagged TbCIA2B ( Fig 7C ) or purified rTbCIA2B ( Fig 7D ) . Fragments C1-C4 were able to bind TbCIA2B-HA from cell lysates , although fragment C2 appears to bind with higher affinity , while the C5 fragment , containing only 1 HEAT domain , has very weak affinity ( Fig 7C ) . Moreover , fragments C1 , C2 , C4 and C5 captured purified rTbCIA2B and also in this case , C2 displayed the highest binding capacity ( Fig 7D ) . Curiously , C1 was less capable of binding to TbCIA2B than the smaller C2 or C3 fragments . One possible explanation is that C1 adopted a fold that hinders the ability of TbCIA2B to reach the interaction surface which could , in turn , be more accessible in C2 . We also observed that the C3 and C4 fragments have an enhanced ability to bind TbCIA2B in cell extracts in comparison to the purified recombinant protein , hinting at the presence of a factor that stabilises the complex . The C5 fragment interacted ( albeit weakly ) with recombinant and native TbCIA2B ( Fig 7C and 7D ) , suggesting that this repeat is the minimal structural unit necessary to form the TbMMS19-TbCIA2B complex . This fragment contains 50 amino acids that roughly correspond to the most C-terminal HEAT repeat in TbMMS19 , although such interaction likely spans a much larger contact surface , involving at least two HEAT repeats localized between the residues Val763 and Lys947 of TbMMS19 . Given the paucity of structural analyses for the CIA proteins , individually or in a complex , the 3D structures of TbMMS19 and TbCIA2B were modelled by homology using the Phyre2 server [53] . The predicted structures were subsequently used to generate models of protein-protein interaction by in silico docking with ClusPro [59] . Corroborating our experimental findings , the top scoring model for the TbMMS19-TbCIA2B complex correctly predicted that TbCIA2B should bind to TbMMS19–CTD ( Fig 7E ) . In fact , most of the top scoring models also pointed to the binding site of the C-terminal domain of TbMMS19 ( S5 Fig ) . Given this reassuring overlap between the experimental data and in silico predictions , we aimed to refine this analysis by examining the best complex model with PredHS , a tool that integrates analysis of structural and energetic properties to identify regions at the contact surface , which are more likely to be crucial for protein-protein interactions ( i . e . hot spots or hot regions of interaction ) [60 , 61] . This analysis suggested that although Thr808 seems to be important , a contiguous region of 12 amino acids in the TbMMS19-CTD ( Phe762-Thr773 ) could be essential for the interaction with TbCIA2B . These residues are depicted in a scale of red in Fig 7E . This model fits satisfactorily our experimental data , which indicated that the C2 fragment ( Val763-Lys947 ) binds tightly to rTbCIA2B , while the C3 fragment ( Thr808-Lys947 ) interacted ( very ) weakly with it , although this association was stabilised when the native protein was present in cell lysates . Collectively , these results indicate that TbCIA2B binds directly and tightly to the C-terminal domain of TbMMS19 , but this interaction is likely to require additional factors to stabilise the complex .
Since the subcellular localisation of the CIA components seems to depend upon the organism under study [34 , 62–66] , we aimed to clarify the cellular compartment in which the late-acting module of the CIA machinery was present in trypanosomes . For this aim , a combination of immunofluorescence and immunoblot analyses of detergent-permeabilised cell extracts localised all four studied proteins to the cytosol , in agreement with data from mammalian cells [13 , 15 , 67] . However , Mms19 in Schizosaccharomyces pombe , and Cia1 in S . cerevisiae are predominantly nuclear [34 , 68] . In the plant Arabidopsis thaliana , MMS19 is exclusively cytosolic , although other members of the CTC exist both in the nuclear and cytosolic compartments [66] . Moreover , Giardia intestinalis exhibits a dual localisation of Cia2 , between the intermembrane space of the mitosome and the cytosol [45] . TbMMS19 and TbCIA2B were shown to be essential for the survival of PCF , but their depletion exhibited only marginal defects in BSF trypanosomes . In human cells , the levels of CIA2B are greatly reduced when MMS19 is ablated [10 , 12 , 49] , yet MMS19 remains steady regardless of the absence of CIA2B , suggesting a tight regulation of CIA2B rather than reciprocal stabilisation between the interacting partners , since MMS19 prevents proteasomal degradation of CIA2B in a binding-dependent manner [49] . Interestingly , overexpressing the C-terminal domain of TbMMS19 produced a dominant-negative phenotype with severe defects on the cell growth and concomitant up-regulation of the TbCIA2B levels . One plausible explanation for this finding concerns the modes of interaction within the CTC , since the C-terminal domain of TbMMS19 appears to be the docking site of the targeting complex , as recently described also for human cells [58] . It is possible that high levels of this truncated protein can sequester TbCIA2B , TbCIA1 , as well as client proteins into non-functional complexes , thus depleting the cell of at least two CTC members and mimicking the effect of a double knockdown . On the other hand , the depletion of TbCIA2A does not affect PCF , and only has a mild effect in BSF . Though the MS data suggests non-redundant functions , such as the formation of different subcomplexes among various components of the CTC , the growth phenotype in the RNAi cell lines , as well as the capability of infection of BSF RNAi cell lines , hint at the possibility of function overlapping . However , residual proteins escaping RNAi knockdown may be sufficient to maintain the functionality of the CIA machinery . The effect of RNAi-mediated depletion of the late-acting CIA factors was monitored through the activity of TbACO . The CIA2A protein aids the maturation of iron regulatory protein 1 ( IRP1 ) , the human homologue of TbACO , and stabilizes IRP2 by Fe-S independent mechanisms , whereas CIA2B has a role in the maturation of numerous cytosolic and nuclear Fe-S proteins [15] . Conversely , the CIA proteins do not exert a direct impact on iron regulation in S . cerevisiae , and an IRP1-like mechanism has not been implicated in T . brucei iron regulation [32 , 69] Regardless , TbMMS19 and TbCIA2B were found to be essential for the activity of the cytosolic but not the mitochondrial fraction of this enzyme . This is in line with previous studies , which demonstrated that the mitochondrial pool of this enzyme is matured by the ISC pathway , the mitochondrial machinery for Fe-S biogenesis [70–72] , while the cytosolic fraction requires both the ISC and CIA machineries to obtain its cluster [25 , 73] . Furthermore , TbACO was shown to interact with TbCIA2B in dedicated pull-down assays . The growth complementation of Cia2-depleted yeast cells by TbCIA2B presents independent evidence for the functional conservation of this protein in the CTC . Taken together , the functional and physical interactions of the CTC with TbACO provide an example of a maturation mechanism of cytosolic Fe-S proteins in T . brucei . We observed that upon silencing of TbCIA2B or TbMMS19 , PCF cells displayed an enhanced sensitivity to the iron chelator deferoxamine , with EC50 values about 1 . 5 times lower than in uninduced controls . The specificity of this effect was confirmed by incubating trypanosomes with deferoxamine pre-saturated with iron , which abolished its toxicity . Furthermore , BSF parasites depleted of TbCIA2B also displayed equally lower EC50 values . This effect was consistent , although not as pronounced as in conditional null mutants of the cation channel mucolipin 1 that delivers iron to the cytosol of BSF flagellates [74] . Although IRP1-like mechanisms implicating the CIA machinery in iron sensing and regulation , such as those described in human cells [15] , seem unlikely to exist in T . brucei [32 , 69] , a role for unknown Fe-S cluster-containing factors in iron regulation cannot be completely ruled out . Deferoxamine acts by scavenging the cellular labile iron pool ( LIP ) , thus preventing incorporation of this element into the newly synthesised apo-proteins [75] . The precise composition of LIP is uncertain , but free iron is seldom present in the intracellular milieu , given its capacity to generate reactive oxygen species via the Fenton reaction [26 , 76] . The source of iron for the assembly of Fe-S clusters in the cytosol remains unknown , although one line of thought speculates that the scaffold proteins for Fe-S cluster assembly can bind LIP directly [77] . If this was the case , LIP depletion by deferoxamine would magnify an already impaired CIA function in cells depleted of TbCIA2B or TbMMS19 , thus explaining the increased sensitivity . The LIP is expected to account for 0 . 2 to 3% of total cellular iron , with its bulk bound to the cytosolic and/or mitochondrial proteins [78] . Lower levels of protein-bound iron were observed in the cytosol of PCF flagellates depleted of TbCIA2B but remained unchanged in organellar fractions , indicating that Fe-S proteins may comprise a considerable portion of the cytosolic iron content in T . brucei . Collectively , these data demonstrate that the CTC is essential for the survival of T . brucei in vitro but does not seem to have an influence on the pathogenicity of the parasite in in vivo mouse experiments . The CTC further functions in both the iron metabolism and the maturation of target Fe-S proteins . However , the processes of DNA damage repair appear to be more resilient to the depletion of the CTC in this excavate protist when compared to other eukaryotic systems , where they are strongly linked to the functionality of Fe-S assembly pathways [13 , 14 , 35–37] . This observation can be partially attributed to the unique mechanisms of nucleotide excision repair ( NER ) utilised by this parasite . In yeast and humans , XPD is part of the transcription factor complex TFIIH [79] . Along with XPB , XPD forms the core of this complex , acting together in transcription initiation and DNA repair [79] . In T . brucei , TbXPD and TbXPB are not part of the same complex [80] , nor do they respond to DNA damage in the same fashion [80 , 81] . Moreover , XPB exhibits two orthologues in this flagellate , known as TbXPB and TbXPB-R ( or TbXPBz ) , of which only the latter seems to be involved in NER independently of TFIIH [80 , 81] . Yet , contrary to yeast and humans , TbXPD knock-downs in PCF and BSF exhibit different growth phenotypes , the protein does not influence NER proficiency and seems to be mostly involved in transcription initiation [80 , 81] . The genetic , functional and physical interactions of XPD ( Rad3 in yeast ) with the late-acting members of the CIA machinery have been well described in various organisms , and the ternary CTC is necessary for efficient maturation of this protein [12 , 35 , 36 , 82] . Interestingly , TbXPD was undetectable in our TAP/MS and V5 co-IP/MS assays , which could suggest a lower affinity of this transient association with the CTC in T . brucei than that observed for its human and yeast counterparts , although an interaction with TbCIA2B was seen in a dedicated pull-down assay . It is also plausible that down-regulation of the CIA machinery can trigger compensatory mechanisms of DNA repair , which are Fe-S independent . An alternative explanation is that residual levels of the CTC components upon RNAi knockdown may be sufficient to maintain adequate levels of maturation of Fe-S proteins involved in DNA repair . We used a combination of TAP/MS , co-IP/MS and dedicated pull-downs to detect potential client Fe-S proteins of the CTC . This approach validated the interactions amongst the late-acting members of the CIA machinery . A relatively large number of proteins was found in ( transient ) association with the CTC , with only a few of them predicted to contain Fe-S clusters . These analyses revealed that the three core components of the canonical ternary CTC could indeed be reciprocally co-purified showing that the CTC is conserved to both life stages of T . brucei . Interestingly , in both PCF and BSF cells , TbCIA2A was only observed in complexes purified from PTP-TbCIA1 or TbCIA1-V5 , while TbMMS19 and TbCIA2B were not detected in co-IPs with V5-tagged TbCIA2A ( S2 , S3 and S4 Tables ) . Consistent with our findings , CIA2A was not reported as a core CTC member in the seminal studies that established the role of the ternary complex CIAO1-MMS19-CIA2B in the maturation of Fe-S proteins [13 , 14] . However , in HeLa cells CIAO1 was shown to associate with both CIA2A and CIA2B in a mutually exclusive fashion , with these complexes interacting selectively with distinct subsets of target proteins [15] . The lack of interaction , between TbCIA2A and either TbCIA2B or TbMMS19 indicates the existence of a binary complex comprised of TbCIA1 and TbCIA2A in a configuration that is reminiscent of that described in mammalian cells [15] , although the biological purpose of TbCIA2A or the complex it forms with TbCIA1 remains elusive at this time . It is worth mentioning here that a very weak interaction between TbCIA2A and TbMMS19 was detected in the BSF cells . We demonstrate that TbCIA2B interacts with the C-terminal domain of TbMMS19 . A schematic representation of the proposed model for the ternary T . brucei CTC is depicted in Fig 8 . The C-terminal domain of TbMMS19 ( TbMMS19-CTD ) acts as a docking site for the other two members of the trimeric complex , namely TbCIA2B and TbCIA1 . We can also conclude from our pull-down assays that TbCIA2B independently interacts with TbMMS19-CTD . In humans , the interaction between CIA2B and MMS19 has been shown to be vital not only for the stability of the CTC itself , but also for the association with client Fe-S proteins [58 , 83] . Interestingly , van Wietmarschen and colleagues [67] reported that in vitro translated murine CIA2B and MMS19 were not able to bind directly to each other , although both could interact with CIAO1 . However , in support of our observations , Odermatt and Gari [58] showed that CIA2B binds to the C-terminal HEAT repeats of MMS19 in HeLa cells , and similar results were observed in pull-down assays with purified human proteins [83] . The status of TbCIA1 in the CTC of T . brucei is less clear , since from our results we cannot distinguish whether its interaction with TbMMS19 depends on the presence of TbCIA2B . However , recombinant fragments of the C-terminal domain of TbMMS19 had an enhanced ability to bind TbCIA2B in cell extracts if compared to the purified recombinant protein . In agreement with these observations , human CIAO1 was reported to stabilise the interaction between CIA2B and the HEAT repeats at the C-terminal domain of MMS19 , forming a trimeric complex [58] . Thus , we favour the interpretation that TbCIA2B independently interacts with the C-terminal domain of TbMMS19 , yet this interaction may be further strengthened by other proteins , with TbCIA1 being a prime candidate . Considering that both TbCIA1 and TbCIA2B are involved in the maturation of Fe-S proteins , it is possible that the assembly of the clusters into apo-proteins takes place at this C-terminal docking site [58] . The binding site of TbCIA2B , as supported by in silico modelling of theTbCIA2B-TbMMS19 complex , was narrowed down to a region between the residues Val763-Lys947 of the C-terminal domain of TbMMS19 . The remarkable complementarity of the experimental observations with the in silico predictions allowed us to model the interface between the two proteins and identify residues likely involved in their interaction . However , bearing in mind that our data also strongly suggested the CTC exists in both binary and ternary versions , these simulations may not exactly reflect the whole scenario taking place at a cellular level . Since a reliable structural model for TbCIA1 could not be generated , we did not attempt to dock a ternary TbCIA2B-TbCIA1-TbMMS19 complex , or predict binary interactions of that protein . It is also important to recognise the caveats associated with this method , as homology-based structural models may not accurately reflect the minutia of biologically relevant conformations of proteins or complexes . Nevertheless , a similar approach has been successfully used to study the specificity of binding of the trans-acting acyltransferase to acyl-carrier proteins [84] and to design inhibitors of the human tumour necrosis factor [85] . Altogether , we believe our model provides a valuable snapshot of the TbMMS19-TbCIA2B interaction . The comprehensive analysis of protein-protein interactions for the CTC presented herein sheds light on the flexibility , as well as on the level of conservation of this ubiquitous eukaryotic pathway .
T . brucei PCF 29–13 [21] , and SmOxP927 [86] cell lines co-expressing T7 RNA polymerase ( T7RNAP ) and the Tet repressor ( TetR ) are referred to as wild-type in this study . The conditions for cultivation have been described elsewhere [87 , 88] . BSF cells used throughout were the single marker strain that constitutively expresses T7RNAP and TetR [21] , and were grown in Hirumi modified Iscove’s medium 11 ( HMI-11 ) [89] supplemented with G418 ( 2 . 5 μg mL-1 ) . BSF were grown at 37°C with 5% ( v/v ) CO2 in humidified atmosphere and kept at cell densities of 1 x 105 to 2 x 106 cells mL-1 and diluted with fresh HMI-11 media as required . For transfections , 10 μg of linearised constructs ( see below ) were electroporated into 1 x 107 to 2 x 107 cells using an Amaxa Nucleofector 2b device or BTX electroporator , as previously described [87 , 88] . Stable transformants were selected by clonal dilution in media containing the appropriate selection drugs . The sequences for all primers used in this study can be found in online supplementary material . RNAi constructs were prepared by amplifying fragments of TbCIA2A , TbCIA2B , and TbMMS19 flanked by BamHI and XhoI restriction sites and cloning into the p2T7-177 RNAi vectors [90] . TbMMS19 and TbCIA2A RNAi in PCF were obtained by Gibson assembly using the pTrypSon vector [91] . Double RNAi constructs were generated by ligating a second gene fragment in previously generated single RNAi constructs upon digestion with BamHI and SpeI as described before [25] . Constructs were linearized with NotI to allow integration into the silent 177 repeats of the T . brucei minichromosome prior to transfection into PCF 29–13 , SmOx or BSF single marker cells . Selection was carried out with 1 . 25 to 5 μg mL-1 phleomycin , or 4 μg mL-1 hygromycin B ( for the BSF TbMMS19 RNAi cell line ) . C-terminal in situ V5 tagging of the TbCIA proteins was performed as described [92] . PCR tagging was performed using a modified version of the pPOTv4 vector in which eYFP was replaced by the sequence of a triple V5 tag . PCR products were electroporated into PCF SmOxP927 cell line and selection was performed with 50 μg mL-1 hygromycin B . For C-terminal HA- or PTP-tagging , ~400–1 , 000 bp upstream of the termination codon of the genes of interest were inserted into the vectors pC-HA-BLA [93] or pC-PTP-PURO [94] using the KpnI and AflII restriction sites . N-terminal PTP-tagging constructs were generated by ligating pN-PTP-PURO [94] with ~400–1 , 200 bp downstream of the start codon of the respective CIA gene using NotI and KpnI restriction sites . The resulting plasmids were linearised at restriction sites within the inserts and transfected into PCF 29–13 or BSF single marker cells then selected with 20 μg mL-1 blasticidin or 2 μg mL-1 puromycin . The vector for conditional ectopic overexpression of N-terminally tagged proteins was constructed by amplifying the PTP-tag sequence from pN-PTP-PURO and ligating into to the plasmid pLEW82v4 [21] using 5’ PacI and 3’ HindIII restriction sites and adding a KpnI recognition sequence downstream of PacI to allow the introduction of the TbMMS19 ORF or sequences corresponding to its N- or C-terminal domain in frame with the PTP tag . pLEW82-PTP constructs were linearised with NotI for integration at the rRNA locus , transfected into TbCIA2B-HA PCF cells and selected with 5 μg mL-1 puromycin . Proteins were resolved by SDS-PAGE , transferred to PVDF or nitrocellulose membranes and blocked in phosphate-buffered saline ( PBS ) with 5% milk for 1 hr at room temperature ( RT ) . Blots were incubated with primary antibodies ( see below ) overnight at 4 oC , washed three times in PBS-T ( PBS supplemented with 0 . 1% Tween 20 ) , and incubated with the corresponding secondary antibodies for 1 hr at RT before further washes in PBS-T . Fluorescent signals were captured using the Odyssey CLx digital Imaging system ( Li-Cor Biosciences ) or chemiluminescent signals developed using the Clarity ECL substrate ( BioRad ) . Data for semi-quantitative Western blots were obtained by densitometry using the FIJI package for ImageJ [95] . The following primary antibodies were used in this study: mouse monoclonal α-V5 ( 1:1 . 000; Invitrogen ) , α-tubulin ( 1:10000 ) , α-TAO ( 1:100 ) [31] , and α-Strep-Tag ( 1:500; IBA Life Sciences ) , mouse polyclonal α-TbPLA1 ( 1:1000 ) [30] , rabbit polyclonal α-TbENO ( 1:2000; a gift from Paul A . M . Michels ) , α-mtHSP70 ( 1:1000 ) , α-protein A ( PAP antibody; 1:500; Sigma-Aldrich ) , and rat monoclonal 3F10 α-HA ( 1:1000; Sigma-Aldrich ) . In some experiments TbCIA2A and TbCIA2B were detected with specific antibodies ( both at 1:200 ) raised in rabbit using protocols described elsewhere [70–73] . Fluorescent secondary antibodies were goat IgG α-rabbit , α-mouse , or α-rat conjugated to IRDye680 or IRDye800 ( Li-Cor Biosciences ) . HRP-conjugated reagents were from Sigma-Aldrich . Preparation of C-terminal in situ-tagged TbCIA1-V5 , TbCIA2A-V5 , TcCIA2B-V5 and TbMMS19-V5 for confocal imaging was performed as described elsewhere , with minor modifications [88] . Cells were fixed with 4% ( w/v ) paraformaldehyde in phosphate buffered saline ( PBS ) , permeabilised with 0 . 2% ( v/v ) Triton X-100 in PBS on microscopy slides and then probed with primary antibodies in PBS/gelatin . Monoclonal α-V5 ( Life Technologies ) and polyclonal anti-TbENO antibodies were used at 1:1000 and 1:2000 dilution , respectively . As secondary antibodies , Alexa Fluor 488 anti-mouse and Alexa Fluor 555 anti-rabbit ( Life Technologies ) were used . DNA was visualized using ProLong Gold antifade reagent with DAPI ( Life Technologies ) . Confocal microscopy was performed using an inverted IX81 motorized FluoView FV1000 confocal ( Olympus ) microscope and detection was carried out with FV1000 software ( Olympus ) . Image analysis was performed using Magic Montage plugin for ImageJ [96] and FIJI [95] . For generation of structural homology models , amino acid sequences of proteins were submitted to Protein Homology/analogy Recognition Engine v . 2 . 0 ( Phyre2 ) [53] , available at http://www . sbg . bio . ic . ac . uk/phyre2/ , using either the normal or intensive modelling modes . The resulting PDB files with the 3D structure of proteins were visualised with MacPyMOL ( Schrodinger ) . PDB files with the 3D structures of proteins were used as input to the ClusPro docking server [59 , 97] , available at https://cluspro . bu . edu/home . php . TbMMS19 was defined as the receptor and TbCIA2B as ligand and all settings were kept as default . Output PBD files containing the top highest scoring models according to the balanced method were downloaded and visualised with MacPyMOL ( Schrodinger ) . PDB files with protein complexes were uploaded to the PredHS server [61] , available at http://www . predhs . org/ . The predicted interaction hot-spots on the surface of proteins were identified by the SVM algorithm and superimposed on the 3D structure of the complex using MacPyMOL ( Schrödinger ) . PCR amplified sequences corresponding to the N- or C-terminal domains of TbMMS19 , or fragments of the latter were cloned into using pGEX-6P-1 ( GE Healthcare ) , using the BamHI and NotI restriction sites . The sequence for a Strep-Tag II was included in the antisense primers to generate a C-terminal Strep-Tag II fusion in addition to the N-terminal GST tag encoded in the expression vector . TbCIA2B was cloned into pASK-IBA7plus ( IBA Life Sciences ) using EcoRI and EcoRV restriction sites and the sequence for a hexahistidine tag was included in the antisense primer to generate a C-terminal 6XHIS fusion in addition to the N-terminal Strep-Tag II present in the vector . Recombinant proteins were expressed in C43 ( DE3 ) pLysS E . coli [98] carrying the pRARE plasmid for rare codons , grown in terrific broth . rTbCIA2B was purified by immobilised metal affinity chromatography with Ni-NTA agarose ( Qiagen ) and eluted in EB1 ( 50 mM Tris . HCl , pH 9 , 250 mM NaCl , 0 . 1% Triton X-100 , 1 mM EDTA , 1 mM DTT , 400 mM imidazole , 10% glycerol ) . Fragments and domains of TbMMS19 were batch purified with Glutathione Sepharose 4B beads ( GE Healthcare ) , followed by Strep-Tactin ( IBA Life Sciences ) affinity purification and eluted in EB2 ( 50 mM Tris . HCl , pH 9 , 250 mM NaCl , 1% Triton X-100 , 0 . 5% sarkosyl , 1 mM EDTA , 1 mM DTT , 5 mM desthiobiotin , and 10% [v/v] glycerol ) . Tandem affinity purifications were performed following a standard protocol as described [99] , with minor modifications . Briefly , 2 . 5 litres of PCF expressing PTP tagged proteins were grown to late log phase , centrifuged and washed in ice-cold PBS . Cell pellets were suspended in TLB buffer ( 20 mM Hepes KOH pH 7 . 7 , 150 mM potassium glutamate , 150 mM sucrose , 3 mM MgCl2 , 2 mM DTT , 1% [v/v] Triton X-100 , Roche cOmplete EDTA-free protease inhibitor cocktail ) and lysed on ice with a Dounce homogenizer . Lysates were cleared by centrifugation , filtered into a 10 mL Poly-Prep column ( Bio-Rad ) and incubated with pre-equilibrated IgG Sepharose 6 Fast Flow resin ( GE Healthcare ) . The resin was washed with PA-150 , equilibrated with TEV buffer and incubated overnight with 400U of AcTEV protease ( Invitrogen ) . TEV eluates were collected , added to buffer PC-150 supplemented with 1 mM CaCl2 and protease inhibitors , then bound to a pre-equilibrated Anti-Protein C affinity matrix ( Sigma-Aldrich ) in another Poly-Prep . After extensive washes , proteins were eluted in 1 . 8 mL of EDTA/EGTA buffer and concentrated with StrataClean resin ( Agilent ) . The resin was pelleted , resuspended in NuPAGE LDS sample buffer ( Invitrogen ) , boiled at 95°C for 10 minutes , and the proteins were resolved in NuPage 4–12% Bis-Tris gels ( Invitrogen ) before staining with SYPRO Ruby ( Molecular Probes ) . Images were captured in a Typhoon FLA 7000 laser scanner ( GE Healthcare ) . Trypsin digests of excised gel sections were analysed by LC/MS in an ABSciex TripleTOF 5600+ mass spectrometer and the spectra were searched against a T . brucei protein database [46] using MASCOT . Proteins hits with less than 2 unique peptides were disregarded . PCF parasites were washed with ice cold PBS , resuspended in TLB buffer and quickly disrupted with glass beads in a FastPrep machine ( MP Biomedicals ) . Lysates were cleared by centrifugation ( 16 , 000 g , 30 minutes , 4°C ) and transferred to 1 . 5 mL tubes containing 25 μL of pre-equilibrated IgG Sepharose 6 Fast Flow resin ( GE Healthcare ) ( for assays with double-tagged cell lines ) or 200 pmoles of TbMMS19 GST-fusion proteins immobilised to 25 μL of Glutathione Sepharose 4B , and incubated with rotation for two hours at 4°C . Alternatively , immobilised proteins were incubated with 400 pmoles of purified rTbCIA2B . The resins were washed 4 times with 1 mL of TLB , resuspended in 25 μL of 2 X SDS-PAGE sample buffer and subsequently boiled at 95°C for 10 min . For Strep Tag pull-downs , 400 pmoles of rTbCIA2B were immobilised in 25 μL of Strep-Tactin resin and everything else performed as described above . Interactions were analysed by Western blot after SDS-PAGE . For V5 co-IP/MS , pellets of 3 x 109 PCF or BSF cells were suspended in PBS , snap-frozen in liquid nitrogen and grinded using a CryoGrinder ( OPS Diagnostic ) [51] . The cell powder was suspended in 500 μL of lysis buffer ( 20 mM HEPES , pH 7 . 4 , 150 mM Na-Citrate , 1 mM MgCl2 , 0 . 2 mM CaCl2 , 0 . 1% [v/v] Triton X-100 , and Roche cOmplete EDTA-free protease inhibitor cocktail ) . Cleared lysates were added to 12 μL of DynaBeads pre-cross-linked with anti-V5 antibody and incubated for two hours at 4°C . Beads were further washed with lysis buffer and proteins were eluted in 100 μL of elution buffer ( 25 mM Tris . HCl , pH 7 . 5 , 2% [v/v] SDS ) at 72°C for 10 minutes . Proteins were precipitated with ethanol , resolved by SDS-PAGE , visualised by silver staining and analysed by Western blot or mass spectrometry . Cell fractionation using a digitonin gradient was performed as described elsewhere [100] . For co-localisation , 1 x 107 cells were suspended in 100 μL of FB ( 20 mM Tris-HCl , 0 . 6 M sorbitol , 1 mM DTT , Roche cOmplete protease inhibitor cocktail; pH 7 . 5 ) containing concentrations of 0 . 01 to 1 mg mL-1 of digitonin . Cells were incubated on ice for 5 min and centrifuged at 16 , 000 g for 5 min at 4°C . The supernatant was transferred to a clean 1 . 5 mL tube and evaporated in a SpeedVac until almost dry . The pellet was suspended in 20 μL of 2 X SDS-PAGE sample buffer , boiled for 10 min at 95°C and resolved by SDS-PAGE . Protein release in each fraction was detected by a semi-quantitative Western blot . Cytosolic and organellar fractions for other assays were prepared by suspending 1 x 108 cells in 1 mL FB containing 0 . 15 mg mL-1 digitonin . The soluble supernatant was considered the cytosolic fraction . The pellet was washed once with 1 mL of FB , incubated for 10 min on ice with 1 mL FB with 0 . 5% ( v/v ) Triton X-100 and centrifuged at 16 , 000 g for 5 min at 4°C . The resulting supernatant was considered the organellar fraction . The cytosol was separated from the organellar fraction as described elsewhere [101] . Mid-log cells expressing the V5-tagged proteins were harvested at 1000 g for 10 min at 4°C , washed with ice cold SHE buffer ( 25 mM HEPES , pH 7 . 4 , 250 mM sucrose , 1 mM EDTA ) , resuspended in fresh SHE buffer to a final concentration of 5 x 109 cells mL-1 , and the protein concentration was determined according to Bradford . One milligram protein aliquots were suspended in Hanks’ balanced salt solution ( HBSS ) ( 1 . 26 mM CaCl2 , 5 . 33 mM KCl , 0 . 44 mM KH2PO4 , 0 . 81 mM MgSO4 , 138 mM NaCl , 4 mM NaHCO3 , 0 . 3 mM Na2HPO4 , 5 . 6 mM glucose , pH 7 . 3 ) and digitonin was added to the final concentration of 0 . 4 μg μL-1 . After vortexing , the suspension was incubated at RT for 5 min , and followed by centrifugation at 14000 g at RT for 2 min . The supernatant represented the cytosolic fraction , while the pellet was washed once with HBSS and then resuspended in HBSS containing 0 . 1% ( v/v ) Triton X-100 and incubated on ice for 5 min . After centrifugation , the supernatant was collected as the organellar fraction . The pellet was washed once more with HBSS and then resuspended in a volume equal to the previous two fractions and analyzed by Western blotting . This final pellet fraction contains proteins that are insoluble or strongly associated to membranes . Aconitase activity was measured as previously described by monitoring the increase of the absorbance at 240 nm due to the conversion of isocitrate into cis-aconitate [102] . Two hundred microliters of lysates were added to 1 . 3 mL of aconitase buffer ( 50 mM Tris . HCl , 1 mM DTT , 20 mM DL-isocitric acid or sodium citrate; pH 7 . 4 ) and incubated at 25°C . The rate of increase of the absorbance at 240 nm per min ( ΔA240 nm/min ) was monitored for 30 min in a Varian Cary 50 UV/Vis spectrophotometer . A blank reaction without cell lysate was run in parallel . Specific activity was obtained by dividing the measured aconitase activity ( mU mL-1 ) by protein concentration in the sample . Uninduced controls were considered as 100% of activity . Cellular fractions from digitonin fractionation were concentrated in a SpeedVac ( Thermo ) and the iron content measured by the Ferene method , as described by [103] , . The pellets were thoroughly suspended in 100 μL of milliQ water , mixed with 100 μL of 1% HCl , incubated for 10 min at 100°C , quickly cooled down on ice and centrifuged ( 12 , 000 g , 5 min ) . Subsequently , 500 μL of 7 . 5% ammonium acetate , 100 μL 4% ascorbic acid and 100 μL of 2 . 5% SDS were added to the samples and vortexed . The samples were centrifuged again ( 12 , 000 g , 10 min ) and 855 μL of the supernatant was transferred to a semi-micro cuvette to which 95 μL of 6 . 2 mM Ferene ( Sigma ) were added . The absorbance of the ferrous-ferene complex at 593 nm was corrected for turbidity by subtraction of the absorbance at 800 nm and measured in a Varian Cary 50 UV/Vis spectrophotometer . Iron content was estimated by interpolation from a standard curve of ferrous sulphate ( 2 , 000–12 . 5 ng ) using least squares linear regression . The Alamar Blue assay was used to assess viability of cells exposed to DNA damaging agents or DFO . In this assay , the resazurin salt is reduced to resorufin , which emits a fluorescent signal proportional to the number of viable cells [104] . Cell densities of exponentially growing cells were adjusted to 1 x 106 or 5 x 104 cells mL-1 for PCF and BSF trypanosomes , respectively , to generate a 2x working cell suspension . One hundred microliters of cell suspension were added in quadruplicate to 96-well plates containing 100 μL per well of 2-fold serial dilutions of drugs . Wells without drugs or without cells served as maximum growth control and blank , respectively . PCF cells were grown for 48 hrs at 28°C , while BSF were incubated for 72 hrs at 37°C , after which 10 μL of a 1 . 1 mg mL-1 solution of resazurin ( Sigma ) were dispensed to each well and the plates were incubated for another 6 hrs . The fluorescent signal was measured in a FLx800TM Microplate reader ( BioTek ) with excitation wavelength set at λ530 and emission at λ590 . All EC50s ( concentration of a compound that reduces cell growth by 50% ) were calculated by nonlinear regression using the software Prism 7 . 0 ( GraphPad Inc . ) . DFX , methyl methane sulfonate ( MMS ) , 4-nitroquinoline 1-oxide ( 4NQO ) , hydroxyurea , and camptothecin were purchased from Sigma-Aldrich , and phleomycin ( Zeocin ) was purchased from Thermo Fisher . Complementation experiments were carried out in Saccharomyces cerevisiae strain W303-1A as WT ( MATa , ura3-1 , ade2-1 , trp1-1 , his3-11 , 15 , leu2-3 , 112 ) . The galactose-regulatable mutants used were GalL-MMS19 and Gal-CIA2 [14 , 43] . The latter mutant strain was constructed by homologous recombination in which the upstream promoter region of CIA2 was replaced by a PCR product containing the NatNT2 resistance marker gene and the GAL promoter . PCR analysis of chromosomal DNA confirmed correct insertion of the promoter . Yeast cells were grown in minimal ( SC ) media , containing galactose or glucose at a concentration of 2% ( m/v ) [105] . The yeast MMS19-encoding gene was cloned into the SmaI and XhoI sites of the pRS424-TDH3 vector [106] . For control of the rescue by yeast Cia2 , the Cia2-encoding sequence with 500 bp natural promoter ( NP ) sequence was amplified from yeast DNA and cloned into the SacI and XhoI sites of pRS416-MET25 . TbCIA2A and TbCIA2B genes were amplified from T . brucei DNA and cloned into the BamHI and SalI sites of pRS416-MET25 . TbMMS19 ( Tb927 . 8 . 3920 ) was cloned into pRS424-TDH3 in two steps . SpeI-BamHI and BamHI-ClaI fragments were consecutively PCR-amplified and cloned . The BamHI site , which is lacking in the TbMMS19 gene , introduces silent mutations at amino acids 514–515 ( Gly-Ser ) . After transformation of plasmids into GalL-MMS19 or Gal-CIA2 cells , growth in liquid minimal media supplemented with 2% galactose was carried out for 16 h . Then cells were shifted to the same medium , but with 2% glucose for 16 h ( Gal-CIA2 ) or 16 and 24 h ( GalL-MMS19 ) . Cell suspensions were diluted to an optical density of 0 . 5 at 600 nm and 5 μl aliquots , including four consecutive 10-fold serial dilutions , were spotted on agar plates . Plates containing minimal media supplemented with 2% galactose or glucose were incubated at 30°C for 48 h and photographed . Mice had food and fresh water ad libitum . The experiment was approved by our institution’s Animal Ethics Committee . To determine the infectivity of trypanosomes depleted for TbCIA2B or TbMMS19 , six groups of BALB/C mice ( uninduced and RNAi-induced TbCIA2B , uninduced and RNAi-induced TbMMS19 , wild type single marker [WT SM] cells with and without doxycycline ) . Each group consisted of 5 females ( 8 to 9 weeks old ) which were infected intraperitoneally with 10 , 000 BSF cells . In their drinking water , the induced groups received 1 mg/ml doxycycline sweetened with 50 mg/ml sucrose , starting 2 days before the infection . The survival was recorded twice a day . Survival data was plotted using Prism 7 .
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Cytosolic and nuclear proteins containing iron-sulphur clusters ( Fe-S ) are essential for the survival of every extant eukaryotic cell . The biogenesis of Fe-S clusters and their insertion into proteins is accomplished through the cytosolic iron-sulphur protein assembly ( CIA ) machinery . Recently , the CIA factors that generate cytosolic Fe-S clusters were characterised in T . brucei , a unicellular parasite that causes diseases in humans and animals . However , an outstanding question in this organism is the way by which the CIA machinery directs and inserts newly formed Fe-S clusters into proteins . We found that the T . brucei proteins TbCIA2B and TbCIA1 assemble at a region of the C-terminal domain of a third protein , TbMMS19 , to form a complex labelled the CIA targeting complex ( CTC ) . The CTC interacts with TbNAR1 and with Fe-S proteins , meaning that the complex assists in the transfer of Fe-S clusters from the upstream members of the pathway into target Fe-S proteins . T . brucei cells depleted of CTC had decreased levels of protein-bound cytosolic iron , and lower activities of cytosolic aconitase , an enzyme that depends upon Fe-S clusters to function .
|
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] |
2018
|
Branched late-steps of the cytosolic iron-sulphur cluster assembly machinery of Trypanosoma brucei
|
Plant responses to changes in environmental conditions are mediated by a network of signaling events leading to downstream responses , including changes in gene expression and activation of cell death programs . Arabidopsis thaliana RADICAL-INDUCED CELL DEATH1 ( RCD1 ) has been proposed to regulate plant stress responses by protein-protein interactions with transcription factors . Furthermore , the rcd1 mutant has defective control of cell death in response to apoplastic reactive oxygen species ( ROS ) . Combining transcriptomic and functional genomics approaches we first used microarray analysis in a time series to study changes in gene expression after apoplastic ROS treatment in rcd1 . To identify a core set of cell death regulated genes , RCD1-regulated genes were clustered together with other array experiments from plants undergoing cell death or treated with various pathogens , plant hormones or other chemicals . Subsequently , selected rcd1 double mutants were constructed to further define the genetic requirements for the execution of apoplastic ROS induced cell death . Through the genetic analysis we identified WRKY70 and SGT1b as cell death regulators functioning downstream of RCD1 and show that quantitative rather than qualitative differences in gene expression related to cell death appeared to better explain the outcome . Allocation of plant energy to defenses diverts resources from growth . Recently , a plant response termed stress-induced morphogenic response ( SIMR ) was proposed to regulate the balance between defense and growth . Using a rcd1 double mutant collection we show that SIMR is mostly independent of the classical plant defense signaling pathways and that the redox balance is involved in development of SIMR .
Plants live in a world of change - fluctuating light , temperature , water availability and pathogen attack are among the conditions that require an appropriate response from the plant . Stress responses are energetically costly [1]–[3] , hence environmental and growth signals must be integrated and balanced . Reactive oxygen species ( ROS ) , such as superoxide and hydrogen peroxide , which can be generated in different subcellular compartments and fulfill signaling roles during both abiotic/biotic stress and development are among these key signals in plants [4]–[6] . While regulation of ROS production is to some extent understood , the mechanisms of ROS perception and downstream signaling are mostly unknown [7] . Activation of programmed cell death ( PCD ) is one of the aspects of plant defense responses where ROS play a crucial role [8]–[10] . The understanding of plant cell death execution lags behind that of mammals; however , some key differences have been identified . Apoptosis does not exist in plants; based on morphological criteria plant PCD has been categorized as vacuolar cell death and necrosis , plus several other types that are not easily categorized [11] . Most studies on PCD in the model plant Arabidopsis thaliana have been in the context of immune responses where a localized rapid cell death program termed the hypersensitive response ( HR ) is a feature of resistance [8] , [12] . The air pollutant ozone ( O3 ) , when applied in short and high pulses , mimics and activates the plant's own production of an apoplastic ROS burst , similar to that seen in immune responses [13] . Hence , O3 can be used as a tool to study the role of ROS during defense and PCD , where the PCD elicited by O3 shares many similarities with HR [10] . However , many of the regulatory steps governing plant PCD in different contexts remain to be elucidated . Massive changes in gene expression are observed during plant defense responses , after application of ROS treatments , and during cell death [14]–[19] . The requirement of changes in gene expression for cell death execution is illustrated by the reduction of O3 activated cell death by application of α-amanitin , a transcriptional inhibitor [10] . Furthermore , several lesion mimic mutants , which display spontaneous HR-like cell death in absence of pathogens [20] , or mutants with altered pathogen tolerance have been used in various experiments including suppressor mutant screens and protein interaction studies to identify more regulators of cell death . Some of these regulators are involved in epigenetics , transcription or mRNA processing and include the transcription factors MYB30 [21] , bZIP10 [22] , SR1 [23] , WRKY70 [24] , mRNA processing proteins MOS2 and MOS4 [25] , and chromatin remodeling factors to fine tune the transcriptional status of chromatin ( LAZ2 , encoding the histone methyltransferase SDG8; [26] ) . Although numerous abiotic/biotic stress microarray studies have been performed , relatively few experiments available in the public domain directly address the transcriptomics of cell death [27] . Further , the variety of cell death forms , and triggers used to initiate them complicate experimental design and hinder identification of cell death gene expression signatures that would not also simultaneously contain genes regulated during plant defense . Indeed cell death and defense are intimately linked , perhaps even inseparable . Clearly there is need for further analysis of gene expression during cell death in diverse experimental systems to identify potential core regulators of cell death execution . The O3 sensitive Arabidopsis mutant radical-induced cell death1 ( rcd1 ) is one of the mutants with defects in PCD control; it develops cell death lesions in response to a normally sub-lethal dose of apoplastic superoxide , but not H2O2 [28] , [29] . RCD1 belongs to a plant specific SRO ( SIMILAR TO RCD-ONE ) gene family with five other genes ( SRO1-SRO5 ) [30]–[32] , some of which are also involved in stress signaling [33] . RCD1 and its closest homologue SRO1 possess a nuclear localization signal and a WWE domain , which is predicted ( but not experimentally shown ) to be involved in protein-protein interactions [34] . RCD1 and SRO1 display unequal genetic redundancy: whereas the rcd1 mutant has pleiotropic phenotypes in development and stress responses , the sro1 mutant has only subtle phenotypes . However , the rcd1 sro1 double mutant has severe developmental phenotypes and requires rescue on sugar containing media to generate viable plants [31] , [35] , [36] . RCD1 has been shown to interact with 21 transcription factors via a novel C-terminal RST ( RCD1-SRO-TAF4 ) domain , and the known target genes of these interaction partners ( such as DREB2A , DEHYDRATION-RESPONSIVE ELEMENT BINDING2A ) have altered expression in rcd1 [31] . These results suggest that RCD1 may regulate PCD at the level of transcription . The balancing act of regulating growth while maintaining an appropriate level of defenses includes a response termed stress-induced morphogenic response ( SIMR ) [14] , [36]–[39] . SIMR includes inhibition of shoot elongation and stimulation of auxiliary branching [37] , [38] . SIMR is thought to be an adaptive response to stress and is regulated by a complex interplay between ROS , auxin , ethylene and antioxidants [14] , [37] , [40] . The morphological phenotypes of rcd1 indicate that it could be classified as a mutant with constitutively heightened SIMR response [14] , [36] , [38] . A screen for modulators of defense responses identified rcd1 as an enhancer of the growth inhibition caused by constitutive activation of defense responses in the mutant snc1 ( suppressor of npr1 , constituive1 ) , giving further support for a role of RCD1 in balancing between growth and defense [41] . A deeper understanding of SIMR could offer new breeding target ( s ) for plants with increased tolerance to abiotic/biotic stresses without accompanying growth defects . Apoplastic ROS , in the form of O3 , alter the expression of thousands of genes assigned to biotic and abiotic stress responses [14] , [42]; however there are only a few studies comparing both O3 sensitive and tolerant genotypes at gene expression level which is required to dissect signaling pathways involved in PCD regulation [43] . To gain deeper mechanistic understanding of this process we used the rcd1 mutant to perform: ( 1 ) microarray analysis of an O3 time course in comparison to Col-0 to explore the role of RCD1 in defense and PCD signaling; ( 2 ) analysis of genes differentially regulated in rcd1 gene expression data using public array experiments to find genes regulated during the PCD process; ( 3 ) detailed quantitative real time PCR ( qPCR ) analysis to find marker genes for PCD; ( 4 ) a screen of rcd1 double mutants to find regulators of apoplastic ROS induced PCD and SIMR . We identify through the genetic analysis WRKY70 and SGT1b as regulators of cell death in rcd1 and show that quantitative rather than qualitative differences in cell death gene expression appear to better explain outcomes in cell death .
The O3-induced transcriptional response in rcd1 was studied in order to define cell death signaling events and to address the potential role of RCD1 as a transcriptional co-regulator . Samples of rcd1 and Col-0 were collected 0 , 1 , 2 , 4 , 8 and 24 hours after the onset of O3 exposure ( 6 h 350 nL L−1 ) . All hybridizations were performed with two-color oligonucleotide microarrays against a common reference RNA , facilitating multidirectional comparisons between the genotypes and treatments . Data were analyzed with linear mixed models and genes having two-fold or higher change of expression ( log2 ratio ±1 , q<0 . 05 ) in at least one time point were regarded as differentially regulated in each comparison ( Figure 1A ) . In rcd1 , 4102 O3-responsive transcripts were identified over the experimental time course ( Figure 1A ) , slightly more than in the O3-tolerant Col-0 control ( 3635 transcripts , [14] ) . In total expression of 475 genes differed between the genotypes; 274 in response to O3 and 114 genes in clean air and 87 under both conditions ( Figure 1B ) . Comparison of the O3 responses of Col-0 and rcd1 revealed remarkable similarity; most O3-responsive transcripts ( 2897 ) overlapped between the genotypes ( Figure 1B ) . Transcript levels of altogether 4544 genes responsive to O3-treatment in one or both of the genotypes ( Figure 1B ) were directly compared to study the quantitative difference in the ROS response between the genotypes . First , lists of O3-induced and –repressed genes were created for each time point and genotype separately . At all time points , there were more O3-regulated genes in rcd1 than in Col-0 , and both genotypes possessed uniquely regulated genes , but no transcripts were oppositely regulated by apoplastic ROS in Col-0 and rcd1 backgrounds , i . e . , increased in one genotype and decreased in the other at the same time point . Therefore , the genotype-specific lists of regulated genes were combined and the differences in absolute levels between O3-treated rcd1 and O3-treated Col-0 were calculated . The magnitude of O3-induced changes was greater in rcd1 than in Col-0 at all O3 time points for both induced ( 64–77% of the O3-induced genes ) and repressed ( 58–91% of the O3-repressed genes ) transcripts ( Figure 1C and 1D , respectively ) . Therefore , O3-regulated genes had a more pronounced expression change in rcd1 , suggesting that the O3 response is quantitatively heightened in rcd1 . However , most of the differences between the genotypes were subtle , less than two-fold ( Figure 1C and 1D ) . Four genes were quantified in more detail with qPCR , and the results from this analysis were in agreement with the array data ( Figure 1E ) . At the whole tissue level , RCD1 transcript levels are slightly responsive to many stresses including O3 ( Figure S1 , [30] , [32] ) and show strong induction only in response to high light [44] . To test for local cellular responses in ROS-induced lesions , activity of the RCD1 promoter was monitored by β-glucuronidase ( GUS ) staining in RCD1-promoter::uidA fusion lines exposed to high O3-concentrations that induced lesions , or treated with the herbicide methyl viologen ( MV; paraquat ) which induces chloroplastic ROS production . The RCD1 promoter was active specifically in the cells inside O3-induced cell death lesions and also in the cells directly treated with MV ( Figure S2 ) . As a comparison , lines expressing uidA under control of the promoter from SRO1 , the paralog most similar to RCD1 , were also monitored . Although SRO1 and RCD1 share similar developmental expression [31] , unlike RCD1 , SRO1 expression was not increased in O3-induced lesions or in response to MV treatment ( Figure S1 ) . Another member of this gene family , SRO5 , regulates salt stress responses [33] and is induced by O3 [32] . The sro1 and sro5 mutants did not display increased O3 sensitivity and the rcd1 sro5 double mutant had unaltered responses compared to rcd1 ( Figure S2 ) . The lack of stress regulation of the SRO1 gene or O3 stress phenotypes in the sro1 and sro5 mutants suggests that RCD1 regulates ROS responses and cell death independent of SRO1 and SRO5 . RCD1 interacts with several transcription factors ( TFs ) and may regulate transcription via protein-protein interactions [31] , [45] . Many RCD1 interacting TFs have no established biological functions . Co-expression analysis has been used to suggest the function of previously uncharacterized proteins [46] . Expression profiles of 15 RCD1 interacting TF genes and RCD1 itself were studied in data sets comprising hormone- , abiotic stress- , biotic stress- and O3-treatments ( Figure S3; see below for a full discussion of data sets used ) . DREB2A , ANAC013 and ANAC046 were the only TFs with major expression changes in response to diverse stresses . ANAC013 is localized to both cytosol and nucleus [47] and is a possible membrane anchored protein that after proteolytic cleavage would move to the nucleus [48] . None of the TFs had altered expression in the rcd1 mutant under O3 , indicating that RCD1 does not transcriptionally regulate genes encoding its interaction partners . Overall , the RCD1 expression profile was not similar to that of its interaction partners , thus other types of data , for example in vivo protein stability or double mutants will be required to position RCD1 in stress signaling pathways . To gain further information on processes downstream of RCD1 , the expression profiles of 423 RCD1 regulated genes were clustered together with publicly available data from experiments performed on the Affymetrix ATH1 chip ( Figure 2 ) . These experiments were selected to distinguish between genes regulated by abiotic and biotic stresses , stress hormones , ROS and cell death ( see Materials and Methods for the complete set of experiments ) . Several constitutive defense mutants ( siz1 , sni1 [suppressor of npr1-1 , inducible1] , lht1 [lysine histidine transporter1] , cs26 [cysteine synthase26] ) clustered together with the salicylic acid ( SA ) analog benzothiadiazole ( BTH ) treatment ( Figure 2 ) . The strongest change in gene expression was in cell death associated treatments including Pseudomonas syringae pv . maculicola ( Psm ) ES4326 infection [49] , the acd11 ( accelerated cell death11 ) lesion mimic mutant [26] , and ROS challenged rcd1 at 8 and 24 h ( Figure 2 ) . These late O3 time points exhibited the largest differences between O3-treated rcd1 and Col-0 , whereas early O3 time points of both genotypes clustered together with H2O2 and flagellin 22 ( flg22 ) treatments ( Figure 2 ) . The rcd1 mutant in clean air had a unique gene expression profile , as discussed in detail below . RCD1-regulated genes clustered into six different major groups , two of which further divided into subclusters . These were further analyzed for enrichment of gene ontology ( GO ) classes and promoter elements ( Figure 2 , see Table S1 for genes belonging to each cluster and statistical results ) . Clusters Ia and Ib contained genes with reduced transcript accumulation in most experiments analyzed . Cluster Ib was enriched for genes encoding 17 chloroplast located proteins and 5 apoplast proteins ( Table S1 ) . Photosynthesis and chloroplast related genes have reduced expression during biotic stress as a defense strategy , likely for energy conservation [50] . However , photosynthesis as a biological process was not enriched in cluster Ib ( Table S1 ) . The heterogeneous cluster II included a few genes with increased expression in rcd1 clean air samples , increased expression in light grown COP9 signalosome mutants ( csn3 , csn4 , csn5; [51] and a late time point after treatment with the proteasome inhibitor Syringolin [52] ( Figure 2 ) . The COP9 signalosome regulates the activity and stability of cullin-RING-type E3 ubiquitin ligases ( CRL ) , and Arabidopsis csn mutants arrest growth at the seedling stage , possibly through a DNA damage pathway [51] . Through most other clusters ( I , IIIb–VI ) , expression profiles in csn mutants were very similar to acd11 , suggesting that they may undergo cell death during seedling growth arrest . However , in cluster II several genes had increased expression in csn mutants and in clean air rcd1 , but were not regulated by other stresses . E3 ligases are involved in targeting specific proteins for degradation by the proteasome , similarly one proposed function of RCD1 is to regulate stability of transcription factors [31] . The specific targets of COP9 and CRLs in Arabidopsis are mostly unknown , but more detailed characterization of genes regulated by both COP9 and RCD1 might reveal new insights into the role of protein degradation in stress responses . Genes in cluster IIIa had a trend towards increased expression in nearly all treatments studied and included ethylene biosynthesis and signaling genes ( ACS6 [1-AMINOCYCLOPROPANE-1-CARBOXYLIC ACID ( ACC ) SYNTHASE 6] , ACO2 [ACC OXIDASE 2] , ERF13 [ETHYLENE RESPONSE FACTOR13] , ERF104 , and the ROS signaling kinase OXI1 ( OXIDATIVE SIGNAL-INDUCIBLE1 ) [53] . Genes in cluster IIIb were characterized by very high expression in clean air rcd1 . This cluster of eleven genes contained AOX1a ( ALTERNATIVE OXIDASE 1A ) , UPOX1 ( UPREGULATED BY OXIDATIVE STRESS1 ) and a putative RCD1 interacting transcription factor ANAC013 [31] . The high expression of AOX1a and cluster IIIb genes indicated that rcd1 was under constitutive stress as previously proposed [30] , [31] . The major RCD1 interactor DREB2A is a regulator of both drought and heat stress [54] . Of the stress treatments included in this study , rcd1 expression profile in clean air shared the highest similarity to heat stress ( Figure 2 ) . Many DREB2A targets are also heat-responsive [54] , of which the NFXL1 ( NF-X-LIKE1 ) transcription factor , involved in heat acclimation [55] , was found in cluster IIIb . Therefore , the rcd1 mutant may have a heat tolerance phenotype in line with its higher accumulation of heat shock proteins [45] . Transcript levels in cluster IIIb were decreased by MV ( Figure 2 ) , which might be connected to the MV tolerance of rcd1 [29] , [30] . Further studies with cluster IIIb genes may also reveal pathways contributing to the MV tolerance of the rcd1 mutant . Intriguingly , the cell death regulator LAZ2 involved in chromatin remodeling [26] reversed the expression of cluster IIIb genes hinting that these genes may be involved in cell death and defense responses . Cluster IV genes generally exhibited increased transcript accumulation under most stress treatments including salt stress , high light and abscisic acid ( ABA ) ( 3 h ) , which suggested that expression of these genes is governed by a “general” stress regulatory circuit . Consistent with a role for ABA , NCED3 ( NINE-CIS-EPOXYCAROTENOID DIOXYGENASE3 ) , an early stress-induced ABA biosynthesis gene [56] , [57] was found in this cluster , and the GO category “response to water deprivation” was significantly enriched together with cis-elements related to ABA responses and abiotic stress ( Table S1 ) . ALD1 ( AGD2-LIKE DEFENSE RESPONSE PROTEIN 1 ) , a regulator of biotic stress responses and cell death [58] , was found is in this cluster together with transcription factors RAP2 . 6 ( RELATED TO AP2 6 ) , WRKY28 and ANAC019 . Genes in cluster V had reduced expression in clean air rcd1 and were strongly induced by O3 , biotic stress , ethylene , SA , BTH , senescence and in constitutive defense mutants . This cluster included multiple TFs , including WRKY18 , WRKY25 , WRKY48 , WRKY75 and bZIP60 . Promoters of cluster V genes were enriched in CONSERVED MOTIF2 ( CM2 ) , a binding site for CAMTA TFs [59] ( Table S1 ) . Furthermore , this cluster contained several regulators of biotic stress responses including PAD4 ( PHYTOALEXIN DEFICIENT4 ) , SAG101 ( SENESCENCE-ASSOCIATED GENE101 ) , FMO1 ( FLAVIN-DEPENDENT MONOOXYGENASE1 ) and NUDX6 ( NUCLEOSIDE DIPHOSPHATES LINKED TO SOME MOIETY X 6 ) . EDS1 ( ENHANCED DISEASE SUSCEPTIBILITY1 ) and its interacting partners PAD4 and SAG101 , are regulators of biotic stress , SA responses and ROS signaling [60] . Cluster VI had genes with strongly reduced expression in clean air rcd1 and increased expression by biotic stress , SA , BTH and in constitutive defense and cell death mutants . Expression of these genes was reduced by ethylene and cluster VI was the only cluster where ethylene gave the opposite result to SA/BTH . Reduced expression of these genes in the SA biosynthesis mutant sid2 ( salicylic acid induction deficient2 ) and the SA receptor/transcriptional co-factor npr1 ( nonexpressor of pathogenesis-related genes1 ) strongly suggested that SA and SA signaling were required for expression of these genes . Cluster VI included several direct targets of NPR1 , such as WRKY38 , WRKY54 and WRKY70 [61] and the SA marker genes PR-2 ( PATHOGENESIS-RELATED PROTEIN 2 ) and PR-5 ( Figure 2 ) . GO analysis revealed significant enrichment of “response to salicylic acid stimulus” and “response to bacterium” in cluster VI ( Table S1 ) . The SA-responsive cell death regulator ACD6 ( ACCELERATED CELL DEATH6 ) [62] also belonged to cluster VI . The rcd1 mutant in clean air did not display any similarity to the constitutive defense mutants sni1 , siz1 , cs26 , mkk1 mkk2 [mitogen activated protein kinase kinase1/2] or lht1 [63]–[67] , which all had high expression of SA and BTH responsive genes ( clusters V and VI ) . In contrast , rcd1 had reduced expression of these genes suggesting that RCD1 is a previously unrecognized positive regulator of SA signaling . In cluster VI , rcd1 in clean air was also very similar to plants with silenced apoplastic peroxidases [68] . Since the rcd1 mutant is specifically sensitive to apoplastic ROS , this expression profile similarity indicates a role for apoplastic signaling events resulting in lowered expression of SA responsive genes . Consistent with this interpretation , signaling activated by flg22 ( which is perceived by the FLAGELLIN SENSITIVE2 ( FLS2 ) receptor in the apoplast ) leads to reduced expression of SA responsive genes [69] . Importantly , this apoplastic signaling does not involve cluster IIIb genes regulated by heat stress , hence partially dissecting these signaling pathways . Overall the clustering of experiments indicated that there was no specific cell death profile . Under O3 , the early rcd1 time points clustered closely with Col-0 . Indeed , many of the genes with decreased expression in rcd1 in clean air , for instance WRKY70 , PR-2 and PR-5 , exhibited normal O3-induction in rcd1 ( Figure 2 ) . At late time points , post O3 treatment , expression levels nearly returned to basal clean air levels in Col-0 . In contrast , rcd1 at 8 and 24 hours maintained a highly altered expression profile similar to biotic stress treatments and the acd11 mutant undergoing cell death . Especially genes in cluster IV were strongly induced in these experiments ( Figure 2 ) . Cluster IV was enriched for GO classes related to water stress and the ABA response element ( ABRE ) , but not defense ( Table S1 ) , and therefore this suggests that cell death and ABA responses coincide at late time points . Spontaneous cell death mutants with associated high expression of various defense genes can arise from various disturbances in cellular homeostasis or signaling [20] , which makes it difficult to deconvolute the response to cell death versus activation of defense gene expression . Both basal and effector triggered immunity are qualitatively similar , i . e . , the same set of defense genes are induced , but in the latter there is a quantitative difference in that the genes are induced higher and faster [70] . We observed a similar phenomenon in the O3 response of rcd1 where apoplastic ROS induced a gene expression response qualitatively similar to Col-0 , but faster and to a higher level . Overall , clustering of genes differentially expressed in rcd1 suggests that the ROS-triggered lesion formation might depend on a fine-tuned threshold and timing , rather than of an on-off regulation of gene expression . Depending on the particular stress , the hormones ABA , SA , jasmonic acid ( JA ) and ethylene can show synergistic or antagonistic interactions [71] , [72] . SA and ethylene promote ROS-induced cell death , when JA antagonizes cell death [73] . The clustering clearly separated the function of the defense hormones ABA , JA , SA and ethylene in regulating ROS-induced gene expression in rcd1 ( Figure 2 ) . ABA and JA mostly had a minor role with only a few genes having strong induction ( ABA only regulated genes in cluster IV ) . In contrast SA/BTH at 24 h and ethylene at 4 h had a very similar and strong effect on many genes in clusters I and IV–V . Timing is clearly important since SA at 3 h did not have strong effect on clusters I and IV . Unfortunately , the only publicly available ethylene Affymetrix experiment using fully grown plants was done only at the 4 h time point [74] , and most public SA/BTH experiments only have late time points ( 8 or 24 h ) . Given this limitation , and the known kinetics for O3 induced biosynthesis of SA ( 5 h; [10] and ethylene ( 1 h or earlier; [28] , [75] ) it is likely that ethylene is the initial stress hormone , later being augmented with SA in regulation of O3 induced gene expression . One exception to the synergistic role of ethylene and SA were the cluster VI genes which were regulated positively by SA and negatively by ethylene . A similar SA-ethylene antagonism in gene expression was detected for a subset of genes responding to PsmES4326 treatment [76] . To further characterize marker genes and potential cell death regulatory genes , a subset of RCD1 regulated genes with functions related to defense , stress or cell death signaling were studied with qPCR in mutants related to cell death and defense signaling ( Figure 2 ) . These genes were NUDX6 , SAP12 ( STRESS-ASSOCIATED PROTEIN 12 ) and transcription factors RAP2 . 6 , WRKY38 , WRKY62 , WRKY70 , WRKY75 and ZAT12 . In addition , O3-responsive genes ACS6 , ALD1 , JAZ1 ( JASMONATE-ZIM-DOMAIN PROTEIN1 ) and FMO1 were included in the analysis . Some of these genes have also been shown to be regulators of cell death based on mutant analysis ( ald1 , fmo1 , [58] ) or to suppress constitutive defense signaling ( wrky70 , [24] ) . Before using these genes in O3 experiments they were validated in an independent cell death experimental system with lesion mimic mutants acd2 ( accelerated cell death2 ) , acd5 ( accelerated cell death5 ) and lsd1 ( lesion simulating disease resistance1 ) . The three lesion mimic mutants were selected to have contrasting mechanisms for lesion formation . ACD2 encodes a protein with multiple subcellular localizations ( chloroplast , mitochondria , cytosol ) which likely antagonizes cell death via binding to PCD-inducing metabolic products [77] . ACD5 encodes a ceramide kinase involved in lipid metabolism and signaling [78] , [79] , and LSD1 has been proposed to act as a cellular hub negatively regulating PCD by interacting with other proteins , such as bZIP10 [22] , AtMC1 ( METACASPASE1; [80] ) and GILP ( GSH-INDUCED LITAF DOMAIN PROTEIN; [81] ) . The lesion mimic mutants were first grown lesion free in permissive conditions for three weeks and then shifted to lesion-inducing long day ( LD ) conditions . Three days after the shift to LD , extensive lesion formation was observed in acd5 and lsd1 plants , and to a lesser extent in acd2 plants . Samples were harvested separately from Col-0 and from lesioned leaves ( + ) , lesion-free leaves from lesion-containing plants ( − ) and lesion free plants ( 0 ) . This harvesting scheme allowed the separation of gene expression effects before lesions ( the 0 samples ) , cell death ( the + samples ) and systemic signaling from dying cells ( the − samples ) . Overall , the expression of marker genes before visible lesions ( 0 samples ) was similar to Col-0 and increased with the appearance of lesions , especially in acd2 , and to some extent elevated in systemic ( − ) leaves ( Figure 3 ) . However , NUDX6 expression decreased in lesioned leaves ( Figure 3 ) . NUDX6 has pyrophosphohydrolase activity towards NADH and regulates redox balance and gene expression in SA signaling [82] . Its close homologue NUDX7 has been extensively characterized in ROS related cell death and defense responses , where knock-out mutants display spontaneous cell death , altered redox balance and constitutive defense gene expression [83]–[85] . SA signaling is dependent on redox changes of NPR1 [86] and NUDX6 is proposed to be involved in regulating this redox balance [82] . Since SA is a positive regulator of cell death [10] , [73] , and knockout of NUDX6 leads to increased sensitivity to SA [82] , the lower expression of NUDX6 in lesion-leaves could be involved in fine-tuning the redox balance and hence downstream events in cell death execution . Having established that the chosen set of marker genes were regulated during PCD ( Figure 3 ) , qPCR analysis of rcd1 and several other single and corresponding double mutants with rcd1 was performed at 2 and 8 h after the start of O3 treatment . The mutants were chosen to disrupt signaling of the major stress hormones JA , ethylene and SA ( coi1-16 the receptor for JA , etr1-1 a dominant negative allele of the ethylene receptor , npr1 the SA receptor and transcriptional co-regulator ) . Furthermore , two other mutants were included: mpk6 , a knockout for MAP KINASE6 , a regulator of many stress responses including O3 [87] , [88] and wrky70 , a positive regulator of SA signaling and negative regulator of JA signaling [89] . Previously we have established that removing JA function enhances , and depletion of SA reduces cell death in rcd1 [10] , [14] . The qPCR data was clustered with bootstrapped Bayesian hierarchical clustering to find similarities between genes and mutants ( Figure 4A ) and statistically analyzed with linear mixed models for differences to the respective Col-0 ( Figure 4B and 4C ) . In control conditions many differences were observed in the mutants , in particular ZAT12 expression appeared to be very sensitive to any perturbation since its expression increased in almost all mutants compared to Col-0 ( Figure 4C ) . Increased expression of WRKY38 , WRKY62 , ALD1 , FMO1 and NUDX6 in wrky70 suggests that WRKY70 is a negative regulator of these genes ( Figure 4C ) . NPR1 was a positive regulator of WRKY38 , WRKY62 and WRKY70 , consistent with previous analysis of NPR1 regulated genes [61] . JAZ1 is a transcriptional repressor of JA responses and is rapidly degraded after binding of the bioactive JA-Ile to the receptor COI1 [90] . The JAZ genes are also regulated at the transcriptional level by JA [91] , and in coi1-16 there was low JAZ1 expression ( Figure 4C ) ; collectively this indicates that JAZ1 makes a good marker for the output of JA signaling ( Figure 4A and 4B ) . Many of the genes ( SAP12 , ZAT12 , ACS6 , ERF104 , JAZ1 , ALD1 and WRKY75 ) were induced by apoplastic ROS early at 2 h , after which their expression started to decline 8 h after the start of the O3 treatment ( Figure 4A ) . ACS6 encodes a stress-inducible ethylene biosynthesis gene often used as a marker for ethylene signaling and consistent with this , ACS6 expression was lower after O3 in the etr1 mutant ( Figure 4B ) . The remaining marker genes ( FMO1 , RAP2 . 6 , NUDX6 , WRKY38 , WRKY62 and WRKY70 ) had the highest expression at 8 h ( Figure 4 A ) . The most dramatic change from WT expression pattern was seen for WRKY38 , and to a lesser extent WRKY62 in npr1 and rcd1 npr1 , where the O3 induction was abolished; hence , NPR1 is an essential positive O3 regulator of these genes ( Figure 4A and 4B ) . SA and Pseudomonas syringae induction of these WRKY TFs also requires NPR1 [92] , thus in particular WRKY38 can be used as a convenient reporter to follow signaling via NPR1 . In contrast , FMO1 expression was enhanced in npr1 , wrky70 and corresponding rcd1 double mutants ( Figure 4B ) , thus NPR1 has both positive and negative signaling roles in apoplastic ROS signaling . The coi1-16 mutant also revealed both positive and negative roles for JA signaling: RAP2 . 6 expression was reduced and NUDX6 enhanced at 2 h O3 in coi1-16 ( Figure 4B ) . Apparently , JA regulation is most important at early signaling since at 8 h there were no longer any differences compared to WT ( Figure 4B ) . The marker genes chosen are related to cell death or defense signaling , and were induced during lesion formation ( Figure 3 ) . Could they also provide insights to the cell death process in rcd1 ? The relative severity of cell death at 8 h is in the order rcd1 coi1-16>rcd1 , rcd1 mpk6 , coi1-16>rcd1 wrky70 , rcd1 npr1 ( [10] , [14]; Figure 5 ) , but there was no indication that rcd1 coi1-16 would be strikingly different from rcd1 at this time point , except for somewhat enhanced ACS6 expression . Instead , it appeared that signaling prior to visible lesion formation ( i . e . , the 2 h time point ) could determine the extent of later cell death . In particular NUDX6 expression was enhanced in the O3 sensitive rcd1 coi1-16 and coi1-16 . Although both the cluster analysis ( Figure 2 ) and qPCR results ( Figure 3 and 4 ) revealed some interesting correlations between cell death and gene expression , it appears that gene expression data alone is insufficient to identify “unique” cell death regulators , i . e . , genes that would be regulated only during cell death and not by other abiotic or biotic stresses . Identification of genes specifically regulated during cell death could , e . g . , be identified with a much higher temporal and spatial resolution , i . e . , cells undergoing cell death ( as well as neighboring cells ) would have to be microdissected out from leaves preferably in a time course [93] , [94] . Analysis of whole leaves/rosettes from dying plants is likely to contain a mix of cell death process and neighboring cells with activated defense responses . It is also a distinct possibility that unique cell death regulators might be rare and instead , regulators ( such as various transcription factors ) are recruited at different times to fulfill signaling roles both during stress and cell death . Cell death in rcd1 is reduced by application of a transcriptional inhibitor [10] . This suggests that some O3 responsive genes in rcd1 are candidate regulators of cell death . Also , many cell death regulators have been identified through the study of pathogen induced cell death . Mutant analysis was used to directly test the role of these cell death regulatory genes in O3-induced cell death . Based on rcd1 and O3 gene expression data , as well as known regulators of pathogen defenses and suppression of lesion mimic phenotypes , several rcd1 double mutants were constructed and evaluated for O3 induced PCD ( Figure 5 ) . Mutants included ald1 and fmo1 , suppressors of a SYNTAXIN lesion mimic mutant syp121 syp122 [58]; mpk6 , a regulator of stress responses [95]; abi4 , a TF regulator of stress signals originating from chloroplasts and mitochondria [96]; ein3 , a TF in the ethylene signaling pathway; wrky70 a TF with positive regulation of SA signaling and negative regulator of JA signaling [89]; rar1 ( required for MLA12 resistance ) and sgt1b ( suppressor of the G2 allele of SKP1b ) regulators of various aspects of pathogen defenses , SA signaling and PCD [97]; ndr1 ( nonrace specific disease resistance1 ) a regulator of ROS mediated cell death in lsd1 , and avirulent Pseudomonas infection [98] , [99]; mc1 ( metacsapase1 ) a positive regulator of PCD [80]; vtc2 ( vitamin C defective2 ) a mutant with low concentration of the important antioxidant ascorbic acid [100]; vpe-gamma ( vacuolar processing enzyme ) [101] , gpa1 and agb1 , the alpha and beta subunits of heterotrimeric G-protein signaling complex [102] . Despite the well-documented role for many of these genes in regulating cell death during pathogen infection and in lesion mimic mutants , only sgt1b and wrky70 were able to partially suppress O3 induced cell death in rcd1 ( Figure 5 ) . WRKY70 acts as an integrator between SA and JA signaling [89] . In addition , wrky70 was isolated from a suppressor screen for mutants that restore normal growth to a dwarfed constitutive defense mutant snc2-1D ( suppressor of npr1-1 , constitutive 2 ) [24] . Hence the decreased cell death in rcd1 wrky70 could be the result of reduced expression of a WRKY70-dependent positive regulator of cell death . SGT1b is an accessory factor to SCF ( Skp1/Cullin1/F-box ) E3 ligases , which are master regulators of ubiquitin targeted protein degradation [103] . Since the SCF E3 ligases have multiple targets in plants , most of which are unknown , we speculate that in rcd1 sgt1b there is negative regulator of cell death that is stabilized when SGT1b function is removed . The lack of influence on O3-induced cell death by other regulators previously shown to alter pathogen-induced cell death or to suppress lesion mimic phenotypes , including ald1 and fmo1 , indicate that despite many similarities between pathogen and O3-induced cell death [10] , the execution of the O3 cell death program requires different components . Alternatively , RCD1 could function as one of the final downstream steps in cell death execution , hence previously identified cell death regulators might be mostly up-stream of RCD1 and epistatic in double mutant analysis . A suppressor mutant screen of rcd1 has the potential to identify new regulators of PCD execution . Cluster IIIb ( Figure 2 ) contained genes with very high expression in clean air rcd1 and included two mitochondria localized proteins AOX1a and UPOX1 . AOX1a acts to bypass the last step of mitochondrial electron transport and is proposed to reduce ROS production in times of stress and to act as a regulator of PCD [104] . To determine whether constitutively higher expression of these two genes is committing rcd1 for PCD , respective rcd1 double mutants were constructed . Furthermore , plants overexpressing AOX1a ( AOX1a OE ) or a constitutively active AOX1a ( AOX1a OE-CA ) were included in the experiments [105] . In contrast to the situation in tobacco where overexpression of AOX1 leads to O3 sensitivity [106] , there was no increased cell death in any of the AOX1a OE or AOX1a OE-CA lines , nor in the single aox1a , and no changes in the O3 damage in rcd1-1 aox1a plants in comparison to rcd1 ( Figure 6 ) . Increased AOX activity has been proposed to lead to stress tolerance by reducing mitochondrial ROS production and/or maintenance of mitochondrial function during stress [104] , [107] . However , the lack of O3 phenotypes in various transgenic lines with altered AOX1a levels suggest that the role of mitochondrial ROS production during cell death is more complicated than anticipated . O3-induced cell death in Col-0 and rcd1 was also independent of the mitochondrial protein UPOX1 [108] ( Figure 6 ) , which is universally induced by oxidative stress [15] . Recently , cytosolic localization of UPOX1 was also demonstrated [47] . It should be noted that the T-DNA insertion in UPOX1 is located at the end of the coding sequence and would only remove the last five amino acids of the protein , and although there was an altered UPOX1 transcript size in upox1 and rcd1 upox1 ( data not shown ) , these plants may still have a functional protein . Apparently , neither AOX1a nor UPOX1 modulate apoplastic ROS cell death of rcd1 . Regulation of AOX1a expression has been extensively studied to reveal components of stress and/or mitochondrial retrograde signaling [109] , [110] . A mutant screen for regulators of AOX1a expression identified rao1 ( regulator of alternative oxidase1 ) , encoding CYCLIN-DEPENDENT KINASE E1 ( CDKE1 ) , as a regulator of stress and growth responses [111] . Since both CDKE1 and RCD1 are regulators of AOX1a expression this prompted us to compare the expression profiles of both mutants . Of 423 genes misregulated in rcd1 ( Figure 2 ) , 123 were also misregulated in rao1 [111] . Subsequent clustering of these genes using raw data from [111] and the rcd1/O3 data did not reveal any striking similarities between the two mutants ( data not shown ) . However , the opposite AOX1a expression phenotypes of the two mutants , i . e . , higher expression in rcd1 and lower in rao1 , suggest that rcd1 may be a valuable tool to further dissect mitochondrial retrograde signaling . The rcd1 mutant displays an altered growth phenotype indicative of constitutive stress-induced morphogenic response ( SIMR ) , a growth response including inhibition of shoot elongation and stimulation of auxiliary branching [37] , [38] . SIMR is thought to be an adaptive response to stress and is regulated by a complex interplay between ROS , auxin , ethylene and antioxidants [14] , [37] . The effect of ROS may at least partially be mediated by direct oxidation of indole-3-acetic acid to form inactive 2-oxindole-3-acid acid [112] . Since rcd1 displays constitutive SIMR , the rcd1 double mutant collection allows the dissection of signaling pathways contributing to SIMR . Most double mutants exhibited no alterations in rcd1 growth habitus , thus excluding a role for ethylene , SA , JA as well as several other defense regulators in the regulation of SIMR ( Table 1 ) . RCD1 has also been shown to regulate growth suppression down-stream from defense activation , a response dependent on ROS production and proper redox balance [41] . The few rcd1 double mutants with altered growth phenotypes include rcd1 axr1 , which displayed an additive growth inhibition [14] thus highlighting a role for auxin in regulation of SIMR . Furthermore , both ascorbic acid biosynthesis mutants , vtc1 and vtc2 , enhanced growth suppression of rcd1 ( Figure 7 ) . In conclusion , the SIMR response was governed by a set of regulators distinct from classical defense signaling and the rcd1 mutant represents a useful tool in dissecting SIMR . Numerous regulators of cell death have been identified through work on plant PCD and lesion mimic mutants . O3-elicited cell death in rcd1 requires a partially distinct set of regulators , indicating further complexity in plant cell death regulation . At the gene expression level quantitatively higher expression of stress related genes was more important than qualitative difference in individual genes . The identification of wrky70 and sgt1b as partial suppressors of rcd1 cell death indicate that there are more cell death regulators to be identified by studying WRKY70 target genes and SGT1B –SCF E3-ligase target proteins . The convergence of stress , growth response and mitochondrial retrograde signaling in RCD1 , its nuclear localization and interaction with various TFs , indicate a role for RCD1 in transcriptional regulation or possibly chromatin regulation or other epigenetic changes .
The growth conditions used and collection of plant material for microarray experiments are described in [14] . In brief , Arabidopsis thaliana ecotype Col-0 and rcd1-1 were grown in controlled environment chambers ( Weiss Bio1300; Weiss Gallenkamp , ( http://www . weiss-gallenkamp . com/ ) with 12-h/12-h ( day/night ) cycle , temperature 22/19°C , relative humidity 70/90% . O3 experiments ( 6 hours of 350 nL L−1 ) were performed with three-week-old plants , which were collected at 0 , 1 , 2 , 4 , 8 and 24 h after the start of the O3 treatment . The experiment was repeated three times , in addition to which a fourth identical repeat was used as the common reference RNA . Lesion mimic mutants acd2-2 and lsd1-3 [aka chs4-1 [113]] and T-DNA knockouts were obtained from NASC ( http://arabidopsis . info/ ) and acd5 was a gift from Dr Jean Greenberg . Lesion mimics were grown in growth rooms with the same conditions as above for 22 days ( no lesions were visible at this point ) , and then moved to long day greenhouse to induce lesions ( 18/6 h day/night ) . Samples were harvested 72 hours later . Col-0 and lesion mimic genotypes with no visible lesions were marked with 0 , individuals with lesion leaves were marked with + and leaves without lesions from the same plant as lesion leaves were marked with - . In this experimental design leaves undergoing cell death ( + leaves ) are separated from systemic leaves ( − leaves ) which might receive a signal from dying leaves; and leaves/plants which have not yet started the cell death program ( 0 leaves ) . Plants were harvested for cell death quantification with ion leakage after 6 h of 350 ( or 400 nL L−1 ) of O3 and 2 h in clean air into 15 ml MilliQ-water . Ion leakage caused by O3 was measured with a conductivity meter 2 h after harvesting . For total ion content measurements , samples were frozen and thawed to release the content of cells . The rcd1 double mutants were constructed with rcd1-1 or rcd1-4 as pollen acceptor and various other defense related mutants as pollen donors , see Table S2 for full details . Mutants were obtained from NASC ( http://arabidopsis . info/ ) or were gifts from Dr Günter Brader ( wrky70 ) , Dr Hans Thordal-Christensen ( ald1 and fmo1 ) , Dr Patricia Conklin ( vtc1 and vtc2 ) , Dr Heribert Hirt ( mpk6 ) , Dr Tesfay Mengiste ( bos1 ) , Dr . Jeff Dangl ( ndr1 , mc1 , rar1-21 , and edm1 ) , Dr Alan Jones ( gpa1 , agb1 ) and Dr . Ikuko Hara-Nishimura ( vpe-gamma ) . Double mutants were initially screened for the visible phenotype of rcd1 ( curly leaves and compact rosette ) , subsequently the mutations were verified with PCR based markers ( Table S2 ) . Other rcd1 double mutants have been described previously [10] , [14] . GUS staining and RCD1 and SRO1 promoter uidA-lines were described previously [31] . Arabidopsis overexpressing AOX1a or a constitutively active AOX1a and vector controls are described in [105] and [114] . These lines are available from NASC with the stock codes N6589–N6598 . Initially all lines were screened for O3 sensitivity , subsequently lines N6589 , N6591 and N6595 were characterized in more detail . RNA extraction , microarray hybridizations , data preprocessing and analysis with scripts in R are reported in [14] . Genes with at least 2-fold change in expression with statistical significance q<0 . 05 were considered as differentially expressed in each comparison made between treatments , genotypes and time points . Overlap between multiple gene lists was studied with Venn diagrams [115] . Gene expression data is deposited into ArrayExpress , accession number: E-MTAB-662 . The raw . cel files were downloaded from public databases , normalized with Robust Multi-array Average ( RMA ) normalization , and manually annotated to control and treatment conditions . For each experiment the log2-base fold changes of treatment versus control were computed . The preprocessed data was clustered using bootstrapped Bayesian hierarchical clustering as described in [116] . Publicly available experiments using the Affymetrix ATH1-121501 platform were obtained from several data sources: NASC Arrays http://affymetrix . arabidopsis . info/narrays/experimentbrowse . pl ( ABA - NASCARRAYS-176; CHX - NASCARRAYS-189; MG132 - NASCARRAYS-190; SA experiment 1 - NASCARRAYS-192; BTH experiment 1 - NASCARRAYS-392; ZAT12 OEX experiment 1 - NASCARRAYS-353; Senescence experiment 1 - NASCARRAYS-52; Senescence experiment 2 - NASCARRAYS-150 ) . ArrayExpress http://www . ebi . ac . uk/microarrayas/ae/ ( MeJA - EATMX-13; PQ - E-ATMX-28; Syringolin A - E-MEXP-739 ) . Gene Expression Omnibus http://www . ncbi . nlm . nih . gov/geo/ ( H2O2 and ZAT12 OEX experiment 2 - GSE5530; Salt - GSE5623; Heat - GSE19603; High light - GSE7743; Flg22 - GSE5615; sid2 - GSE9955; lht1 - GSE19109; edr1 - GSE26679; Pseudomonas syringae ES4326 - GSE18978; sni1 - GSE6827; csn3 , csn4 and csn5 - GSE9728; cs26 long day - GSE19241; Norflurazon - GSE12887; SA experiment 2 - GSE14961; siz1 - GSE6583; BTH experiment 2 and mkk1mkk2 - GSE10646; Ethylene and amiR-ETP1/2 ( constitutive ethylene response ) - GSE14247; npr1 - GSE13833; ERF104 OEX - GSE11807; Botrytis cinerea infection - GSE5684 ) . The Integrated Microarray Database System http://ausubellab . mgh . harvard . edu/imds ( Experiment names: NPR1 direct targets full genome , FBP1-antisense transgenic and Local and systemic responses to Trichoplusia ni feeding ) . Raw data for wrky33 , acd11 , laz1 and laz2 [26] , [117] were obtained from Dr John Mundy . Raw data for atx1 [118] was obtained from Dr Zoya Avramova . Gene ontology enrichment analysis was carried out with GO information downloaded from TAIR ( ftp://ftp . arabidopsis . org/ ) on 30th November 2010 . Enrichment was computed with scripts in R implementing Fisher exact test for the set of genes in each cluster in comparison to the clustering gene list ( 423 genes ) . Promoter analysis of the genes was carried out with 500 base pair upstream promoter sequences from TAIR 10 , available from ftp://ftp . arabidopsis . org/ . Matching of 196 known binding motifs was carried out with scripts in R for both plus and minus DNA strands of the promoter areas as described by [14] . Cluster-specific enrichment of motifs was determined with Fisher exact test . Gene expression analysis of selected marker genes was performed with qRT-PCR ( Table S2 has primer sequences and primer amplification efficiencies ) . RNA was isolated and treated with DNaseI as in [31] . Reverse transcription was performed with 5 µg of RNA with RevertAid Premium RT and Ribolock RNase inhibitor ( Thermo Scientific Fermentas ) and the reaction diluted to the final volume of 100 µl . PCR was performed in triplicate using iQ SYBR GREEN supermix ( Bio-Rad ) . The cycle conditions with Bio-Rad CFX384 were: 1 cycle initiating with 95°C 10 min , 39 cycles with 95°C 15 s , 60°C 30 s , 72°C 30 s and ending with melting curve analysis . Normalization of the data was performed in qBase 2 . 0 ( Biogazelle ) , with three reference genes TIP41 , At5g08290 and PP2AA3 selected from [119] and validated with geNorm to have stable expression in the samples used in this study . Primer amplification efficiencies were determined in qBase from a cDNA dilution series . Statistical significances in qPCR data were evaluated with scripts in R . In statistical analysis , a 2-base logarithm was first taken from the data to improve the model fit . Then a linear mixed model was fitted in using R package nlme , having fixed effects for Genotype , Treatment and Time and their interactions , plus a random effect for the biological repeat . The model contrasts were then computed with multcomp package , and the subsequent p-values were adjusted for multiple comparisons by Benjamini-Hochberg correction . Statistical analysis of ion leakage data was carried out with scripts in R . A linear mixed model with fixed effects for Genotype , Treatment and their interaction was fitted to the data , plus a random effect for biological repeat . The model contrasts were estimated with multcomp package , and the estimated p-values were subjected to single-step p-value correction .
|
Reactive oxygen species ( ROS ) are utilized in plants as signaling molecules to regulate development , stress responses and cell death . One extreme form of defense uses programmed cell death ( PCD ) in a scorched earth strategy to deliberately kill off cells invaded by a pathogen . Compared to animals , the regulation of plant PCD remains largely uncharacterized , particularly with regard to how ROS regulate changes in gene expression leading to PCD . Using comparative transcriptome analysis of mutants deficient in PCD regulation and publicly available cell death microarray data , we show that quantitative rather than qualitative differences in cell death gene expression appear to better explain the cell death response . In a genetic analysis with double mutants we also found the transcription factor WRKY70 and a component of ubiquitin mediated protein degradation , SGT1b , to be involved in regulation of ROS induced PCD .
|
[
"Abstract",
"Introduction",
"Results",
"and",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"plant",
"science",
"gene",
"expression",
"plant",
"genomics",
"plant",
"genetics",
"biology",
"molecular",
"cell",
"biology"
] |
2014
|
Transcriptomics and Functional Genomics of ROS-Induced Cell Death Regulation by RADICAL-INDUCED CELL DEATH1
|
A causative role for single nucleotide polymorphisms ( SNPs ) in many genetic disorders has become evident through numerous genome-wide association studies . However , identification of these common causal variants and the molecular mechanisms underlying these associations remains a major challenge . Differential transcription factor binding at a SNP resulting in altered gene expression is one possible mechanism . Here we apply PWAS ( “proteome-wide analysis of SNPs” ) , a methodology based on quantitative mass spectrometry that enables rapid screening of SNPs for differential transcription factor binding , to 12 SNPs that are highly associated with type 1 diabetes at the IL2RA locus , encoding the interleukin-2 receptor CD25 . We report differential , allele-specific binding of the transcription factors RUNX1 , LEF1 , CREB , and TFAP4 to IL2RA SNPs rs12722508*A , rs12722522*C , rs41295061*A , and rs2104286*A and demonstrate the functional influence of RUNX1 at rs12722508 by reporter gene assay . Thus , PWAS may be able to contribute to our understanding of the molecular consequences of human genetic variability underpinning susceptibility to multi-factorial disease .
Genome-wide association studies ( GWAS ) of common diseases typically result in the identification of genomic susceptibility loci , in which several single nucleotide polymorphisms ( SNPs ) showing strong inter-marker linkage disequilibrium ( LD ) are equally associated with disease predisposition . Further fine-mapping and re-sequencing studies can then uncover additional SNPs , ideally including those that are causal in the disease etiopathogenesis [1] , [2] . However , the SNPs that are most associated with the disease are commonly located in non-coding regions where they have no obvious function . Such SNPs presumably alter expression of a nearby gene via differential transcription factor ( TF ) binding or by influencing gene splicing . To date , there are few published examples in which a GWAS-identified SNP ( s ) is correlated with TF binding . We therefore set out to develop an unbiased , sensitive and streamlined method for detection of SNP sequences that differentially bind protein in an allele-specific manner . Affinity purification combined with mass spectrometry ( AP-MS ) can be a powerful tool to study protein interactions particularly when using a quantitative filter to distinguish specific interactors from the vast majority of background binders by their isotope ratio in the mass spectrometer [3] , [4] . The binding of transcription factors to DNA is predominantly mediated by interactions with the phosphate backbone of the DNA . Analysis of differential interactions due to single nucleotide changes is challenging because SNP-related differences in binding affinity are typically low . As a consequence , binding differences are small , even for sequences mutated at multiple positions [5]–[8] . Here we describe PWAS , a technique to study differential transcription factor binding to nucleotide sequences in a streamlined manner . To demonstrate PWAS in a disease relevant context , we also report differential transcription factor binding to type 1 diabetes- ( T1D- ) associated SNPs at the IL2RA or CD25 locus .
We improved a recently described technology for DNA affinity capture by quantitative mass spectrometry [5] and developed a pipeline for routine screening of SNPs . To establish an automated protocol for SNP screening , we used a single low stringency buffer for immobilization of the oligonucleotides , incubation with the extracts , and washes . To counterbalance this increased complexity in the lysates , we increased the density of binding sites by concatenation of chemical synthesized DNA oligonucleotides resulting in a greater enrichment of transcription factors . A high enrichment is desirable in mass spectrometric experiments with data-dependent acquisition in order to ensure identification of the desired binding proteins among the majority of peptides originating from background proteins . The chosen TT/AA-overhang further allows incorporation of modified nucleotides by Klenow polymerase . Previously we had used a biotinylated oligonucleotide , which we removed by restriction enzyme cleavage [5] , but this introduced a large amount of exogenous protein into the analyzed sample . Here , we performed strand-specific labeling with a desthiobiotin-analog that can be removed conveniently by competition with biotin ( Figure 1 ) . The desthiobiotinylated oligonucleotides of the two alleles were then incubated with either light or heavy nuclear extract in parallel . After mild washing , both bead fractions were combined prior to release of the desthiobiotin-labeled oligonucleotide by biotin . We found that PWAS detected differential binding to SNP alleles with great sensitivity . It employs approximately 40 bp of synthetic DNA containing either variant of the SNP , relatively small amounts of nuclear extract ( 200 µg ) that are labeled by SILAC ( stable isotope labeling by amino acid in cell culture ) [9] , [10] , single , high resolution mass spectrometric runs , and proved sufficiently simple and robust to be automatable in a robotic format ( Figure 1 ) . We benchmarked our system with a SELEX-derived TFAP2 binding site mutated at a single nucleotide position and a SNP ( rs509813 C/G ) in the promoter region of the muscarinic acetylcholine receptor M1 ( CHRM1 ) . This locus is associated with functional differences in gene expression and differential binding of an unidentified transcription factor [11] . While we were only able to visualize TFAP2 binding by immunostaining and not in a Coomassie stained gel ( Figure 2A ) , by mass spectrometry we measured robust and reproducible differential binding of TFAP2 to its SELEX derived binding site with a SILAC ratio of 10 ( Figure 2B ) . For rs509813 , we found SP1 as well as SP3 as differential interactors . Both are predicted to bind to the sequence containing the C-allele SNP , but not SP2 , which has a slightly different binding motif [12] . Furthermore , we detected binding of the transcription factor ZNF148 ( also known as ZBP89 ) to this region containing rs509813 . ZNF148 is a zinc finger protein which has not been predicted to interact with this site , but which has been reported to bind to SP1 binding sites in a mutually exclusive fashion [13] ( Figure 2C ) . Next , we applied the PWAS methodology to the complex , multi-SNP T1D susceptibility association of the IL2RA gene , encoding CD25 in the 10p14 region [1] , [2] ( Table 1 ) . There are three SNPs in this region which together can be used to tag four common disease-associated haplotypes [1] , representing a total of 12 SNPs . The tagging SNPs are rs12722495 ( A/G ) , rs11594656 ( A/T ) and rs2104286 ( A/G ) . The haplotype ( A , A , T ) was associated with increased susceptibility to disease , whereas the three haplotypes ( G , G , T ) ; ( A , A , A ) and ( A , G , T ) were all associated with lower risk of type 1 diabetes . Importantly , these four common haplotypes have also been associated with differences in surface expression of CD25 in T cells , implying that IL2RA is a causal gene for T1D in this region [1] . Specifically , individuals with one or two T1D-protective rs12722495 alleles show 27% higher mean CD25 levels on their CD4+ memory T cells compared to fully susceptible individuals or donors with protective rs11594656 or rs2104286 alleles [1] . This is thought to be related to haplotype-dependent transcriptional differences altering CD25 expression , in turn leading to modulation of autoreactivity against pancreatic beta cells . However , it is not known which of the SNPs in the 10p14 region have a direct functional effect , and the identity of the specific transcription factor ( s ) responsible for this differential binding is equally unclear . Therefore , the precise causal variant ( s ) in this region has not been determined . As preferential binding of transcription factors can occur on either allele , we performed two separate DNA pull-down experiments for each SNP . In the ‘forward’ experiment , the heavy SILAC labeled nuclear extract was incubated with one SNP allele and the light SILAC labeled extract with the other allele . In the ‘reverse’ experiment the SILAC label was switched . As visualized schematically in Figure 3 , this strategy allows us to create a two dimensional interaction plot for each pull-down in which interaction partners are grouped into two of four quadrants . Contaminants such as keratins are unlabeled and are sorted into the lower left quadrant because they have a low SILAC ratio in both pull-downs . We performed 48 SNP pull-downs with the 12 type 1 diabetes SNPs using extracts from the Jurkat T lymphocyte cell line , selected because type 1 diabetes is a T-cell mediated disease ( Table S2 ) . A very small number of proteins were clearly separated from the bulk of proteins that bound non-specifically to DNA or to the beads ( Figure S1 ) . These outliers were statistically significant in both pull-downs , with a combined forward and reverse pull-down p-value less than 10−7 . For three SNPs we did not detect significant differential protein binding , suggesting that they may be non-causative and instead represent markers for the causal variant ( s ) . In the eight SNPs of group 1 , we found RUNX1 ( also known as CBFA ) five-fold enriched at allele rs12722508*A ( Figure 4B ) . RUNX1 is a transcriptional regulator likely to be involved in hematopoesis [14] , which has already independently been linked to risk of autoimmune disease [15] . Notably , one of the two other significant binders to this SNP is CBFB , which is known to form a heterodimer with RUNX1 , underscoring the specificity of our screen [16] . The third differential interactor is SAFB1 , a less characterized transcription factor reported to be important in transcriptional regulation of HSP27 and ERα [17] . Additionally , LEF1 , a key transcription factor in the Wnt signaling pathway [18] involved in regulating T cell specific genes [19] , interacted with rs41295061*A ( Figure 4C ) in our SNP pull-down experiments . The transcriptional regulators CREB and TFAP4 differentially interacted with rs12722522*C ( Figure 4A ) . The SNP variant rs11597367*G of the three SNP containing group 2 bound ZNF148 and CGGBP1 specifically . We hypothesize that these genotype-dependent interactions are part of the molecular mechanism responsible for the association between these SNPs and expression of CD25 in naïve T cells and stimulated monocytes [1] . Three of the identified transcription factors have known DNA consensus motifs and we therefore investigated whether these motifs were present in the DNA fragment to which they bound . For CREB1 this was indeed the case: the sequence around rs12722522*C ( CGTCA ) when reverse complemented reconstituted the binding motif TGACG [20] , whereas the other allele was TGACA ( Figure 4A ) . Interestingly , for the other two cases , the region around the SNP did not reconstitute the deposited consensus sequence completely , but generated an additional mismatch ( Figure 4B , 4C ) . However , we note that for RUNX1 , the consensus sequence ( TGTGGBH ) for its murine homologue obtained in a recently published ChIP-seq experiment [21] matches the sequence around rs12722508*A ( ACCCACA ) when reverse complemented ( TGTGGGT ) . RUNX1 has previously been implicated in other autoimmune diseases [15] , which prompted us to further validate this transcription factor - SNP association . We reproduced the allele-specific binding of RUNX1 detected in our mass spectrometric assay by immunostaining ( Figure 5A ) and investigated the effects on transcription in a transactivation assay . To test whether changes in RUNX1 level would act differently on rs12722508 , we reduced the level of this transcription factor by RNA interference ( Figure 5B ) . Upon knock-down of RUNX1 , we observed an allele-specific activation of our reporter construct which was not observed when expression levels of control transcription factors were reduced ( Figure 5C ) . Since RUNX1 binds to both alleles , it upregulated both SNP variants; however , consistent with allele-specific differential binding , the upregulation was different between the alleles: rs12722508*A by 16±7 percent and rs12722508*T by 34±11 percent compared to mock ( P = 0 . 016 ) . These results link the allele-specific binding of RUNX1 detected in our mass spectrometry screen to functional differences in transcriptional activity .
Our results indicate that differential transcription factor binding to candidate causal SNPs can indicate which SNPs and which TFs might be involved in the causal mechanism ( s ) from gene-to-protein expression . In the case of SNP group 1 of the IL2RA type 1 diabetes locus [1] , three of the SNPs differentially bind common transcription factors ( rs12722508*A , rs12722522*C and rs41295061*A ) implying that there may be more than one SNP within SNP group 1 affecting IL2RA transcription . Extrapolating from the SILAC ratios , occupancy of the three SNPs rs12722508*A , rs12722522*C and rs41295061*A was altered between four and eight-fold for these transcription factors . Using reporter assays , we have shown that RUNX1 can mediate allele-specific expression via rs12722508 . The relatively small difference measured in our reporter gene assay , and the fact that several SNPs in the haplotype also show differential binding to common TFs , offer a plausible explanation for the observed expression difference of 30% in cell surface CD25 expression [1] , [2] . Our results indicate that several sites in a haplotype may contribute to differential transcription factor binding in a cumulative manner , as suggested here for three of eight SNPs of group 1 . Supporting such a scenario , genome-wide chromatin immunoprecipitation studies have shown that common transcription factors typically occupy thousands of sites in the genome [22] . We propose that multiple SNPs in a haplotype cooperatively modulate expression levels of nearby genes , contributing to individual traits , including risk of common diseases . Large scale differential transcription factor binding at SNPs has previously been reported for NFkB and PolII using allele-specific ChIP [23] . Our unbiased , SNP-centered , PWAS approach is orthogonal and complementary to the protein-centered ChIP-seq method and links the detected SNPs from genomics studies directly to the protein without a priori knowledge . In conclusion , PWAS is specific , reproducible and generic and only requires synthesis of 40 mers of DNA and batch labeling of cells without the need to obtain cell lines with matching haplotypes . The throughput is currently up to five SNP pairs per day and per mass spectrometer . PWAS may contribute evidence that a given variant is causal . It can thus help to select a subset of polymorphisms from a much larger candidate set that cannot be distinguished by genetic association mapping . Positive results from PWAS directly suggest further gene-phenotype associations that can be investigated to extend the molecular chain of events at least one step further than the GWAS-mapped SNPs themselves .
Hela S3 and Jurkat cells were SILAC-labeled in RPMI 1640 ( -Arg , -Lys ) medium containing 10% dialyzed fetal bovine serum ( Gibco ) supplemented with 84 µg/ml 13C615N4 L-arginine and 40 µg/ml 13C615N2 L-lysine ( Sigma Isotec or Cambridge Isotope Labs ) or the corresponding non-labeled amino acids , respectively . Nuclear extracts were prepared essentially as described [24] . 25 µg of corresponding pairs of oligonucleotides ( Table S1 ) were annealed and phosphorylated for 2 h at 37°C in the presence of polynucleotide kinase ( Fermentas ) in 1× T4 ligase buffer . 20 Units T4 ligase ( Fermentas ) was added to the reaction and incubated at RT overnight . Polymerisation was monitored by agarose gel electrophoresis of a small aliquot of the reaction mixture . After subsequent chloroform-phenol-extraction , the concatemerized oligonucleotides were desthiobiotinylated with d-desthiobiotin- ( N6- ( 6-Amino ) hexyl ) -dATP ( custom synthesis , Jena Biosciences ) using 30 units Klenow fragment ( Fermentas ) overnight at 37°C . Unreacted desthiobiotin nucleotides were removed by size exclusion using a G50 column ( GE Healthcare ) according to the manufacturer's protocol . The baits were stored at −20°C . DNA oligonucleotides were immobilized on 50 µl Dynabeads MyOne Streptavidin C1 ( Invitrogen ) and subsequently incubated with 200 µg of SILAC-labeled nuclear extract ( light and heavy separately ) in PBB buffer ( 150 mM NaCl , 50 mM Tris/HCl pH 8 . 0 , 10 mM MgCl2 , 0 . 5 percent NP-40 , Complete Protease Inhibitor-EDTA [Roche] ) for 2 hours at 4°C in a rotation wheel . After three times washing with PBB , bead fractions were pooled and bound DNA-protein complexes were eluted at RT with 200 µl PBB containing 16 mM biotin . The identical steps were automated on a TECAN EVO workstation equipped with a 4 channel LiHAN , robotic arm , cooled buffer storage reservoirs , a temperature controlled shaker and a magnetic separator . Incubations were performed in a 96 well plate ( Nunc ) . Instead of incubation on a rotation wheel , the automated TECAN shaker was operated at 8°C and 700 rpm . Proteins in the elution fraction were precipitated with ethanol and resolubilized in 20 µl 8 M urea for MS analysis . Samples were reduced in 0 . 5 mM DTT ( Sigma ) for 30 min , alkylated with 3 mM iodoacetamide ( Sigma ) for another 30 min and subsequently digested with trypsin ( Promega ) overnight at room temperature . The digested protein mixture was diluted in 50 mM ammonium bicarbonate buffer pH 8/0 . 5% TFA and loaded onto a self-made stage tip . MS analysis was performed essentially as previously described [25] . In short , peptides were eluted from stage tips and analyzed by nanoflow liquid chromatography on an EASY-nLC system from Proxeon Biosystems into a LTQ-Orbitrap XL ( Thermo Fisher Scientific ) . Peptides were separated on a C18-reversed phase column packed with Reprosil ( Dr . Maisch ) directly mounted on the electrospray ion source on an LTQ-Orbitrap XL . We used a 140 min gradient from 2% to 60% acetonitrile in 0 . 5% acetic acid at a flow of 200 nl/min . The LTQ-Orbitrap XL was operated with a Top5 MS/MS spectra acquisition method in the linear ion trap per MS full scan in the orbitrap . The raw files were processed with MaxQuant [26] ( version 1 . 0 . 12 . 27 ) and searched with the Mascot search engine ( version 2 . 2 . 4 . 1 , Matrix Science ) against a IPI human v3 . 37 protein database concatenated with a decoy of reversed sequences . Carbamidomethylation was set as fixed modification while methionine oxidation and protein N-acetylation were included as variable modifications . The search was performed with an initial mass tolerance of 7 ppm for the precursor ion and 0 . 5 Da for the MS/MS spectra . Search results were processed with MaxQuant filtered with a false discovery rate of 0 . 01 . Prior to statistical analysis , known contaminants and reverse hits were removed . Proteins identified with at least 1 unique peptide and minimum 2 quantitation events in a single pull-down were considered for analysis and plotted in R ( prerelease version 2 . 8 . 0 ) . Transcription factors identified for differential SNP binding were checked for deposited recognition sequences in the JASPAR database ( www . jaspar . com ) . For western blotting , proteins were solubilized in LDS sample buffer ( Invitrogen ) , boiled for 5 min at 85°C and fractionated on a 4–12 percent NOVEX gradient gel using MOPS buffer ( Invitrogen ) . Proteins were transferred to a Protran 85 membrane ( Whatman ) in a blotting chamber ( Biorad ) at 300 mA for 1 h . The membrane was blocked with PBST ( 0 . 1% ) +4 percent low fat milk ( Roth ) for 15 min prior to incubation with the primary antibody for 1 h at room temperature . The following antibodies were diluted in PBST ( 0 . 1% ) with 4 percent low fat milk: SP1 [1∶1000] , TFAP2 [1∶2000] , RUNX1 [1∶1000] and LEF1 [1∶1000] ( all Abcam ) . Membrane was washed with PBST ( 0 . 1% ) three times prior to incubation with either HRP-anti-mouse or HRP-anti-rabbit antibody ( both Amersham ) for 1 h at room temperature . For detection ECL Western blotting detection reagent ( Amersham ) was used according to manufacturer's instruction . Chemiluminescence screens ( GE Healthcare ) were used to visualize the band patterns . Endoribonuclease-prepared short interfering RNAs ( esiRNAs ) were produced as previously described [27] with the following primers: Rluc ( for: GGATAACTGGTCCGCAGTGGT , rev: CCCATTCATCCCATGATTCAA ) ; TFAP4 ( for: GTGCCCTCTTTGCAACATTT , rev: TTCTCGTCCTCCCAGATGTC ) ; RUNX1 ( for: GGCTGGCAATGATGAAAACT , rev: GATGGTTGGATCTGCCTTGT ) ; CREB1 ( for: GGAGTGCCAAGGATTGAAGA , rev: CCTCTCTCTTTCGTGCTGCT ) ; CBFB ( for: TTTGAAGGCTCCCATGATTC , rev: CCATGGCAGTTTGTGATGTC ) . The heat shock promoter was amplified ( Hsp68_for: GAGAAAGCTTCAGGAACATCCAAACTGAGCA , Hsp68_rev: GAGAAAGCTTCGCTTGTCTCTGGATGGAAC ) from genomic DNA prepared from C2C12 cells . The promoter was cloned into pGL3-basic ( Promega ) in front of the firefly luciferase gene at the HindIII site . A gateway cassette was inserted 5′ of the promoter to facilitate insertion of different DNA sequences , generating the pGL3/GW/mHsp68prom vector . A triple repeat of the sequence surrounding rs12722508 ( AACCCACCCAC[A/T]GAAACTATCAGAG ) was cloned into pCR8 and transferred into pGL3/GW/mHsp69prom using LR recombination . 100 , 000 cells HeLa Kyoto were reverse transfected with 150 ng esiRNA and 1 µl oligofectamine ( Invitrogen ) in complete medium in a 24 well plate . After 16 hours , 4 ng Rhenilla luciferase vector phRL-TK ( Promega ) were co-transfected with 200 ng firefly reporter ( pGL3/GW3/mHsp69prom ) containing either SNP variant with Optimem medium ( Gibco ) . Medium was exchanged against complete medium 4 hours past transfection . Cells were harvest the next day using passive lysis buffer ( Promega ) . Independent biological triplicates were measured on the GloMax luminometer ( Promega ) using the Dual Luciferase Assay kit ( Promega ) according to the manufacturer's protocol . For representation , the mean and the standard deviation were calculated . Transfection efficiency was monitored by parallel transfection of RLuc esiRNA targeting rhenilla luciferase . Cells form one well ( 24 well plate ) transfected with esiRNA and reporter assay constructs were trypsinized , washed in PBS and RNA extracted using a spin filter kit ( UBS ) . Around 1 µg of pure RNA were reverse-transcribed with polyT primers using the Fast cDNA kit ( Fermentas ) . The following primers were used for the amplification: TFAP4 ( for: GCAGACAGCCGAGTACATCTT , rev: CCTATGCCTTCGTCCTTGTCC ) , RUNX1 ( for: GATGGCACTCTGGTCACTGTGA , rev: CTTCATGGCTGCGGTAGCAT ) , CBFB ( for: AGAAGCAAGTTCGAGAACGAG , rev: GAAGCCCGTGTACTTAATCTCAC ) , CREB ( for: CACCTGCCATCACCACTGTAA , rev: GCTGCATTGGTCATGGTTAATGT ) , GAPDH ( for: TGCACCACCAACTGCTTAGC , rev: GGCATGGACTGTGGTCATGAG ) . Three independent replicates using the IQ SybrGreen supermix ( Biorad ) were measured on a CFX96 qRT-PCR machine ( Biorad ) . For assessment of knock-down the ΔΔCt method was used .
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Genome-wide association studies ( GWAS ) are a powerful approach to identifying genes contributing to risk of disease . However , individual mapped single nucleotide polymorphisms ( SNPs ) may not map close to a gene , and it can be difficult to distinguish marker SNPs from causal SNPs . Furthermore , the molecular mechanism responsible for disease association is usually not clear . Here we develop a method termed “proteome-wide analysis of SNPs” ( PWAS ) that identifies differentially binding transcription factors ( TFs ) and thereby helps to unravel the molecular mechanisms by which the SNPs may exert their effect on gene regulation . We use quantitative interaction proteomics to identify proteins with allele-specific binding . Applied to fine-mapped SNPs conferring risk in type 1 diabetes , PWAS revealed preferential binding of common transcription factors to certain disease-associated SNPs , suggesting they could be causal . In general , a proportion of causal SNPs are likely to function by mimicking binding motifs for transcription factors , increasing their occupancy and modulating gene expression . In addition , PWAS is streamlined and can be used as an informative follow-up approach to GWAS results .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"biochemistry",
"functional",
"genomics",
"biology",
"genomics",
"proteomics",
"genetics",
"and",
"genomics"
] |
2012
|
Proteome-Wide Analysis of Disease-Associated SNPs That Show Allele-Specific Transcription Factor Binding
|
The complement system consists of more than 40 proteins that participate in the inflammatory response and in pathogen killing . Complement inhibitors are necessary to avoid the excessive consumption and activation of this system on host cells . Leptospirosis is a worldwide zoonosis caused by spirochetes from the genus Leptospira . Pathogenic leptospires are able to escape from complement activation by binding to host complement inhibitors Factor H [FH] and C4b-binding protein ( C4BP ) while non-pathogenic leptospires are rapidly killed in the presence of fresh serum . In this study , we demonstrate that complement control protein domains ( CCP ) 7 and 8 of C4BP α-chain interact with the outer membrane proteins LcpA , LigA and LigB from the pathogenic leptospire L . interrogans . The interaction between C4BP and LcpA , LigA and LigB is sensitive to ionic strength and inhibited by heparin . We fine mapped the LigA and LigB domains involved in its binding to C4BP and heparin and found that both interactions are mediated through the bacterial immunoglobulin-like ( Big ) domains 7 and 8 ( LigA7-8 and LigB7-8 ) of both LigA and LigB and also through LigB9-10 . Therefore , C4BP and heparin may share the same binding sites on Lig proteins .
Leptospirosis is a worldwide zoonosis caused by spirochetes from the genus Leptospira [1] . Rodents are the main reservoir of Leptospira , which can be excreted in animal urine [2] . The disease is transmitted by contact of abraded skin or mucosal membranes with water contaminated with infected urine and it presents a higher incidence in developing countries , which lack proper sanitation conditions . In developed countries , leptospirosis is more frequently associated with agriculture and aquatic sports . According to the World Health Organization [WHO] , more than 500 , 000 cases of leptospirosis are diagnosed per year worldwide and the mortality rates reach 10% in some areas . Since 80–90% of the patients initially present nonspecific symptoms , such as headache , fever and joints pain that may disappear without any specific treatment , these epidemiological data may be underestimated [3–4] . So far , 13 pathogenic and 6 saprophytic species with more than 320 serovars ( 260 pathogenic and 60 saprophytic ) of Leptospira species have been described [5] . Recent studies on the differences between pathogenic and saprophytic Leptospira have contributed to our knowledge concerning the mechanisms that allow survival of pathogenic strains inside the host . In 2004 , Koizume and Watanabe described two surface proteins expressed only in pathogenic Leptospira: Leptospiral immunoglobulin-like A ( LigA ) and B ( LigB ) [6–8] . During the last decade several groups have been studying the functions of Lig proteins and their contributions to Leptospira infection . A well-established role for LigA and LigB is their capacity to interact with multiple extracellular matrix components such as fibrinogen , fibronectin [9] collagen [10] laminin and elastin [11] . Saprophytic Leptospira expressing recombinant LigA or LigB proteins present stronger adhesion to eukaryotic cells and to fibronectin in vitro [12] . A fragment of the LigA protein has been shown to be a promising vaccine candidate , conferring high-levels of protection in hamster models of leptospirosis [13–14] . Lig proteins have also been shown to contribute to pathogenic Leptospira immune evasion by binding to the complement system inhibitors Factor H ( FH ) , FH-like 1 ( FHL-1 ) , FH-related 1 ( FHR-1 ) and C4b-binding protein ( C4BP ) [15] . In addition , LcpA , another surface protein present exclusively in pathogenic Leptospira , binds to C4BP [16] , FH and vitronectin [17] . The acquisition of such inhibitors on the bacterial surface potentially enables pathogenic Leptospira to down-regulate all pathways of this system . FH is a 150 kDa protein composed of 20 control complement protein ( CCP ) domains ( also known as short consensus repeat ( SCRs ) [18–19] . CCPs 1–3 interact with C3b which is important for FH’s role as a cofactor in Factor I ( FI ) -mediated cleavage of C3b [19] . FH cofactor activity is maintained when bound to Lig proteins [15] . FH also inhibits the interaction of Factor B with C3b , accelerating decay of the C3 convertase of the alternative pathway [20] . FH binds to LcpA mainly by CCP 20 [17] and to Lig proteins through CCPs 5 and 20 [15] . C4BP is a 570-kDa glycoprotein and relatively abundant in plasma ( 200 μg/ml–500 μg/ml ) [21] . The C4BP molecule is comprised of two different polypeptide chains: C4BP α chain ( 75 kDa ) and C4BP β chain ( 45 kDa ) . In serum , three C4BP isoforms can be observed which differ in the stoichiometries of α and β chains: α7β1 ( most common ) , α6β1 and α7β0 [22] . C4BP α chain contains eight CCPs and C4BP β chain contains three CCPs ( Fig 1 ) . C4BP inhibits the classical and the lectin pathways acting as a cofactor for the cleavage of C4b by FI . It also prevents binding of C2a to C4b and accelerates the decay of the C3 convertase ( C4bC2a ) of both pathways [23–25] . Binding sites for several ligands of C4BP have been localized using C4BP mutants . The alpha-chains CCP2 and CCP3 are crucial for the interaction with C4b [26–27] while binding to heparin requires CCPs 1–3 of the alpha chain [28] . The first three CCP domains of the alpha chain are also involved in interactions with several bacterial pathogens . C4BP also interacts with protein S through its beta-chain CCP1 [29–31] . In a previous study , we showed that LigA and LigB interact with C4BP in a dose-dependent manner and that bound C4BP remains functionally active , mediating degradation of C4b by FI [15] . In this study , we focused more closely on the interaction of Lig proteins with C4BP . Using a panel of C4BP mutants , we mapped the CCPs involved in the interaction with whole Leptospira and specific LigA and LigB domains . We show that ionic forces play a role in the binding of C4BP to Lig proteins and that the interaction is inhibited by heparin , a known C4BP ligand .
All the experiments involving laboratory animals were evaluated by the “Ethics Committee for Animal Use” from Institute of Biomedical Sciences—University of São Paulo ( our Institutional Animal Care and Use Committee ) and approved under the protocol number06/10/CEUA/ICB and 99/2/CEUA/ICB . The procedures are according to the Brazilian National Law number 11794 from 10/08/2008 , which regulates all research activities involving animal use in the country . L . interrogans serovar Kennewicki strain Fromm was cultured in EMJH medium [Difco] supplemented with 10% of bovine serum albumin ( BSA ) during 7 days at 29°C under aerobic conditions . The virulence of this strain was confirmed in vivo upon infection in hamsters at the Laboratory of Bacterial Zoonosis of the Faculty of Veterinary Medicine and Zootechny , University of São Paulo . Human purified C4BP ( α7β1 ) was purchased from Complement Technology ( Texas , USA ) . Recombinant wild type C4BP ( rC4BP ) and C4BP mutant α chains lacking single CCP domains α6β0 ( Fig 1 ) were expressed in eukaryotic cells and purified by affinity chromatography as previously described [26 , 32] . To exclude the possibility that polyclonal rabbit anti-human C4BP ( Calbiochem ) used in this study would interact preferentially with the C-terminal rather than to the N-terminal region of C4BP , which could potentially interfere with the detection of C4BP lacking the CCPs localized in this region , an ELISA was performed to guarantee that the antibody could recognize equally all C4BP mutants . All mutants were specifically recognized by anti-human C4BP . LigA is composed of 13 bacterial immunoglobulin-like [Big] domain repeats while LigB consists of only 12 Big domains . The first six and a half domains of LigA and LigB are identical ( residues 26–630 ) . This recombinant fragment was expressed and named LigBN . Fragments corresponding to the C-terminal half of the 7th Big domain until 13th Big domains of LigA ( residues between 631–1225 ) and LigB ( residues between 631–1156 ) were named LigAC and LigBC . Cloning , expression , and purification of LigAC , LigBC , LcpA and LIC10301 ( negative control ) were described previously [15 , 16] . To pinpoint the C4BP binding sites on Lig proteins , a series of tandem Ig-like domains ( also known as Big domains ) of Lig proteins were expressed and purified as previously described [11]: LigA7-8 , LigA8-9 , LigA9-10 , LigA10-11 , LigA11-12 , LigA12-13 , LigA8-13 ( or LigAC ) ( numbers correspond to Big domains of LigAC ) , or LigB7-8 , LigB8-9 , LigB9-10 , LigB10-11 , LigB11-12 , LigB7-12 ( or LigBC ) ( numbers correspond to Big domains of LigBC ) . Polyclonal mouse anti-LigA and anti-LigB antibodies were obtained in our laboratory after immunizing mice with 10 μg/each of purified recombinant proteins , using Al ( OH ) 3 as adjuvant . Two booster injections of the same protein preparation were given at 2-week intervals . One week after each immunization , animals were bled from the retro-orbital plexus and sera pooled . The titers of anti-LigA and anti-LigB were determined by enzyme-linked immunosorbent assay ( ELISA ) . Secondary antibodies conjugated with peroxidase or FITC were purchased from KPL ( Maryland , USA ) and Abcam ( Cambridge , UK ) , respectively . To map the regions of C4BP CCPα-chain which interact with LigAC , LigBC and LcpA , microtiter plates ( Costar 3590 ) were coated with 1 μg of LigAC , LigBC , LcpA or BSA ( negative control ) overnight at 4°C . The wells were washed with PBS ( 137 mM NaCl , 2 . 7 mM KCl , 10 mM Na2HPO4 , 1 . 8 mM KH2PO4 ) , blocked with PBS containing 3% BSA ( PBS-BSA ) for 2 h at 37°C , and incubated with 1 μg of each recombinant C4BP fragment ( diluted in PBS ) for 60 min at 37°C . After three washes with PBS containing 0 . 05% Tween 20 ( PBS-T ) , C4BP bound to L . interrogans recombinant proteins was detected with rabbit polyclonal anti-human C4BP ( 1:2000 in PBS-T containing 1% BSA ) , followed by peroxidase-conjugated anti-rabbit IgG ( 1:5000 in PBS-T 1% BSA ) . Substrate reaction was performed with o-phenylenediamine dihydrochloride ( Pierce ) , and absorbance was measured at 492 nm . The interaction between L . interrogans recombinant proteins with recombinant wild-type C4BP ( rC4BP WT ) was considered 100% binding . To map the CCP alpha-chains of C4BP that interact with intact L . interrogans , bacteria were harvested at 10 . 000 x g for 10 min , washed 2 times with PBS and suspended in 1 ml PBS . Suspensions were adjusted to 1x108 leptospires using a Petroff-Hausser chamber under dark-field microscopy ( Nikon Eclipse 50 ) . Suspensions of L . interrogans were incubated with 2 μg of each C4BP mutant or with PBS for 2 h at room temperature . After washing , bacteria were incubated with polyclonal rabbit anti-human C4BP ( 1:150 in PBS/1% BSA; final volume: 40 μl ) , followed by FITC-conjugated anti-rabbit IgG ( AbCam; 1:200 in PBS/1% BSA ) . Geometric mean fluorescence intensity ( GMFI ) was measured and the binding of L . interrogans to rC4BP WT was set up as 100% . The auto fluorescence of Leptospira was discounted before plotting the data . It is worth to mention that Matsunaga and colleagues ( 2005 ) showed that the expression of LigA and LigB proteins by L . interrogans is determined by environmental signals [33] . Thus , to perform this experiment we used low-passage forms of L . interrogans obtained from hamster . Before we used we confirmed the expression of LigA and LigB proteins on L . interrogans by Western blot and flow cytometry . Microtiter plates were coated with 1 μg of each LigA or LigB fragment overnight at 4°C in PBS ( 130 mM NaCl , 7 mM Na2HPO4 , 3 mM NaH2PO4 ) . After blocking with 3% BSA , C4BP ( 1μM—0 . 0156 μM , 2 fold serial dilution ) was then added to the plates for 1 h at 37°C . Between each step , plates were washed with PBS-T three times . Subsequently , mouse monoclonal anti-C4BP antibodies ( 1:2000 ) ( EMD Millipore ) recognizing C4BP α- and β-chains were used as primary antibodies . Horseradish peroxidase ( HRP ) -conjugated rabbit anti-mouse IgG antibody ( 1:2000 ) ( Invitrogen ) was used as secondary antibody . After washing three times with PBS-T , 100 μl of 0 . 2 mg/ml 3 , 3’ , 5 , 5’-tetramethylbenzidine ( TMB ) substrate ( Kirkegaard & Perry Laboratories ) was added to each well . Finally , after a 10 min-incubation the microtiter plates were read at 630 nm using an ELISA plate reader ( Biotek EL-312 ) . Each value represents the mean ± SE of three independent experiments , each performed in triplicate . To confirm the importance of the C4BP C-terminal region on the interaction with LigAC and LigBC proteins , we performed a competition assay with heparin , which interacts with C4BP CCP 1 , CCP 2 and CCP 3 [28] . ELISA was performed as described above . Briefly , plates were coated with LigAC or LigBC proteins . After blocking and washing 1 μg of C4BP was mixed with different amounts of heparin and added to the reaction . C4BP bound to LigAC or LigBC proteins was detected using anti-C4BP . To verify if Lig proteins interact directly with heparin , microtiter plate wells were coated with 1 μg of heparin overnight . After washing and blocking , 1μg of LigAC or LigBC was added to the reaction . After three washes , LigAC and LigBC bound to heparin were detected using mouse anti-LigA or mouse anti-LigB , respectively , ( 1:1000 in PBS-T containing 1% BSA ) followed by peroxidase-conjugated anti-mouse IgG ( 1:5000 in PBS-T 1% BSA ) . To identify which Big domains of LigA and LigB proteins interact with heparin , heparin-BSA conjugates were prepared as previously described [34] . Free BSA was separated from the heparin-BSA by Superdex-75 size exclusion column ( GE Healthcare ) . Plates were coated with 1 μg of heparin-BSA or BSA ( negative control ) at 4°C overnight . After blocking with PBS-BSA for 2h , different concentrations of Lig protein constructs were applied to heparin-coated wells for 1h at 37°C . Between each step , the wells were washed with PBS-T three times . To evaluate the heparin binding ability of each Lig construct , mouse monoclonal antibodies ( 1:1000 ) against Lig proteins were used as primary antibodies and HRP-conjugated rabbit anti-mouse IgG antibody ( 1:2000 ) ( Invitrogen ) was used as secondary antibody with TMB as substrate . In order to examine if the C4BP binding sites on Lig proteins are located near the heparin binding regions , a competitive ELISA was performed . Firstly , Lig protein constructs , which can potentially interact with C4BP , were immobilized overnight on microtiter plates at 4°C . After blocking with PBS-BSA for 2h , 1 μg of C4BP mixed with different concentrations of heparin ( 0–10 mg/ml ) were added to the plates . Between each step , the microtiter wells were washed with 0 . 05% PBS-T for three times . Subsequently , mouse monoclonal anti-C4BP α/β chains antibodies ( 1:2000 ) ( EMD Millipore ) and HRP-conjugated rabbit anti-mouse IgG antibodies ( 1:2000 ) ( Invitrogen ) were used respectively as primary and secondary antibodies to detect the binding levels of C4BP to different Lig protein fragments . C4BP binding to each Lig protein fragment in the absence of heparin was set as 100% . To estimate binding affinities for the interaction between LigAC or LigBC with C4BP or with heparin , increasing concentrations of Lig proteins were incubated with a fixed amount of C4BP ( 1μg ) or heparin ( 1μg ) . Using GraphPad Prism 5 . 0 ( GraphPad Software , Inc . ) , the dissociation constant ( Kd ) was estimated by nonlinear regression , using the equation Y = Bmax*X/ ( Kd + X ) . To test if the interaction of C4BP with LigA , LigB and LcpA is dependent on ionic strength an ELISA assays [as described above] were performed in the presence of increasing amounts NaCl [0 to 600 mM] diluted in 10 mM Na2HPO4 and 1 . 8 mM KH2PO4 . GraphPad Prism 5 . 0 ( GraphPad Software , Inc . ) was used for statistical analyses and ANOVA were used to analyze the data .
We used recombinant fragments of the entire C-terminus regions of LigA and LigB to map their binding to specific C4BP CCPs . LcpA , a confirmed leptospiral ligand for C4BP [16] , was included in this study as a positive control . Using the panel of C4BP mutants lacking single CCPs in the α-chain ( Fig 1A and 1B ) , we observed that interaction with LigAC ( Fig 2A ) , LigBC ( Fig 2B ) and LcpA ( Fig 2C ) was significantly reduced in the absence of CCP7 and CCP8 . In addition , we observed that CCP4 of C4BP is also important for the interaction with LigAC ( Fig 2A ) . The C4BP ΔCCP4 mutant also showed a tendency towards reduced binding to LigBC and LcpA , though this reduction was not statistically significant ( Fig 2B and 2C ) . C4BP CCP4 , CCP7 and CCP8 domains were also found to be important for the binding of this regulatory protein directly to whole L . interrogans cells . Interestingly , the binding of L . interrogans to the C4BP mutant lacking the CCP1 domain of the α-chain was 3 times greater than that observed for C4BP WT ( Fig 2D ) . To investigate the C4BP binding sites on Lig proteins , wild type C4BP was added to microtiter plates coated with recombinant LigA and LigB truncations . Interaction of the variable C-terminus halves of both LigA ( LigAC ) and LigB ( LigBC ) with C4BP is significantly greater than that observed for the N-terminus region ( LigBN , which is identical in both proteins ) ( Fig 3A ) . To further pinpoint the minimal C4BP binding sites on the two Lig proteins , a series of two-domain tandem Ig-like repeats ( Big domains ) across the variable C-terminus region of LigA and LigB were generated . The results indicate that all the LigA and LigB tandem repeats bind to C4BP but with different affinities . The major binding sites are located in fragments LigA7-8 , LigA9-10 , LigA10-11 ( Fig 3B ) and in fragments LigB7-8 , LigB9-10 and LigB11-12 ( Fig 3C ) . A minimal two-domain Ig-like repeat seems to be critical for the C4BP/Lig interaction since binding is significantly reduced when constructs containing only a single-Ig-like domain were used . We also observed that LigAC and LigBC affinities for C4BP are , respectively , Kd = 88 . 8 +/- 7 . 8 nM and Kd = 51 . 6 = /- 5 . 9 nM , ( Fig 3D ) . From these results , we conclude that the variable regions of Lig proteins are the most important binding sites for C4BP , that the minimal binding motifs seem to be made up of pairs of Big domains and that the Big domains pairs displayed the strongest affinity for C4BP are domains 7–8 , 9–10 , 10–11 in LigA and domains7-8 , 9–10 and 11–12 in LigB . The results in Fig 2 suggest the C4BP N-terminus domains CCP1 , CCP2 and CCP3 do not contribute to binding with LigA and LigB . To further test this hypothesis , we investigated this interaction in the presence of the anionic polysaccharide heparin . Heparin is a component of the extracellular matrix and it is known to interact with C4BP CCP1 , CCP2 and CCP3 of the α-chain [28] . Recombinant L . interrogans proteins were incubated with recombinant C4BP WT in the presence of different concentrations of heparin . Surprisingly , we observed that heparin inhibited the interaction of C4BP with LigBC even at concentrations as low as 10-3 mg/ml while the interaction of C4BP with LigAC was inhibited with 0 . 2 mg/ml of heparin ( Fig 4A ) . We then investigated if the above phenomenon could be a result of heparin binding directly to LigA and LigB . According to Ching and colleagues [35] , recombinant LigBC binds to heparin , and this interaction may help L . interrogans to invade host cells . In the present study , we confirmed these results and additionally observed that LigAC interacts with immobilized heparin as well ( Fig 4B ) . In order to verify if heparin and C4BP would compete for the same binding sites on LigA and LigB , we performed heparin binding experiments using recombinant fragments corresponding to pairs of Big domains from the C-terminus halves of LigA and LigB . Fig 5A shows that Big domain pairs 7–8 of both LigA and LigB are the most important regions for the interaction with heparin . To test whether heparin and C4BP share binding sites on LigA and LigB proteins , a competition assay was performed using different LigA and LigB fragments . In the presence of the highest concentration of heparin used ( 10 mg/ml ) , the binding of C4BP to LigA7-8 and LigB7-8 was reduced by 50% and 45% , respectively . The interactions of C4BP with LigA10-11 and LigB9-10 were decreased by 35% and 27% ( respectively ) under the same conditions ( Fig 5B ) . We also observed that LigAC and LigBC seem to have similar affinities for heparin ( Kd = 0 . 89 +/-0 . 1 μM and Kd = 0 . 91 +/- 0 . 1 μM , respectively ) ( Fig 5C ) . Together , the above results suggest that there is significant overlap of the heparin and C4BP binding sites on LigA and LigB and that these shared binding regions include Big domains 7 and 8 from both leptospiral proteins . To further characterize the nature of the interaction of C4BP with LigA , LigB and LcpA , binding assays were performed using different concentrations of NaCl from 50 mM to 600 mM . As shown in Fig 6 , the amount of LigAC and LigBC bound to C4BP is decreased by 45% at 150 mM NaCl while the amount of LcpA bound to C4BP decreased only 30% under these conditions . These results suggest that the interaction between C4BP and recombinant LcpA , LigAC and LigBC have a significant ionic component .
The complement system plays an important role in the innate and acquired immune responses , contributing to pathogen elimination . In 1955 , Lawrence published one of the first studies about the role of complement activation in leptospiral infection , concluding that complement does not contribute to leptospiral killing [36] . However , in the 1960s , Johnsons published a series of three studies showing that complement plays an important role in leptospiral infection and demonstrated that saprophytic leptospires are more susceptible to lysis mediated by the complement system than pathogenic leptospires [37–39] . Since then , several groups worldwide have been interested in understanding the immune evasion mechanisms acquired by pathogenic strains of leptospires . In 2005 , Meri and colleagues [40] showed that FH maintains its regulatory activity when bound to pathogenic leptospires , but does not bind to saprophytic leptospires . A variety of pathogens bind to complement inhibitors to evade complement system activation: relapsing fever spirochete Borrelia recurrentis binds to FH [41] , C4BP [41] and C1 inhibitor [42] and Neisseria gonorrhoeae [43–44] , Streptococcus pyogenes [45–46] , Haemophilus influenza [47–48] , Moraxella catarrhalis [49–50] , Bordetella pertussis [51–52] , Escherichia coli [53–54] and Pasteurella pneumotropica [55] interact with C4BP and FH . Not only bacteria , but also fungi have been shown to interact with C4BP , for example , Candida albicans[56] and Aspergillus[57] . In 2009 , our group was the first to describe the interaction of C4BP with pathogenic leptospires and to show that C4BP bound to the leptospire surface is able to interact with and cleave C4b , regulating complement system activation [58] . LigA and LigB are multi-functional molecules closely associated with host infection and capable of inducing specific antibody responses [59–61] . We have previously identified three L . interrogans surface proteins that bind to C4BP: LcpA , LigA and LigB [15 , 16] . In the present study , we mapped the binding sites of these proteins on the C4BP molecule using C4BP mutants lacking single CCPs of the α-chain . We observed that CCP4 , CCP7 and CCP8 domains of C4BP α-chains are important for the interaction with LigAC and with intact L . interrogans whereas only CCP7 and CCP8 interact with purified LigBC and LcpA proteins , as summarized in Fig 7 . Since the C4b binding site on C4BP is located on α-chain domains CCP1 , CCP2 and CCP3 [28] , C4BP bound to Leptospira probably retains cofactor activity . Such a strategy may help these spirochetes to regulate and inhibit the activation of the classical and lectin pathways inside the host . The same C4BP domains interact with Streptococcus pnemoniae [62] , Salmonella [63] , Yersinia [64] , and Haemophilus influenzae [65] . Surprisingly , the interaction of C4BP with LigBC and with the leptospiral surface was increased in the presence of C4BP lacking CCP1 α-chain ( Fig 2 ) . One possible explanation is that C4BP lacking CCP1 may more easily adopt specific conformational states that yield tighter interactions with the leptospiral surface or LigBC . However , additional studies on the C4BP structure will be necessary to address this hypothesis . It is worth to mention that unexpected results have been also observed with Porphyromonas gingivalis , which display a stronger interaction with C4BP lacking CCP3 or CCP5 [66] . Next , we pinpointed the regions of LigA and LigB proteins capable of binding to C4BP . The conserved N-terminus region of LigA and LigB proteins does not interact significantly with C4BP compared to the C-terminus of LigA ( LigAC ) and LigB ( LigBC ) ( Fig 3 ) . Using a series of tandem Big domain constructs of Lig proteins , we observed that LigA7-8 , LigA9-10 and LigA10-11 and LigB7-8 , LigB9-10 and LigB11-12 are the main binding sites for C4BP on LigAC and LigBC , respectively . Several studies have used LigA and LigB constructs in order to map the regions of interaction with extracellular matrix components . Elastin and human tropoelastin bind to LigB7-8 , LigB9 and LigB12 [11] while fibronectin binds to LigA7-8 , LigA10 , LigA11 , LigA12 , LigA13 , LigB7-8 , LigB9 and LigB12 domains [9 , 67] . So , it seems that one ligand may have multiple binding sites on LigA and LigB proteins . Unlike elastin , human tropoelastin and fibronectin , C4BP is not able to bind to recombinant fragments containing a single Big domain derived from LigA or LigB proteins . Although the binding sites of Lig proteins and heparin on the C4BP molecule do not overlap , binding of LigAC and LigBC to C4BP was inhibited by this highly sulphated glycosaminoglycan ( Fig 4A ) and we observed that both leptospiral proteins directly interact with heparin ( Fig 4B ) . Using a series of tandem Big domain constructs of Lig proteins , we also observed that heparin binds preferentially to LigA7-8 and LigB7-8 ( Fig 5A ) and that heparin can inhibit the binding of LigA7-8 , LigA10-11 , LigB 7–8 and LigB9-10 fragments to C4BP ( Fig 5B ) . Whether this inhibition is due to heparin binding to Lig proteins or to heparin binding to C4BP is not yet clear . However , since heparin´s interaction with C4BP may be stronger than its interaction with LigA and LigB we cannot rule out the possibility that heparin bound to C4BP CCPs 1–3 could interfere with or modify the binding of LigAC and LigBC to C4BP CCP4 , 7 and 8 by way of an allosteric mechanism . To date we still lack an efficient vaccine that protects people from different continents against leptospirosis . Lig proteins , notably LigA [13] , have been shown to be the best vaccine candidates described so far . Unfortunately , they are not able to confer sterilizing immunity . A combination of different antigens , including Lig proteins , may eventually constitute an ideal vaccine against human leptospirosis . Understanding which regions of LigA and LigB are involved in the interactions with host´s complement regulators , such as C4BP , may contribute to the development of a human vaccine against the disease .
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Leptospirosis is a neglected infectious disease of public health significance . It is one of the most important zoonosis worldwide , affecting at least 500 , 000 people each year of which approximately 10–20% of cases are fatal . Most of the cases occur in tropical developing countries which lack proper sanitation facilities . The complement system plays an important role to control this infection and pathogenic leptospires developed several strategies to escape from this system , for example , binding to complement regulatory proteins such as C4b binding protein ( C4BP ) . In this work , we fine mapped the C4BP domains important for this evasion . We also mapped C4BP interactions with LigA and LigB , two surface proteins present in all pathogenic leptospires . The available leptospirosis vaccines are based on region-specific serovars and are not very protective . Consequently , the search of better vaccines is necessary and Lig A and Lig B represent promising vaccine candidates to immunize people worldwide . We believe that deeply understanding the interactions between C4BP with LigA and LigB could open new perspectives for immunotherapy and development of new drugs to be used during the first days of infection , when the production of specific antibodies is still low and the complement system plays an important role to control this infection .
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[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[] |
2015
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Fine Mapping of the Interaction between C4b-Binding Protein and Outer Membrane Proteins LigA and LigB of Pathogenic Leptospira interrogans
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PB1-F2 is a 90 amino acid protein that is expressed from the +1 open reading frame in the PB1 gene of some influenza A viruses and has been shown to contribute to viral pathogenicity . Notably , a serine at position 66 ( 66S ) in PB1-F2 is known to increase virulence compared to an isogenic virus with an asparagine ( 66N ) at this position . Recently , we found that an influenza virus expressing PB1-F2 N66S suppresses interferon ( IFN ) -stimulated genes in mice . To characterize this phenomenon , we employed several in vitro assays . Overexpression of the A/Puerto Rico/8/1934 ( PR8 ) PB1-F2 protein in 293T cells decreased RIG-I mediated activation of an IFN-β reporter and secretion of IFN as determined by bioassay . Of note , the PB1-F2 N66S protein showed enhanced IFN antagonism activity compared to PB1-F2 wildtype . Similar observations were found in the context of viral infection with a PR8 PB1-F2 N66S virus . To understand the relationship between NS1 , a previously described influenza virus protein involved in suppression of IFN synthesis , and PB1-F2 , we investigated the induction of IFN when NS1 and PB1-F2 were co-expressed in an in vitro transfection system . In this assay we found that PB1-F2 N66S further reduced IFN induction in the presence of NS1 . By inducing the IFN-β reporter at different levels in the signaling cascade , we found that PB1-F2 inhibited IFN production at the level of the mitochondrial antiviral signaling protein ( MAVS ) . Furthermore , immunofluorescence studies revealed that PB1-F2 co-localizes with MAVS . In summary , we have characterized the anti-interferon function of PB1-F2 and we suggest that this activity contributes to the enhanced pathogenicity seen with PB1-F2 N66S- expressing influenza viruses .
Influenza viruses cause annual epidemics and occasional pandemics which may result in up to 50 million excess deaths as seen during the outbreak in 1918 [1] . The virus strains causing the influenza pandemics that occurred in the years of 1918 , 1957 and 1968 , all have been found to express the virulence factor PB1-F2 . The PB1-F2 protein is encoded by the +1 alternate open reading frame ( ORF ) in the PB1 gene of some influenza A viruses , giving rise to a 90 amino acid protein that was initially found to possess pro-apoptotic activity [2] . In animal studies , PB1-F2 has been shown to contribute to pathogenesis by delaying viral clearance , potentially by eliminating immune cells via apoptosis [3] . A recent study has identified a single point mutation at amino acid position 66 in the PB1-F2 protein of the 1918 pandemic and of an H5N1 influenza virus strain , which is associated with increased virulence [4] . This mutation consists of a change of the amino acid asparagine ( 66N ) to serine ( 66S ) and causes increased weight loss and viral loads in infected mice . In search of the molecular mechanism for these findings , microarray analyses on whole lung homogenates of mice infected with isogenic viruses expressing either PB1-F2 66N or 66S were performed . Interestingly , infection with a virus expressing PB1-F2 N66S lead to a suppression of interferon-stimulated genes ( ISGs ) at an early stage of infection [5] . In the present study , we aim to verify these findings in vitro and elucidate the molecular mechanism for the IFN antagonism function of PB1-F2 N66S . The innate immune system is the first line of defense in response to viral infection . Besides Toll-like receptors ( TLR ) and Nod-like receptors ( NLRs ) in the endosome and cytoplasm , respectively , RNA helicases such as the retinoic acid inducible gene-I ( RIG-I ) and the melanoma differentiation-associated gene-5 ( MDA-5 ) are able to recognize characteristic patterns of invading pathogens and induce the production of type I interferons ( IFN ) , potent antiviral molecules [6] , [7] . In influenza virus infected cells , RIG-I has been shown to be the major sensor of viral RNA leading to type I IFN production , as knock down of RIG-I expression has been demonstrated to abolish type I IFN secretion in response to virus infection [8] . Expression of type I IFN genes has been found to be regulated by the so-called enhanceosome , constituted by the transcription factors IRF3/7 , NF-κB and ATF/c-Jun [9] . Upon recognition of viral RNA species , RIG-I interacts with the mitochondrial antiviral signaling protein ( MAVS , also known as IPS-1 , VISA , CARDIF ) in the mitochondrial membrane . This leads to the phosphorylation and activation of both IRF3 and IRF7 by IKKε and TBK1 [10] . Upon secretion , IFN binds to specific IFN receptors in an autocrine or paracrine manner and activates the JAK/STAT pathway . This leads to the formation of the ISG factor 3 ( ISGF3 ) transcription complex which drives the expression of antiviral genes such as protein kinase R ( PKR ) , Mx GTPases and others . Viruses have evolved mechanisms to counteract the antiviral IFN production and/or signaling pathways . Nipah virus V protein has been shown to bind STAT1 and thus inhibit nuclear translocation of ISGF3 [11] , [12] . The VP35 protein of Ebola virus can block IFN induction by interacting with both TBK1 and IKKε and interfere with the phosphorylation of IRF3 [13] . Influenza virus expresses the non-structural protein 1 ( NS1 ) which can inhibit IFN induction using multiple strategies [14] . NS1 has been shown to bind dsRNA and thus mask viral RNA species from recognition [15] , [16] . Furthermore , NS1 interacts with RIG-I and its co-activator TRIM25 leading to impaired activation of the IRF3 , ATF/c-Jun and NF-κB transcription factors that drive IFN-β expression [17] , [18] , [19] , [20] . In addition , NS1 can interact with PKR and inhibit its activation [21] , [22] . Recent reports describe an IFN antagonism function also for PB2 and other polymerase proteins [23] , [24] . In the study by Graef et al . [23] , PB2 was found to interact with MAVS at the mitochondria and thus impair IFN-β production without apparently affecting viral replication in vitro . Besides NS1 , PB1-F2 is another non-structural protein of influenza viruses [25] . In contrast to NS1 , the molecular mechanism for the contribution of PB1-F2 to the pathogenicity of influenza virus has not been thoroughly studied . PB1-F2 triggers the intrinsic apoptosis pathway by interacting with the mitochondrial adenine nucleotide translocator 3 ( ANT3 ) and voltage-dependent anion channel 1 ( VDAC1 ) proteins in vitro [26] and it induces apoptosis in a strain-specific way [27] . Several studies demonstrated that PB1-F2 has pro-inflammatory activity and exerts its function as a virulence factor by causing increased immune cell infiltration , elevated cytokine levels and tissue damage [4] , [27] , [28] . A study by Mazur et al . describes an interaction of PB1-F2 with the PB1 subunit of the viral polymerase complex in virus infected cells , but this interaction does not seem to contribute to pathogenicity in a mouse model [29] , [30] . In search for residues in the PB1-F2 protein that confer increased virulence , we have previously identified a serine at position 66 that is associated with increased pathogenicity by suppressing the early IFN response in a mouse infection model [4] , [5] . Herein , we confirm and characterize the IFN antagonism function of PB1-F2 in vitro , finding that a serine at position 66 enhances the anti-IFN activity in an overexpression system , when expressed from a Newcastle disease virus ( NDV ) vector as well as in the context of influenza virus infection . Furthermore , we show that PB1-F2-mediated IFN suppression is exerted via its C-terminal domain at the level of the MAVS adaptor protein . We also investigated the relationship between PB1-F2 and the well characterized IFN antagonist NS1 and observed that PB1-F2 N66S in combination with NS1 lead to lower IFN induction compared to NS1 alone . Based on our results we propose that PB1-F2 proteins of highly pathogenic influenza virus strains contribute to pathogenesis by suppressing the host innate response at the level of the MAVS adaptor protein . This is the first report that links a molecular mechanism to the observed pathogenic phenotype caused by PB1-F2 in vivo .
This study was carried out in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health . The protocol was approved by the Institutional Animal Care and Use Committee at Mount Sinai School of Medicine ( Permit Number: 03-0058 ) . Mice were sacrificed for the isolation of bone marrow according to these guidelines and all efforts were made to minimize suffering . Madin Darby Canine Kidney ( MDCK ) , 293T , Vero and A549 cells were obtained from ATCC ( Manassas , VA , USA ) and were maintained in Minimal Essential Medium ( MEM ) or Dulbecco's Modified Eagle Medium ( DMEM ) ( Gibco , Invitrogen , San Diego , CA , USA ) supplemented with 10% fetal bovine serum ( FBS , Hyclone , South Logan , UT , USA ) and penicillin/streptomycin ( Gibco ) . LA-4 cells were obtained from ATCC and maintained in F-12K medium ( Gibco ) supplemented with 15% FBS ( Hyclone ) and penicillin/streptomycin ( Gibco ) . The generation of the MDCK cell line constitutively expressing the IFN-β reporter ( MDCK-IFN-beta luc ) has been described before [31] and this cell line was maintained in DMEM with 10% FBS ( Hyclone ) and penicillin as well as hygromycin and geneticin ( Gibco ) . Monoclonal antibodies to actin , FLAG and HA were obtained from Sigma-Aldrich ( St . Louis , MO , USA ) . NP protein levels were detected using a monoclonal NP antibody generated by our laboratory ( clone 28D8 ) . The polyclonal rabbit NS1 antibody was raised against amino acids 1–73 of the Tx/98 swine virus NS1 protein . The polyclonal rabbit sera against PB1-F2 has been described before [26] . The polyclonal rabbit serum against NDV was prepared by Dr . Qinshan Gao . Peripheral blood mononuclear cells were isolated by Ficoll density gradient centrifugation ( Histopaque , Sigma- Aldrich ) from buffy coats of healthy human donors ( New York Blood Center ) as previously described [32] . Briefly , CD14-positive cells were immunomagnetically purified using anti-human CD14 antibody-labeled magnetic beads and iron-based MiniMACS LS columns ( Miltenyi Biotec , Auburn , CA , USA ) . After elution from the columns , cells were plated ( 106 cells/mL ) in DC medium ( RPMI medium [Invitrogen] , 2 mM L-glutamine [Invitrogen] , 1 mM Sodium Pyruvate [Invitrogen] and penicillin-streptomycin [Invitrogen] and 10% FBS [Hyclone] ) supplemented with 500 U/mL human granulocyte macrophage colony-stimulating factor ( GM-CSF , Peprotech , Rocky Hill , NJ , USA ) , and 1 , 000 U/mL human interleukin-4 ( IL-4 , Peprotech ) and incubated for 5 days at 37°C . Murine dendritic cells were obtained from bone marrow as described previously [33] . Bone marrow was prepared from the leg bones of 7–9 week old BL/6 mice ( Jackson Laboratories , Bar Harbor , ME , USA ) . Tibia and femur were aseptically dissected and the bone marrow flushed out . Bone marrow cells were cultured with Iscove's modified Dulbecco's medium ( Gibco ) supplemented with GM-CSF ( 20 ng/mL; Peptrotech ) , non-essential amino acids ( Gibco ) , 50 mM β-mercaptoethanol ( Gibco ) , 10% FBS ( Hyclone ) and penicillin-streptomycin ( Gibco ) at 37°C in 5% CO2 for 7 days . Fresh media was added every second day . Floating dendritic cells were recovered and seeded for subsequent viral infection the following day . The influenza A/Puerto Rico/8/1934 ( PR8; H1N1 ) viruses were rescued as described previously [34] . Briefly , 293T cells were transfected with seven bidirectional pDZ constructs for PB2 , PA , NP , HA , NA , M and NS ( giving rise to viral genomic and messenger RNAs ) as well as a pPolI construct expressing the wildtype ( WT ) or the PB1-F2 N66S PB1 . In addition , four pCAGGS protein expression vectors encoding the subunits of the WSN viral polymerase and the nucleocapsid protein were added . The transfected 293T cells were injected into 10-day old embryonated chicken eggs ( Charles Rivers Laboratories , Wilmington , MA , USA ) and propagated for 48 h . Rescued viruses were plaque purified on MDCK cells , propagated in 10-day old embryonated chicken eggs and sequenced to confirm the presence of the introduced mutations . The N66S mutation in the PB1-F2 open reading frame was introduced by a single point mutation at position 315 in the PB1 gene which changed the nucleotide from an A to a G using the Stratagene Quick-Change mutagenesis kit ( Stratagene , La Jolla , CA , USA ) . This mutation does not affect the PB1 amino acid sequence . For rescue of the recombinant PR8 virus expressing a dsRNA/TRIM25 binding mutant form of NS1 ( R38A/K41A ) , a pCAGGS-NS1 expression plasmid has been added to the 293T transfection mixture and 7-day old embryonated chicken eggs ( Charles Rivers Laboratories ) were used as described before [20] . The PB1-F2 ( WT and N66S , PR8 ) expressing Newcastle disease viruses ( NDV ) were generated from the LaSota strain cDNA as described before [35] , [36] . The PB1-F2 and GFP genes were inserted into the NDV genome between the P and M segments via Sac II restriction sites . The NDV-GFP virus was prepared by Dr . Qinshan Gao [37] . The NDV-NS1 ( B1 strain ) virus has been described previously [32] . Primary human DCs were infected with the NDV recombinant viruses at a multiplicity of infection ( MOI ) of 2 or the NS1 dsRNA/TRIM25 binding mutant PR8 influenza viruses at an MOI of 0 . 5 in serum-free DC media for 45 min at 37°C . After the adsorption time , DCs were plated in complete DC medium and incubated for indicated timepoints at 37°C . Then , cells were recovered by centrifugation at 400× g for 10 min for subsequent RNA isolation . In addition , supernatants were collected for cytokine production evaluation by Multiplex ELISA ( Millipore , Billerica , MA , USA ) . Adherent cells were washed with PBS and virus diluted in PBS supplemented with 0 . 3% bovine albumin ( BA , Gibco ) and penicillin streptomycin ( Gibco ) was added for 1 h at 37°C . Cells were subsequently washed and growth media was added in case of single cycle analyses . For multicycle analyses , DMEM ( Gibco ) supplemented with 0 . 1% FBS ( Hyclone ) and 0 . 3% BA ( Gibco ) including 1 µg/mL trypsin was added . RNA from human DCs ( 5×105 ) was extracted using the Absolutely RNA Microprep Kit ( Stratagene ) . RNA yields were evaluated in a Nanodrop spectrophotometer ( Nanodrop technologies , Wilmington , DE , USA ) at 260 nm , and 500 ng of RNA was reverse transcribed using the iScript cDNA Synthesis Kit ( Bio-Rad , Hercules , CA , USA ) according to the manufacturer's instructions . RNA from all other cells was harvested using β-mercaptoethanol-containing RLT buffer from the RNeasy Qiagen kit and RNA was extracted following the manufacturer's instructions; cDNA was generated using the Superscript First-Strand RT-PCR kit ( Invitrogen ) according to the manufacturer's instructions . Evaluation of mRNA levels was carried out using iQ SYBR Green SuPermix ( Bio-Rad ) according to the manufacturer's instructions . The PCR temperature profile was 95°C for 10 min , followed by 40 cycles of 95°C for 10 s , 60°C for 60 s . The mRNA levels of target genes were normalized to α-tubulin and rps11 expression for human DCs and 18S for all other cell types , respectively . The sequences of the primers used for qRT-PCR analyses on DC samples have been described elsewhere [38] . The primers for viral M and PB1 RNA levels detect cRNA , vRNA and mRNA and the sequences are as follows: 5′-TCAGGCCCCCTCAAAGCCGA-3′ ( forward ) and 5′-GGGCACGGTGAGCGTGAACA-3′ ( reverse ) for M , 5′-AATTCTTCCCCAGCAGTTCA-3′ ( forward ) and 5′-TTTTTGCCGTCTGAGCTCTT-3′ ( reverse ) for PB1 . The primers used to quantify murine IFN-β are 5′-CAGCTCCAAGAAAGGACGAAC-3′ ( forward ) and 5′-GGCAGTGTAACTCTTCTGCAT-3′ ( reverse ) . The primers for murine IP-10 are: 5′-TTCACCATGTGCCATGCC-3′ ( forward ) and 5′-GAACTGACGAGCCTGAGCTAGG-3′ ( reverse ) . The primers used for A549 cells are: 5′-TCTGGCACAACAGGTAGTAGGC-3′ ( forward ) and 5′-GAGAAGCACAACAGGAGAGCAA-3′ ( reverse ) for IFN-β , 5′-GTAACCCGTTGAACCCCATT-3′ ( forward ) and 5′-CCATCCAATCGGTAGTAGCG-3′ ( reverse ) for 18S and 5′-GGAACCTCCAGTCTCAGCACCA-3′ ( forward ) and 5′-AGACATCTCTTCTCACCCTTC-3′ ( reverse ) for IP-10 . All experiments included biological triplicates and technical duplicates . CXF Manager software ( Bio-Rad ) was used to analyze the normalized relative mRNA levels in the samples . The levels of IFN-α and IP-10 proteins in human DC supernatants after infection was quantified using the MILLIPLEX Multi-Analyte Profiling Human Cytokine/Chemokine Kit ( Millipore ) according to the manufacturer's instructions . Data were analyzed using the Milliplex Analyst Software ( Millipore ) . The pCAGGS vector possessing a chicken β-actin promoter has been described previously [39] . An N-terminal FLAG tag was added to PB1-F2 WT and N66S ( PR8 ) as well as NS1 genes ( PR8 ) by PCR with 5′ gene-specific primers containing the tag sequence and cloned into pCAGGS using EcoRI and XhoI restriction sites . The HA-tagged Nipah virus V protein , the FLAG-tagged Ebola virus VP35 protein , FLAG-tagged RIG-I N , HA-tagged MAVS , FLAG-tagged TRIF , FLAG-tagged TBK1 and IKKε , IRF3-5D expression plasmids have been described elsewhere [13] , [40] . The generation and subcellular localization of the PB1-F2 truncation constructs have been described previously [26] . Ten-day old embryonated chicken eggs ( Charles Rivers Laboratories ) were inoculated with 100 plaque forming units ( PFU ) of virus and incubated at 37°C for the indicated amount of time and subsequently placed at 4°C . Allantoic fluids were harvested and centrifuged at 3 , 000 rpm for 30 min at 4°C . A549 cells were infected at a multiplicity of infection ( MOI ) of 0 . 01 and supernatants were harvested at the indicated time points and centrifuged at 1 , 200 rpm for 5 min to remove cell debris . The viral titers were determined via plaque assays on MDCK cells . The NDV-GFP bioassay to quantify IFN levels has been described before [12] . Briefly , 293T cell supernatants were harvested and spun down at 1 , 200 rpm for 5 min to remove cell debris . Vero cells grown in a 96-well format were overlaid with serial 2-fold dilutions of the supernatants for 24 h and subsequently infected with NDV-GFP at an MOI of 5 . The fluorescence intensities were measured with a plate reader ( Beckman Coulter DTX 880 instrument ) at 18–24 hours post infection ( hpi ) ( excitation wavelength: 485 nm , emission wavelength: 535 nm ) . Images were taken using a fluorescence microscope ( Olympus IX70 ) . For IFN-β reporter assays , 293T cells were transfected with lipofectamine 2000 ( Invitrogen ) at a ratio of 1∶1 with plasmid DNA and lysed 24 h post transfection . For the ISRE reporter assay , transfected 293T cells were stimulated with 1000 U/mL of universal type I interferon ( PBL interferon source , Piscataway , NJ , USA ) and lysed 24 h later . For luciferase assays , cells were lysed using the provided lysis buffer by the dual-luciferase assay kit according to the manufacturer's instructions ( Promega , Madison , WI , USA ) . The fold-induction was calculated as the ratio of stimulated versus unstimulated samples . Expression plasmids for RIG-I N , MAVS , TBK1 , IKKε , IRF3-5D and TRIF were transfected as stimuli for the IFN-β reporter assays . Cells were lysed in urea buffer ( 6 M urea , 2 M β-mercaptoethanol , 4% sodium dodecyl sulfate [SDS] ) and sonicated three times at output level 3 . 0 for 5 s . Samples were run on 4–20% precast gradient gels ( Bio-Rad ) and transferred onto polyvinylidene fluoride ( PVDF ) membranes ( GE Healthcare , Buckinghamshire , UK ) . Blotting against HA or FLAG-tagged proteins as well as PB1-F2 , actin and viral proteins was achieved by using antibody dilutions of 1∶1000 in 5% non-fat dry milk-containing PBS-Tween-20 0 . 05% . Anti-NP , NS1 and NDV antibodies were used at a dilution of 1∶2000 and 1∶5000 , respectively . Horseradish peroxidase-conjugated secondary antibodies ( GE Healthcare ) were used at a dilution of 1∶5000 . Hela cells were transfected as described above for 293T cells to overexpress HA-tagged MAVS , FLAG-tagged PB1-F2 or empty vector and allowed to adhere to round glass slides in a 24-well plate . Twenty-four h post transfection , cells were fixed with 4% para-formaldehyde and stained with anti-FLAG and anti-HA antibodies as well as DAPI . Secondary antibodies conjugated to Alexa 488 and Alexa 555 ( Invitrogen ) were used to visualize the proteins . Images were taken on an LSM 510 Meta confocal microscope ( Carl Zeiss MicroImaging GmbH , Jena , Germany ) at a magnification of 100× . Confocal laser scanning microscopy was performed at the MSSM-Microscopy Shared Resource Facility , supported with funding from NIH-NCI shared resources grant ( 5R24 CA095823-04 ) , NSF Major Research Instrumentation grant ( DBI-9724504 ) and NIH shared instrumentation grant ( 1 S10 RR0 9145-01 ) .
In our previous study we have shown that an influenza virus expressing PB1-F2 66S of an H5N1 virus suppresses interferon-stimulated genes ( ISGs ) at an early stage of infection in vivo compared to an isogenic virus expressing PB1-F2 66N [5] . To confirm and characterize the interferon ( IFN ) antagonism function of PB1-F2 , we employed several in vitro assays . We first examined whether PB1-F2 could block IFN secretion induced by constitutively active RIG-I , since RIG-I is the most upstream molecule involved in triggering the antiviral IFN response against influenza virus . We transfected 293T cells , which are highly transfectable , with a plasmid expressing constitutively active RIG-I ( RIG-I N ) as well as an empty vector ( pCAGGS ) , NS1 ( PR8 ) or PB1-F2 ( PR8 ) WT or N66S expressing constructs and measured IFN secretion by these cells in an NDV-GFP bioassay . Vero cells were overlaid with 293T cells supernatants and infected with NDV-GFP virus 24 hours later . The replication efficiency of the NDV-GFP virus is a measure for the amount of IFN in the supernatants where a high GFP signal indicates IFN suppression activity and low GFP expression is a read-out for high IFN concentrations . Expression of an empty vector did not block RIG-I N induced IFN secretion , whereas the well described IFN antagonist NS1 enabled NDV-GFP to replicate efficiently ( Figure 1A ) . Interestingly , overexpression of PB1-F2 also blocked IFN production and a serine at position 66 ( N66S ) showed an increased IFN antagonism activity in the NDV-GFP bioassay ( Figure 1A ) . We did not observe any cytopathic effects by expressing PB1-F2 proteins without any additional stimuli which is in accordance with previous findings [26] . Of note , the NDV-GFP bioassay measures not only the amount of secreted IFN , but also the activity of other antiviral molecules . Hence , to examine whether PB1-F2 interferes with the activation of the IFN-β promoter , we performed an IFN-β reporter assay , using a reporter construct that carries the IFN-β promoter driving the expression of a firefly luciferase gene . This reporter was transfected into 293T cells along with a constitutively expressed renilla luciferase reporter control to monitor cytotoxicity and transfection efficiency . As shown in Figure 1B , overexpression of RIG-I N strongly induced the reporter ( approximately 100-fold ) which was not affected by the empty vector control ( pCAGGS ) , but was efficiently blocked by NS1 . In accordance with the bioassay data , PB1-F2 decreased activation of the IFN-β reporter and PB1-F2 N66S was approximately two-fold more efficient in inhibiting the reporter compared to PB1-F2 wildtype ( WT ) at equal protein amounts ( Figure 1B ) . Compared to NS1 , PB1-F2 WT inhibited IFN induction about four times less efficiently , whereas PB1-F2 N66S was only two-fold less efficient than NS1 in these assays ( Figure 1A and 1B ) . We also tested the influenza virus nucleoprotein ( NP ) and observed no inhibition of the RIG-I N induced IFN-β reporter ( data not shown ) . To examine whether PB1-F2 proteins from other strains besides PR8 could inhibit IFN induction , we tested both PB1-F2 66N and 66S proteins from the A/Brevig Mission/1/1918 ( H1N1 ) pandemic and A/Viet Nam/1203/2004 ( H5N1 ) strains and found similar results ( data not shown ) . Activation of RIG-I leads to the secretion of type I IFN which binds to IFN receptors to activate the JAK/STAT pathway resulting in the establishment of an antiviral state in a paracrine or autocrine manner . To examine whether PB1-F2 is also able to interfere with IFN signaling , we employed an interferon-stimulated response element ( ISRE ) reporter assay that contains an ISG54 promoter fused to a firefly luciferase gene . 293T cells were transfected with ISRE and renilla reporters as well as an empty vector , PB1-F2 plasmids or a vector expressing Nipah virus V protein as a positive control [11] . Twenty-four hours post transfection , 293T cells were treated with universal type I IFN and analyzed for reporter activity 24 hours later . Nipah virus V protein strongly repressed activation of the ISRE reporter , whereas empty and PB1-F2 expressing vectors did not affect the ISRE reporter ( Figure 1C ) . We have also not observed an effect of PB1-F2 on the ISRE reporter in unstimulated cells ( data not shown ) . It has been demonstrated that a C-terminal portion of PB1-F2 interacts with both ANT3 and VDAC1 to induce apoptosis , while an N-terminal fragment of PB1-F2 is not able to trigger cell death [26] . In view of this finding , we tested whether the IFN antagonism function of PB1-F2 was confined to the N- or C-terminal domain . For this purpose , we used an N-terminal fragment of PB1-F2 which contains amino acids 1–38 and a C-terminal portion that contains amino acids 39–87 of the PR8 PB1-F2 protein . The design of the PB1-F2 truncations is based on previous structural data by Bruns et al . [41] . Of note , the C-terminal domain includes the mitochondrial localization sequence ( MLS ) . These PB1-F2 fragments were fused to GFP for protein stability and also contain an N-terminal HA tag as described previously [26] . As shown in Figure 1D , the C-terminal fragment and the full-length PB1-F2 protein which is also fused to GFP and contains an HA tag , inhibited the IFN-β reporter to similar levels . Conversely , the N-terminal portion was unable to block the reporter even though it was expressed at higher levels than the other constructs . It is possible that localization of these peptides to the mitochondria is necessary for the IFN antagonism function , so we also tested an N-terminal PB1-F2 fragment that contains an MLS derived from the human cytochrome C oxidase as described by Zamarin et al . [26] . Even though this peptide localizes to the mitochondria as efficiently as the full-length PB1-F2 construct [26] , it was still unable to suppress IFN induction in this assay ( data not shown ) . Collectively , these results suggest that the C-terminus , which contains the characteristic amphihelical structure , mediates the IFN antagonism function of PB1-F2 . To investigate whether PB1-F2 66S is a stronger IFN antagonist than PB1-F2 66N in the context of influenza virus infection , we employed reverse genetics to generate a PR8 influenza virus expressing PB1-F2 66S or 66N ( WT ) . We then examined the growth kinetics of these two viruses in A549 cells , a human lung epithelial cell line which supports efficient viral replication , and in 10-day old embryonated chicken eggs . Both viruses displayed similar replication kinetics in both A549 cells and in ovo ( Figure 2A ) . We next examined IFN induction by these two viruses at early timepoints of infection . For this purpose we infected an MDCK IFN-β reporter cell line with the PR8 viruses either expressing PB1-F2 66N or 66S . At 4 hours post infection ( hpi ) , PR8 WT virus induced the IFN-β reporter approximately 5-fold over mock infected cells . This weak induction is due to the strong IFN antagonist activity of the NS1 protein . Yet , PR8 N66S suppressed the IFN-β reporter by 2-fold compared to the PR8 WT virus ( Figure 2B ) . At 8 and 12 hpi similar results were observed . Of note , this phenomenon was not due to increased PB1-F2 protein expression by the PR8 N66S virus ( data not shown ) . NS1 and NP proteins from both viruses were expressed at equal levels with increasing protein expression over time , as determined by Western blot analysis , indicating that the enhanced IFN suppression activity by the PR8 N66S virus was not due to increased replication in these cells or higher NS1 protein expression ( Figure 2B ) . In Western blots with higher exposure , NP protein levels are visible at 4 hpi showing similar levels in PR8 WT and PR8 N66S virus infected cells ( Figure 2B ) . It has been suggested that PB1-F2 exerts its pro-apoptotic function specifically in immune cells [2] . We thus examined the IFN antagonism function of PB1-F2 N66S in both epithelial as well as immune cells . Murine dendritic cells were infected with PR8 viruses expressing either 66N or 66S PB1-F2 at an MOI of 2 and RNA was harvested 8 hpi to analyze gene expression via qRT-PCR . Infection with PR8 WT virus induced IFN-β mRNA levels approximately 20-fold over mock ( Figure 2C ) . Infection with PR8 N66S , in contrast , induced two-fold less IFN-β ( Figure 2C ) . We also examined mRNA levels of IP-10 , an ISG , which showed a similar trend to the IFN-β induction by the two PR8 viruses ( Figure 2C ) . We also infected murine lung epithelial ( LA-4 ) cells with the PR8 viruses and quantified IFN-β as well as IP-10 mRNA levels . Infection with PR8 WT virus did not efficiently increase IFN-β in these cells over mock treated cells , but we observed a two-fold suppression of IFN-β induction by the PR8 N66S virus ( Figure 2D ) . In LA-4 cells , there was a more pronounced suppression of IP-10 mRNA levels compared to dendritic cells ( Figure 2C and 2D ) . Newcastle disease virus ( NDV ) has been shown to be a potent IFN inducer by activating RIG-I [42] . Therefore , we asked the question whether PB1-F2 could block NDV induced IFN activation in primary human dendritic cells . Dendritic cells are important immune effector cells that are involved in clearing viral infections by secreting pro-inflammatory cytokines as well as type I IFN and bridging the innate and adaptive immune responses . It has been demonstrated that primary human dendritic cells are a suitable ex vivo model to study human influenza A virus host responses [43] . We generated recombinant NDV viruses that express GFP or PB1-F2 ( WT and N66S ) and infected primary human dendritic cells to investigate the induction of type I IFN in this model . As shown in Figure 3A , NDV-PB1-F2 N66S replicated more efficiently than the NDV-PB1-F2 WT and NDV-GFP viruses at 18 hpi and 22 hpi . To test whether this growth advantage may be due to suppressed IFN induction by PB1-F2 N66S expressed from the NDV vector , we analyzed IFN induction at 14 hpi by the three recombinant NDV viruses when replication was still found to be similar . NDV-GFP induced high levels of IFN as well as IP-10 as measured by qRT-PCR and ELISA ( Figure 3B and 3C ) . Expression of PB1-F2 WT did not suppress IFN and IP-10 levels compared to GFP , whereas PB1-F2 N66S efficiently inhibited IFN induction in NDV infected dendritic cells ( Figure 3B and 3C ) . These results indicate that PB1-F2 N66S is also able to suppress IFN production in primary human dendritic cells confirming that this inhibitory effect is not strictly cell type-specific . The NS1 protein is the major IFN antagonist of influenza viruses as the knockout of NS1 strongly attenuates the virus in IFN competent cells [44] . NS1 employs multiple strategies to counteract the initiation and establishment of an antiviral state in virus infected cells [45] . One of these strategies is the masking of viral RNA species from recognition by RIG-I [16] , [17] . Within the N-terminal domain of NS1 , the basic residues R38 and K41 were found to mediate binding to dsRNA species [16] and mutating these residues to alanine was shown to induce a robust IFN response in influenza virus infected cells [15] . In addition , these mutations also result in loss of NS1 interaction with TRIM25 , an interaction that is required for inhibition of RIG-I activation mediated by this E3 ligase [20] . We thus aimed to examine the IFN induction by viruses that express a dsRNA/TRIM25 binding mutant form of NS1 and PB1-F2 66N or 66S . For this purpose we rescued PR8 viruses with the described point mutations in NS1 ( R38A/K41A ) and those that express the WT or N66S form of PB1-F2 . Infection of the MDCK-IFN-β reporter cell line with these two mutant viruses resulted in approximately 3 log higher induction of the IFN-β reporter compared to the PR8 viruses with WT NS1 at 8 hpi ( Figure 2B and 4A ) . At 4 hpi , no remarkable difference in IFN induction was seen between the PB1-F2 66N or 66S expressing viruses . At 8 hpi , however , the NS1 dsRNA/TRIM25 binding mutant virus expressing PB1-F2 66S induced significantly less IFN than the isogenic virus expressing PB1-F2 66N ( Figure 4A ) . At 12 hpi , the difference in IFN induction became minimal ( Figure 4A ) and a substantial detachment of cells was observed at this time point ( data not shown ) . At 4 hpi and 8 hpi , the NP and NS1 protein levels were comparable in cells infected with both viruses . Of note , the NP protein levels in cells infected with the NS1 mutant viruses do not increase as dramatically between 4 and 8 hpi compared to those in cells infected with PR8 viruses expressing wildtype NS1 ( Figure 2B ) . This finding indicates that the NS1 dsRNA binding mutant viruses are attenuated compared to the wildtype NS1 expressing viruses . Next , we infected primary human dendritic cells with the NS1 dsRNA/TRIM25 binding mutant viruses and analyzed the induction of type I IFN via qRT-PCR and ELISA . At 6 hpi and 8 hpi , IFN-β mRNA as well as IFN-α protein levels were decreased by the PB1-F2 N66S expressing virus compared to the isogenic virus ( Figure 4B and 4C ) which confirms the data obtained in the MDCK IFN-β reporter cell line ( Figure 4A ) . We also performed qRT-PCR analyses to quantify IFN-α mRNA levels and found a similar trend to IFN-β mRNA levels ( data not shown ) . To test this phenomenon in epithelial cells , we also infected A549 cells with the dsRNA/TRIM25 binding mutant NS1 viruses . As shown in Figure 4D , the PB1-F2 N66S expressing virus induced less IFN and IP-10 compared to the PB1-F2 WT virus at 6 hpi and 8 hpi ( data not shown ) . In summary , these data demonstrate that even in the presence of an NS1 protein that lacks the dsRNA and TRIM25 binding function , PB1-F2 66S is able to reduce the induction of IFN compared to PB1-F2 66N . To characterize the relationship between NS1 and PB1-F2 regarding IFN antagonism , we overexpressed NS1 with either PB1-F2 66N or 66S in 293T cells and analyzed the induction of the IFN-β reporter activated by RIG-I N . As shown in Figure 5A , PB1-F2 66N did not significantly reduce IFN promoter activation compared to the empty vector control when co-expressed with NS1 . In contrast , PB1-F2 66S further decreased IFN induction when co-expressed with NS1 even though PB1-F2 66N is expressed more efficiently as shown in Western blot analyses ( Figure 5A ) . To confirm these findings in the human DC model , we performed a co-infection experiment with the recombinant NDV viruses . Co-infection of human DCs with NDV-NS1 and NDV-PB1-F2 66N induced similar levels of IFN compared to co-infection with NDV-NS1 and NDV-GFP ( Figure 5B ) . In contrast , co-infection of primary human DCs with NDV-NS1 and NDV-PB1-F2 N66S strongly reduced type I IFN and IP-10 mRNA levels ( Figure 5B ) . Collectively , these data indicate that PB1-F2 N66S , but not PB1-F2 WT , enhances the IFN antagonism activity of NS1 . In order to examine at which stage the PB1-F2 protein inhibits the IFN production pathway , we used different stimuli downstream of RIG-I to induce the IFN-β reporter in 293T cells . We overexpressed MAVS , TBK1 , IKKε and IRF3-5D , a phosphomimetic form of IRF3 , and analyzed the activation of the IFN-β reporter in the presence of PB1-F2 . We observed that PB1-F2 WT and N66S inhibited the MAVS-induced IFN-β reporter , but did not affect any factors downstream of MAVS such as TBK1 or IRF3-5D ( Figure 6A , 6C , 6D ) . We performed densitometry analyses and found that the MAVS expression levels were not decreased in NS1 , PB1-F2 WT or PB1-F2 N66S expressing cells compared to the empty vector control ( data not shown ) . Upon stimulation with IKKε , we found a slight enhancement of the IFN-β reporter activity in cells expressing PB1-F2 WT ( Figure 6B ) . For TBK1 and IKKε ( Figure 6B and 6C ) , Ebola virus VP35 protein was used as a positive control since it has been previously described by Prins et al . to bind to both kinases and thus interfere with the phosphorylation of IRF3 [13] . NS1 has been reported to inhibit MAVS in an IFN-β reporter assay by a yet unclear mechanism [46] . To confirm that PB1-F2 inhibits IFN production at the level of MAVS , we tested whether PB1-F2 affects a TRIF-induced IFN-β reporter . TRIF is an adaptor protein that mediates IFN production upon activation of endosomal TLRs in a MAVS-independent manner . As shown in Figure 6E , the PB1-F2 proteins did not affect TRIF-induced IFN which indicates that PB1-F2 inhibits IFN in a MAVS-dependent fashion . Next , we investigated whether PB1-F2 co-localizes with MAVS at the mitochondria to confirm the reporter assay data shown in Figure 6A . For this purpose we overexpressed HA-tagged MAVS and FLAG-tagged PB1-F2 proteins in Hela cells and examined localization patterns via confocal microscopy . We chose Hela cells for our studies because they have been previously used to investigate MAVS localization upon influenza virus infection [47] . As shown in Figure 7A , both PB1-F2 66N and 66S co-localized with MAVS while the influenza virus nucleoprotein ( NP ) , which does not affect IFN induction ( data not shown ) , did not . It has been reported that MAVS undergoes a redistribution in the mitochondrial membrane upon activation of the IFN pathway [47] . We thus asked whether PB1-F2 could also associate with redistributed MAVS . Upon infection of transfected Hela cells with Sendai virus ( SeV ) , we observed a relocalization of MAVS into distinct speckle-like structures ( Figure 7B ) as has been shown previously by Onoguchi et al . [47] . Interestingly , PB1-F2 also co-localized with these structures , while NP did not appear to do so ( Figure 7B ) . In summary , these data indicate that PB1-F2 inhibits the induction of IFN at the level of MAVS , possibly by interacting with MAVS and/or affecting MAVS function .
In our previous work , we have shown that infection of mice with a recombinant influenza virus expressing PB1-F2 of the H5N1 Hong Kong 1997 strain with an N66S mutation results in decreased IFN induction in the lungs compared to infection with the isogenic wildtype virus without affecting the levels of the induction of apoptosis [5] . Here , we report on the molecular mechanism by which an N66S point mutation in the PR8 PB1-F2 protein leads to decreased IFN induction in both overexpression as well as viral infection models ( Figure 1 and 2 ) . We show that PB1-F2 wildtype ( 66N ) has IFN antagonism activity in a RIG-I N induced IFN-β reporter assay and an NDV-GFP bioassay when overexpressed in 293T cells . However , the anti-interferon function of PB1-F2 66N seems to be weaker than that of NS1 or PB1-F2 66S in a 293T cell overexpression system ( Figure 1A and 1B ) . In human dendritic cells , PB1-F2 66N shows no to minimal IFN antagonism activity compared to GFP when expressed from an NDV vector that induces high levels of IFN . This may be due to a diminished IFN antagonism activity of the PB1-F2 WT protein in immune cells ( Figure 3 ) . These results indicate that PB1-F2 66N is a weaker IFN antagonist than NS1 or PB1-F2 66S in all systems studied . Interestingly , PR8 N66S virus suppressed IP-10 mRNA levels to a greater extent in epithelial cells than in dendritic cells ( Figure 2C and 2D ) which may have to do with a differential regulation of IP-10 expression in these cell types . We also tested the PB1-F2 proteins of the 1918 pandemic and the A/Viet Nam/1203/2004 ( H5N1 ) viruses and found an inhibition of RIG-I N induced IFN-β reporter in 293T cells ( data not shown ) . We thus believe that the IFN antagonism activity of PB1-F2 is a general function shared by various influenza A virus strains . The pro-apoptotic activity was found to be mediated by the C-terminal domain of PB1-F2 which interacts with VDAC1 and ANT3 [26] . Interestingly , we also found that this C-terminal region spanning amino acids 38 to 87 was sufficient to inhibit the induction of IFN ( Figure 1D ) . Further work will focus on characterizing the minimal region needed to suppress IFN and identifying key residues mediating the IFN antagonism function of PB1-F2 . Also , it will be important to determine the relationship between the pro-apoptotic and anti-interferon function of PB1-F2 . A recent report describes a possible anti-apoptotic role of MAVS by interacting with and destabilizing VDAC1 [48] . PB1-F2 might interfere with the interaction between MAVS and VDAC1 and thus decrease IFN production while promoting VDAC1-mediated cell death . Further work will be necessary to test this hypothesis . Of note , a recent report by Le Goffic et al . indicates that PB1-F2 enhances the induction of IFN in epithelial cells [49] . In agreement with the findings of Le Goffic et al . , we observed decreased IFN levels of a PR8 virus lacking PB1-F2 compared to wildtype PR8 virus . Further characterization of the PB1-F2 knockout virus revealed increased levels of the N40 protein in infected cells , while N40 protein levels expressed by the PB1-F2 N66S virus were similar to that found in wildtype virus infected cells ( data not shown ) . The increased N40 expression levels in PB1-F2 ( PR8 ) knockout virus infected cells is in accordance with a report by Wise et al . [50] . N40 is a third protein expressed from the PB1 gene and has been identified as a truncated form of PB1 [50] . It is possible that the reduced IFN levels caused by PB1-F2 knockout viruses are due to altered N40 expression levels and we thus feel that the use of a PB1-F2 knockout virus is not suitable to assess the induction of IFN . Despite the reduced induction of IFN by PB1-F2 N66S , we do not observe a growth advantage of the PR8 PB1-F2 N66S virus in A549 cells or embryonated chicken eggs ( Figure 2A ) . It is possible that moderate differences in IFN levels induced by the virus do not affect viral replication in a cell line that is highly adapted to support influenza virus growth . PB1-F2 N66S may confer a growth advantage in cells where NS1 may be non-functional or restricted in its IFN antagonism capability . In fact , we have shown that a virus containing mutations that render NS1 deficient in dsRNA and TRIM25 binding induces high levels of IFN and PB1-F2 66S is able to reduce the induction of IFN by this virus compared to PB1-F2 66N ( Figure 4 ) . Furthermore , overexpression of PB1-F2 N66S in combination with NS1 further enhances the anti-IFN activity of NS1 ( Figure 5 ) . PB1-F2 N66S may target a protein that is not affected by NS1 or support NS1 in inhibiting a particular factor important for IFN production . We found that PB1-F2 inhibits a MAVS-induced IFN-β reporter , but doesn't affect the reporter when induced by factors that are downstream in the IFN production pathway , namely the IRF3 kinases or IRF3 ( Figure 6A–6D ) . Further work will be necessary to identify the domain of MAVS and/or adaptor proteins targeted by PB1-F2 . The question arises how a mutation from an asparagine to serine at position 66 could increase the IFN antagonism function of PB1-F2 . This residue lies within the minimal mitochondrial targeting sequence of PB1-F2 [51] and it is possible that an N66S mutation alters the efficiency of mitochondrial targeting , potentially by altering the secondary structure . This may allow for more efficient interactions with MAVS and/or insertion into the mitochondrial membrane to disrupt MAVS function , for example by interfering with the interactions of MAVS with adaptor proteins . Alternatively , an N66S mutation could create a phosphorylation site that is now recognized by cellular kinases . Multiple IFN antagonists of influenza virus have been described ( NS1 , PB1 , PB2 , PA , PB1-F2 ) [5] , [23] , [24] , [44] which demonstrates that the IFN antagonism strategies used by influenza virus are more complex than previously thought . It will be important to study and understand the biological significance for this functional redundancy . In this report , we have characterized the IFN antagonism function of PB1-F2 and provide evidence that PB1-F2 N66S works in conjunction with NS1 . Further work will be needed to understand the interplay between PB1-F2 and the polymerase proteins regarding IFN antagonism . Overall , our findings contribute to understanding the molecular mechanism for PB1-F2 mediated pathogenicity and highlight the importance of position 66 in the PB1-F2 protein for virulence .
|
Influenza viruses can cause global pandemics and are thus a major health concern . The novel H1N1 pandemic virus infected a large number of people , but resulted in relatively mild symptoms in the majority of cases . In contrast , the avian H5N1 viruses are associated with a high mortality rate , but are not transmitted from human to human . Understanding the viral and host factors that play a role in causing disease is crucial in developing effective vaccines and therapeutics . Furthermore , finding viral markers for high virulence may help predict the impact of newly emerging pandemic influenza viruses . We have previously established that a single amino acid substitution ( N66S ) in the viral PB1-F2 protein causes increased virulence in an H5N1 and the 1918 pandemic virus . Here we show that PB1-F2 N66S reduces the induction of interferon ( IFN ) , a potent antiviral molecule secreted by cells in response to infection . Furthermore , we demonstrate that the inhibition of IFN by PB1-F2 N66S occurs at the level of the mitochondrial antiviral signaling protein ( MAVS ) , a key player in the IFN production pathway . Our work here characterizes a new function for the PB1-F2 protein and how this function can lead to increased disease severity .
|
[
"Abstract",
"Introduction",
"Materials",
"and",
"Methods",
"Results",
"Discussion"
] |
[
"virulence",
"factors",
"and",
"mechanisms",
"viral",
"immune",
"evasion",
"virology",
"biology",
"microbiology",
"host-pathogen",
"interaction",
"pathogenesis"
] |
2011
|
The Influenza Virus Protein PB1-F2 Inhibits the Induction of Type I Interferon at the Level of the MAVS Adaptor Protein
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Glial cells are exquisitely sensitive to neuronal injury but mechanisms by which glia establish competence to respond to injury , continuously gauge neuronal health , and rapidly activate reactive responses remain poorly defined . Here , we show glial PI3K signaling in the uninjured brain regulates baseline levels of Draper , a receptor essential for Drosophila glia to sense and respond to axonal injury . After injury , Draper levels are up-regulated through a Stat92E-modulated , injury-responsive enhancer element within the draper gene . Surprisingly , canonical JAK/STAT signaling does not regulate draper expression . Rather , we find injury-induced draper activation is downstream of the Draper/Src42a/Shark/Rac1 engulfment signaling pathway . Thus , PI3K signaling and Stat92E are critical in vivo regulators of glial responsiveness to axonal injury . We provide evidence for a positive auto-regulatory mechanism whereby signaling through the injury-responsive Draper receptor leads to Stat92E-dependent , transcriptional activation of the draper gene . We propose that Drosophila glia use this auto-regulatory loop as a mechanism to adjust their reactive state following injury .
Glial cells are extraordinarily sensitive to disruptions in central nervous system ( CNS ) homeostasis and exhibit an impressive ability to respond to a diversity of neural injuries including hypoxia , chemical insults , and mechanical injury ( e . g . , axotomy or traumatic brain injury ) [1]–[3] . During injury responses , glia exhibit robust changes in gene expression , migrate or extend membranes to sites of trauma , and phagocytose degenerating neuronal debris . Glial reactive responses after injury can be beneficial and promote recovery . For example , glial clearance of degenerating neuronal debris is thought to suppress nervous system inflammation and facilitate remyelination [4]–[8] . However , glia can also exacerbate damage in the CNS by driving inflammation through the release of pro-inflammatory cytokines and actively destroying healthy cells [9]–[14] . Whether reactive gliosis is ultimately more beneficial or harmful to the nervous system remains an open question and is likely context dependent . However , mounting evidence points to reactive glial cells as an excellent target for therapeutically modifying CNS disorders [15] . Reactive glial cells have been studied in a variety of neurodegenerative diseases and injury models but surprisingly little is known about how glial sensitivity to neuronal health is initially established in the healthy brain , or how glial perception of neuron-derived “injury signals” is translated into dynamic reactive responses after trauma . A wealth of studies indicate that reactive gliosis is not an all-or-none response , but rather includes a wide range of graded responses that correlate with the severity of nervous system injury [15] . The ability of glia to “measure” the severity of trauma in the nervous system and respond accordingly implies a tight functional coupling between signaling pathways sensing neural injury and those executing glial reactive responses [16] . In some cases reactive glia can modulate the intensity of reactive gliosis in an autocrine way . For example , focal demyelination leads to activation and release of endothilin-1 ( ET-1 ) from astrocytes , which in turn can signal back to astrocytes ( through ET-1 receptors ) and further enhance astrocyte cellular hypertrophy , proliferation , and GFAP expression [17]–[22] . However , the initial injury signal which activates ET-1 expression remains to be identified . An intriguing possibility is that so-called “eat me” cues on degenerating neuronal debris directly activate receptors on glial cells , which in turn modulate initiation of reactive gliosis , but evidence for such molecular regulation is lacking . We have previously shown that Draper , a glial-expressed immunoreceptor-like molecule , is a key surface receptor required for glial engulfment of degenerating axons after axotomy in the adult Drosophila brain [23]–[25] . Draper is the Drosophila ortholog of CED-1 , an engulfment receptor that is essential for engulfment of cell corpses in Caenorhabditis elegans [26] . Within hours after axotomy of Drosophila antennal olfactory receptor neuron ( ORN ) axons , Draper protein , and mRNA levels are dramatically increased in glia surrounding degenerating axons . Glial membranes are then recruited to severed axons , glia phagocytose axonal debris , and ultimately glia return to a resting state [24] , [27] . Loss of Draper function blocks all glial morphological and molecular responses to axonal injury and axonal debris lingers in the brain for the life of the animal . Similar phenotypes have been observed when the components of the Src-family kinase signaling cascade that acts downstream of Draper ( i . e . , Shark and Src42a ) are eliminated specifically from glial cells [25] . These data argue that Draper acts very early in the activation of Drosophila glia after axonal injury , perhaps even in the recognition of cues presented by engulfment targets like degenerating axons . Recently the mammalian orthologs of CED-1/Draper , MEGF10 , and Jedi , have been implicated in satellite glial engulfment of neuronal cell corpses in developing mouse dorsal root ganglia [28] and MEGF10 has been shown to engulf pruned synapses in the postnatal dorsal lateral geniculate nucleus during activity-dependent synaptic refinement [29] . Thus , Draper/MEGF10/Jedi engulfment signaling appears to be a conserved feature of glial cells in evolutionarily distant species . To further explore the molecular mechanisms by which glial cells establish competence to respond to axonal injury and dynamically regulate reactive responses , we performed an in vivo RNAi screen for novel signaling molecules required for glial engulfment of degenerating ORN axons in Drosophila . We identified PI3K signaling and the Stat92E transcription factor as important regulators of glial Draper expression . Interestingly , both of these pathways were necessary for the expression of Draper in the resting , uninjured brain . However , while PI3K signaling was dispensable for injury-induced up-regulation of Draper , we found that Stat92E was necessary in glial cells for both injury-induced up-regulation of Draper and clearance of degenerating axonal debris . Surprisingly , we find that Stat92E acts downstream of Draper to activate transcription . We propose a simple model for glial activation after axotomy whereby Draper signaling is stimulated in a graded fashion according to the level of axonal debris ( i . e . , the severity of axonal injury ) , which in turn promotes Stat92E-dependent changes in glial gene expression in a way that is proportional to the strength of Draper pathway signaling . Such a mechanism places levels of glial activation directly downstream of the total amount of axonal debris present in the adult brain .
In order to identify new glial engulfment genes we performed an in vivo RNAi screen using a previously established assay [24] . Briefly , ∼300 candidate engulfment genes were knocked down specifically in glia by driving UAS-regulated RNAi constructs with the glial specific repo-Gal4 driver . To assay the ability of glia to clear degenerating axonal debris we labeled a subset of maxillary palp ORNs with green fluorescent protein ( GFP ) , severed the axons by surgically removing the maxillary palps , and assayed for clearance of GFP-labeled ORN axonal debris at various timepoints . Interestingly , we found glial-specific knockdown of pi3k92e , raptor , or pdk-1—key components of the phosphoinositide 3-kinase ( PI3K ) signaling pathway—led to a decrease in glial clearance of axonal debris 5 days after axotomy ( Figure 1A and 1B; Data S1 ) . Similar results were found when we drove glial expression of a dominant-negative version of PI3K92e ( UAS-pi3k92eDN; Figure S1; Data S2 ) . We note that while axon clearance was delayed in these backgrounds , ultimately all axonal debris was cleared from the brain 7–10 days after axotomy ( unpublished data ) , indicating that glia exhibited a delay in clearance rather than a blockade . To further explore the cellular basis for this delayed axon clearance phenotype , we assayed glial expression of the engulfment receptor Draper . Surprisingly , glial knockdown of pi3k92e , raptor , or pdk1 , or over expression of pi3k92eDN reduced Draper expression significantly in the uninjured brain ( Figures 1C and S1; Data S3 ) . Reductions in Draper protein levels were confirmed and quantified on Western blots ( Figures 1D , 1E , and S1; Data S3 ) . Reciprocally , we found that over-expression of a constitutively activate version of PI3K92E ( UAS-pi3k92eCAAX ) in glia led to a dramatic increase in Draper levels in uninjured brains ( Figure 1C–1E; Data S3 ) . Thus , Draper expression was tightly correlated with glial PI3K signaling levels in the healthy uninjured brain . To our knowledge this is the first pathway shown to modulate the establishment of Draper expression levels in glia . While maxillary palps house ∼60 ORN cell bodies and ablation of maxillary palps results in a modest increase in Draper protein expression , more severe injury of ORN axons by removal of antennae ( which house ∼600 ORN cell bodies ) results in a dramatic up-regulation of Draper protein and mRNA levels [24] , [27] . To determine whether this response was normal when the PI3K pathway was compromised , we ablated antennae in control , pi3k92eRNAi , raptorRNAi , and pdk1RNAi backgrounds . Despite the fact that knockdown of pi3k92e , raptor , or pdk1 significantly reduced Draper levels in the brains of uninjured control animals , we found that antennal ORN axotomy induced significant up-regulation of Draper levels in glia surrounding the antennal lobe ( Figure 1C ) . While it is possible that this injury-induced Draper up-regulation could be the result of incomplete RNAi mediated knockdown of components of the PI3K signaling pathway , on the basis of the consistency in results among the different components of the pathway we favor the notion that basal levels of Draper ( i . e . , those present in the healthy brain ) are regulated by PI3K signaling and injury-induced up-regulation of Draper is regulated by alternate signaling pathways . Our observations that basal and injury induced Draper expression were controlled by distinct molecular pathways prompted us to attempt to identify draper gene enhancer elements responsible for establishing basal levels of draper expression in adult brain glia , and/or increasing draper expression specifically after ORN axotomy . We focused our search on an ∼40 kb region centered around the draper locus ( Figure 2A ) . We cloned nine different potential draper enhancer elements ( termed dee2-dee10 ) from 5′ , intronic , or 3′ regions of the draper gene into the Gal4-based pBGW vector [30] and inserted these elements into identical genomic locations ( Figure 2A ) . Each dee-Gal4 line was then used to drive two copies of UAS-mCD8::GFP in vivo and expression patterns were examined in the adult brain before and after injury . No expression in ensheathing or cortex glia was observed with any of the enhancer element lines in the healthy , uninjured brain ( unpublished data ) . We therefore failed to identify any single enhancer element that was capable of driving glial expression of reporters in the adult brain in a pattern similar to endogenous Draper protein . This observation suggests that PI3K-dependent regulation of Draper levels might be governed by an enhancer element some distance from the draper gene , requires the convergent activity of multiple enhancers along the draper gene , or could be controlled through post-transcriptional mechanisms . To determine whether any of these potential DEEs were responsive to axotomy , we ablated antennae or maxillary palps and assayed reporter activation in glia one day after injury . We did not observe glial expression after axonal injury with dee2-6- or dee8-10-Gal4 lines ( unpublished data ) . However , one day after antennal ORN axotomy , we observed a striking increase in glial expression of mCD8::GFP in the dee7-Gal4 reporter background , and GFP levels were further increased four days after axon injury ( Figure 2B ) . We note that in uninjured animals we observed low level expression of this element in astrocyte-like glia , which were randomly distributed in the neuropil ( Figure 2B ) but it did not drive GFP expression prior to axon injury in ensheathing or cortex glia , those adult brain glia which normally express Draper [23] . Interestingly , not only was injury-induced reporter expression strong in ensheathing glia surrounding the antennal lobe—those glia that normally engulf degenerating ORN axons [23]—but the reporter expression also increased robustly in cortex glia throughout the brain ( Figure 2B and 2D ) . This widespread activation of the reporter supports the notion that glia , even at locations distant from the injury site , can respond molecularly to axonal damage . Notably , we found the activation of the dee7-Gal4 element appeared to scale with the severity of the axonal injury: when we ablated one maxillary palp ( ∼60 ORNs ) , two maxillary palps ( ∼120 ORNs ) , one antenna ( ∼600 ORNs ) , or both antennal ( ∼1 , 200 ORNs ) , we observed a correlated increase in dee7-Gal4-driven mCD8::GFP ( Figure 2D ) . We further note that ablation of maxillary palps , whose axons are found in the maxillary nerve and ventro-medial regions of the antennal lobe , resulted in a much reduced and more localized increase in dee7-Gal4-driven mCD8::GFP in cortex glia located in the ventral region of the antennal lobe ( Figure 2D ) . Sequence analysis of the 2619 bp dee7 element led to the discovery of three consensus Stat92E binding sites ( TTC3n/4nGAA ) [31] , the sole member of the signal transducer and activator of transcription ( STAT ) family of molecules in Drosophila [31] , [32] . Of these three Stat92E sites , two were also present in dee6-Gal4 , which was not responsive to axonal injury ( Figure 2A ) . We mutated the Stat92E binding site specific to the dee7-Gal4 element , integrated this dee7MUT-Gal4 construct into the same genomic location used for the previously generated reporter lines , and examined its responsiveness to axonal injury . While baseline levels of mCD8::GFP expression were similar to dee7-Gal4 , dee7MUT-Gal4 exhibited an ∼30%–40% decrease in the injury-induced expression of mCD8::GFP at both 1 and 4 days after axotomy ( Figure 2B and 2C; Data S4 ) . In contrast , simultaneous mutation of the two other Stat92E binding sites within the dee7 element had no effect on transcriptional activation of the dee7-Gal4 reporter ( Figure S2A–S2C; Data S5 ) . From these data we conclude that dee7 contains a glial transcriptional regulatory element that is responsive to axonal injury , and our data suggest that at least one Stat92E binding site is required for maximal activation of this element after axotomy . We next sought to determine whether Stat92E was required for modulating glial responses to axonal injury and clearance of degenerating axonal debris . Stat92E function was knocked down specifically in glia by expressing a UAS-stat92eRNAi construct with the pan-glial repo-Gal4 driver . In controls the majority of GFP+ maxillary palp ORN axon material was cleared from the brain 5 days after injury ( Figure 3A–3C; Data S6 ) . However , in stat92eRNAi animals , GFP+ axonal debris persisted 5 days after injury ( Figure 3A–3C; Data S6 ) . In contrast to depletion of the PI3K signaling cascade , glial stat92eRNAi suppressed clearance of antennal ORN axons even 15 days after injury ( Figure S3A ) , indicating an essential requirement for STAT92E in glial clearance of axonal debris . We were able to confirm the UAS-Stat92ERNAi line efficiently targets stat92e as glial co-expression of a GFP-tagged stat92e molecule with the stat92eRNAi construct eliminated all Stat92E-GFP expression compared to controls ( Figure S3B ) . These observations argue that Stat92E is an important regulator of glial engulfment activity in the adult brain . On the basis of our identification of a Stat92E-dependent injury-responsive element in the draper gene , we predicted Stat92E would modulate glial phagocytic activity by regulating draper expression after axotomy . Draper is normally expressed in ensheathing and cortex glia throughout the uninjured brain and is up-regulated around the antennal lobe after antennal ORN axotomy ( Figure 3D ) . Glial-specific knockdown of Stat92E led to nearly undetectable levels of Draper expression in the adult brain even prior to injury ( Figure 3D and 3E; Data S7 ) . We confirmed this widespread loss of Draper by performing Western blots on dissected adult brains from control , stat92eRNAi , and draperRNAi animals ( Figures 3F , 3G , and S3; Data S8 ) . In contrast to loss of PI3K signaling , knockdown of Stat92E in glia was also sufficient to potently suppress glial activation of draper after axotomy: stat92eRNAi animals exhibited no detectable increase in Draper levels after antennal ablation compared to controls . In addition , while Draper was localized specifically to severed ORN axons after maxillary palp ablation in controls , we found no detectable Draper localization to severed axons in stat92eRNAi animals ( Figure 3D and 3E; Data S7 ) . These data suggest that Stat92E is required to establish normal basal levels of Draper expression in glia , and dynamically regulates increased Draper levels as glia respond to axotomy . STAT signaling is involved in multiple cellular processes including cell survival , differentiation , motility , and immunity [33]–[42] . To exclude the possibility that the defects we observed in stat92eRNAi animals were the result of abnormalities in glial cell development we used the conditional Gal80ts system to specifically activate stat92eRNAi at adult stages . When temperature sensitive stat92eRNAi animals were raised and tested at 18°C , we found that glia efficiently cleared axonal debris and expressed normal levels of Draper ( Figure S4; Data S9 ) . However , when they were shifted to and tested at the restrictive temperature during adult stages ( thereby activating the RNAi construct only after development was complete ) , we found that stat92eRNAi animals exhibited reduced expression of Draper and failed to clear degenerating axons ( Figure S4; Data S9 ) . Glial cell morphology ( visualized with membrane-tethered GFP ) and numbers ( counted with α-Repo antibody nuclear staining ) appear grossly normal in these animals , arguing that these phenotypes are not the result of glial cell loss in stat92eRNAi backgrounds ( Figure S5A and S5B ) . Moreover , we found that adult-specific activation of the RNAi was reversible , as shifting these animals back to 18°C ( thereby turning the RNAi off ) re-established normal levels of Draper and initiated clearance of axonal debris ( Figure S4; Data S9 ) . Thus Stat92E functions in adult brain glia , where it modulates Draper expression and glial phagocytosis of degenerating axons . Draper and the PTB domain-containing protein dCed-6 are both required for glial engulfment of degenerating axons and are expressed in glial cells in the adult brain [23] . To determine whether Stat92E broadly regulates engulfment gene expression we assayed dCed-6 levels in the adult brain in animals expressing stat92eRNAi in glia and found that dCed-6 was still present at high levels throughout the brain ( Figure S5C and S5D ) . Interestingly , while dCed-6 levels appeared grossly normal in a stat92eRNAi background , dCed-6 was not recruited to severed maxillary palp axons 1 day after axotomy ( Figure S5C ) . Thus , while Stat92E is necessary for dCed-6 recruitment to severed axons ( i . e . , glial responses to injury ) , basal levels of dCed-6 do not appear to be regulated by a Stat92E dependent mechanism . Finally , we sought to determine whether Draper levels were regulated transcriptionally by STAT92E and/or PI3K signaling . We performed quantitative real-time PCR to measure draper transcript levels in dissected brains from control animals and animals expressing glial draperRNAi , pi3k92eCAAX ( gain-of-function ) , pi3k92eRNAi , or stat92eRNAi . Consistent with STAT92E regulating draper at the transcriptional level , we found that glial RNAi for stat92e severely reduced draper transcripts to a level comparable to depletion with draperRNAi ( Figure 3H; Data S10 ) . In contrast , neither loss- or gain-of-function manipulation of PI3K92E resulted in a statistically significant difference in draper transcript levels . While this argues for a post-transcriptional mechanism of Draper regulation by PI3K signaling , we cannot rule out the possibility that PI3K at least partially regulates draper at the transcriptional level considering loss of PI3K resulted in a trend of decreased ( ∼40% ) draper mRNA ( Figure 3H; Data S10 ) . To explore the dynamics of Stat92E signaling in adult brain glia we examined the expression patterns of transcriptional reporters for Stat92E activity [43] . These reporters have been previously shown to accurately reflect Stat92E transcriptional activity during development as well as in the adult [43]–[46] . We first used the 10XStat92E-GFP , which harbors ten Stat92E binding sites driving expression of enhanced GFP . In co-stains with α-Draper and α-Repo ( a glial nuclear marker ) we observed quite specific glial expression of the Stat92E reporter in uninjured controls ( Figure 4A ) . Moreover , after ablation of antennae we found strong GFP labeling of antennal lobe glia , and the GFP signal completely overlapped with Draper ( Figure 4A ) . After ablation of maxillary palps we found GFP signals co-localized with Draper in glomeruli housing severed axons ( Figure 4A ) . These data argue that Stat92E is active at a transcriptional level in adult brain glia . The GFP driven by the 10XStat92E-GFP reporter is quite stable and can perdure in cells for ∼20 hours after activation , which precludes our use of this construct to examine dynamic changes in Stat92E transcriptional activity . We therefore used a second reporter , 10XStat92E-dGFP , which drives the expression of a rapidly degraded , destabilized GFP ( dGFP ) , thereby allowing for increased temporal resolution of Stat92E activity . Prior to injury , we were unable to detect any activation of this Stat92E transcriptional reporter in adult brains ( Figure 4B ) . However , beginning ∼16 hours after antennal ablation we detected 10XStat92E-dGFP expression in cells surrounding the antennal lobe ( Figure 4B , arrows ) . GFP intensity peaked at ∼24 hours after antennal ablation and disappeared by 48 hours after axotomy ( Figure S6 ) . To confirm that activation of the 10XStat92E-dGFP reporter after axotomy is Stat92E-dependent and glial specific , we knocked down Stat92e specifically in glia , severed axons , and assayed 10XStat92E-dGFP activity . We found that glial-specific knockdown of Stat92E completely suppressed the axotomy-induced activation of the 10XStat92E-dGFP transcriptional reporter ( Figure 4B ) . Consistent with our observations of widespread activation of the dee7-Gal4 driver in glia throughout the brain after injury , we also found that axonal injury led to broad activation of the 10XStat92E-dGFP reporter in glial cells ( Figure 4C ) and expression of these two reporters colocalize in glial cells after axotomy ( Figure 4D ) , indicating they are active in the same cells . Together these data indicate that Stat92E can transiently increase the transcriptional activation of target genes in glial cells throughout the brain after axonal injury , with the strongest increases in target gene activation occurring adjacent to injury sites . STAT activity is generally regulated by the JAK signaling platform , and this pathway is conserved in all higher metazoans . The Drosophila JAK/STAT signaling pathway consists of a single JAK molecule , Hopscotch ( hop ) [47] , and the cytokine like receptor Domeless ( Dome ) [48] . To determine if Stat92E-dependent activation of draper is mediated through canonical JAK/STAT signaling we drove RNAi constructs targeted against hop , and a dominant negative Domeless molecule , DomelessΔCYT [49] , in glial cells , and assayed Draper expression and clearance of severed axons . Surprisingly , in each of these backgrounds we found Draper levels were similar to control animals and axons were efficiently cleared 5 days after injury ( Figure S7A and S7B; Data S11 ) . Reciprocally , we found a gain-of-function allele of hop , hopTUM , which has been shown in numerous assays to activate Stat92E transcriptional activity [43] , [50] , [51] , failed to activate the10XStat92E-dGFP reporter in adult brain glia ( Figure S7C ) . Notably , expression of an activated version of Stat92E , Stat92EΔNΔC [52] , led to strong activation of the 10XStat92E-dGFP reporter ( Figure S7C ) . Stat92EΔNΔC has previously been shown to require phosphorylation at Y711 for activation [52] . We therefore expressed a version of Stat92EΔNΔC with a Y711F mutation in glia , and found it was insufficient for activation of the 10XStat92E-dGFP reporter ( Figure S7C ) . Together these data argue that while phosphorylated Stat92E mediates activation of Stat92E reporters in glia , canonical JAK/STAT signaling is neither necessary nor sufficient to activate Stat92E-dependent glial responses to axon injury . To date Domeless is the only receptor known to positively regulate Stat92E signaling under normal physiological conditions [48] , [53] , [54] . Considering we were unable to demonstrate a role for Domeless signaling in activation of Draper after injury , we sought to determine whether Draper itself might have a role in modulating glial gene expression . We assayed 10XStat92E-dGFP activation after axonal injury in draperΔ5 null mutants and , intriguingly , loss of Draper resulted in a complete lack of 10XStat92E-dGFP activation after axotomy ( Figure 5A ) . Consistent with a direct requirement for Draper signaling in STAT92E-dependent activation of draper after axotomy , we also found a lack of activation of the dee7-Gal4 reporter after axonal injury in draperΔ5 animals ( Figure 5B ) . These data demonstrate a direct role for the Draper receptor in modulating Stat92E-dependent changes in glial gene expression following local axonal injury . We next explored whether other identified components of the Draper signaling pathway modulate Stat92E transcriptional activity after axon injury . Draper is thought to be phosphorylated by Src42a upon activation , initiating binding of the non-receptor tyrosine kinase Shark , which together , with Rac1 and dCed-6 , promote engulfment [23] , [25] . Interestingly , we found that glial-specific knockdown of Shark , Src42a , or Rac1 blocked injury-induced activation of the10XSTAT92E-dGFP transcriptional reporter ( Figures 5A and S8A ) . This finding was confirmed using multiple RNAi lines and/or dominant negative alleles for each gene in the pathway ( Figure S8A ) . However , while RNAi mediated knockdown of dCed-6 efficiently eliminated dCed-6 immunoreactivity ( Figure S8B ) , axotomy-induced activation of 10XSTAT92E-dGFP was still detectable in dCed-6RNAi animals ( Figure 5A ) . Thus Draper , Src42a , Shark , and Rac1 , but not dCed-6 , are essential for Stat92E-dependent activation of transcriptional targets in glia responding to axonal injury . These are the first data that demonstrate a functional divergence between dCed-6 and Draper/Src42a/Shark function during engulfment signaling . Since Src42a is known to signal downstream of Draper and has been shown to mediate Stat92E signaling in multiple contexts [25] , [55] , [56] , we sought to determine whether Src42a activity was sufficient to activate Stat92E transcriptional reporters . Indeed , expression of a constitutively active Src42a molecule ( Src42aCA ) resulted in robust 10XStat92E-dGFP reporter activation throughout brain ( Figure 5C , refer to Figure 4B for control ) . Based on our previous genetic studies it appears that Src42a acts through modulating Rac1 activity . We therefore drove glial expression of a constitutively active Rac1 molecule ( Rac1v12 ) and found this also robustly activated the 10XStat92E-dGFP reporter throughout the adult brain ( Figure 5C , refer to Figure 4B for control ) . These data are consistent with the notion that Src42a acts in glia downstream of Draper to activate Stat92E signaling through Rac1 after axonal injury . In summary , our data suggest that axonal injury leads to Stat92E-dependent transcriptional up-regulation of draper through a Draper/Src42a/Shark/Rac1-dependent signaling cascade . While activation of Stat92E , Src42A , or Rac1 led to robust activation of the 10XStat92E reporter , none were sufficient to increase basal Draper levels ( unpublished data ) , suggesting additional transcriptional inputs are required for injury-induced transcriptional activation of draper . Together our data argue that draper transcription is regulated by Stat92E after axotomy . Stat92E may regulate many genes after axotomy , or only a few critical targets essential for engulfment . To further explore the relationship between Stat92E and the draper gene , we over-expressed Draper in a stat92eRNAi background to determine whether resupplying Draper was sufficient to overcome the engulfment deficit observed in Stat92E knockdown animals . Remarkably , over-expression of Draper in stat92eRNAi animals led to a near complete rescue of the engulfment defect ( Figure 6A and 6B; Data S12 ) . As an alternate method to increase Draper levels we also drove glial expression of the activated PI3K molecule , PI3K92eCAAX , in the presence of stat92eRNAi or control draperRNAi animals . We found that activation of PI3K signaling was sufficient to increase Draper levels ( Figures 6E , 6F , and S9; Data S13 ) and rescue engulfment defects in stat92ERNAi backgrounds ( Figure 6C and 6D; Data S14 ) . Consistent with the ability of PI3K signaling to drive Draper expression independently of Stat92E , we also found that activated PI3K signaling was not sufficient to activate expression of the 10XStat92edGFP reporter ( Figures 6E , 6F , and S9 ) . However , PI3K signaling appeared to sensitize brain glia to injury as glial PI3K92eCAAX enhanced 10XStat92E-dGFP activation after antennal ablation compared to controls ( Figure S9 ) , perhaps through driving increased Draper levels . From these data we conclude that draper is a critical target of Stat92E during glial responses to axonal injury . In addition , our data argue that activated PI3K signaling results in Stat92E-independent increases in Draper levels .
Our work identifies two new signaling pathways important for regulating glial engulfment function in vivo . First , we show that the PI3K signaling pathway modulates Draper levels in the healthy , uninjured brain as reduced PI3K signaling leads to dramatically decreased glial Draper and constitutive activation of PI3K signaling leads to Draper up-regulation . However , depletion of PI3K signaling components delays but does not completely block the ability of glial cells to up-regulate Draper levels or clear axonal debris in response to axotomy , perhaps due to positive signaling through the small amount of Draper that remains under these conditions . Second , we identify Stat92E as a potent regulator of both basal and injury induced Draper levels in adult brain glia . Loss of Stat92E in mature glia results in significantly decreased draper transcript levels and a near complete loss of Draper protein in the uninjured brain . What is the relationship between PI3K signaling and Stat92E in regulating basal levels of Draper in the healthy brain ? On the basis of our analysis of draper mRNA levels we speculate that STAT92E regulates draper at least in part at the transcriptional level . It appears unlikely that Stat92E functions downstream of PI3K signaling since loss of Stat92E in a constitutively activated PI3K background did not suppress PI3K-dependent increases in Draper levels , and gain-of-function PI3K was sufficient to rescue reduced Draper levels and engulfment defects in stat92eRNAi animals . STAT molecules have been shown to be capable of acting as adaptor molecules for receptors that ultimately lead to activation of PI3K signaling [57]–[59] , therefore Stat92E could function in adult brain glia upstream of PI3K signaling in a non-transcriptional manner to regulate basal levels of Draper . Finally , Stat92E might transcriptionally regulate key molecules required for activation or execution of PI3K signaling . In such a situation Stat92E and PI3K signaling could modulate Draper levels through parallel mechanisms , but both would be required for expression of appropriate levels of Draper in the adult brain . A role for PI3K signaling in phagocytic function appears to be conserved from Drosophila to mammals . Activation of PI3K signaling occurs downstream of the Fcγ receptor [60]–[63] . This finding is intriguing in light of the fact that Draper appears to act as an ancient immunoreceptor , activating a Src family kinase signaling cascade through ITAM/ITIM-dependent mechanisms [25] , [27] . PI3K signaling is required for efficient formation of the phagocytic cup in macrophages [61] , [64] , [65] , and loss of PI3K has been reported to lead to a delay in clearance of cell corpses and myelin [62] , [66] , [67] . This latter finding argues for conservation of a requirement for PI3K signaling even among glial cell types in Drosophila and mammals . In our study we also find a delay of axonal clearance when PI3K signaling is suppressed , but ultimately axons are cleared . We speculate this phenotype is a result of decreased expression of Draper since we can fully rescue loss of PI3K signaling phenotypes by resupplying Draper . This observation argues strongly that a key role for PI3K signaling in phagocytic function is the regulation of engulfment factors , and in particular Draper . Ablation of Drosophila third antennal segments leads to axotomy of all antennal ORNs and Wallerian degeneration of ∼1 , 200 ORN axons and their synapses within the antennal lobe of the brain . We previously observed a robust increase in Draper levels in glia surrounding the antennal lobe after antennal injury ( but not elsewhere ) and proposed that Draper increases were only local [23] , [24] . Using the dee7-Gal4 and 10XStat92E-dGFP reporters we have now shown that severe axonal injury ( i . e . , axotomy of nearly all ORNs ) in the antennal lobe is sufficient to induce a transcriptional response in glial cells throughout the entire Drosophila brain ( Figures 2B–2D , 4B–4D , 5A , and 5B ) . How can glial cells in distant parts of the brain receive signals that an injury has occurred ? We can envision at least two scenarios to explain this observation . First , severed axons could release signals that act at a long distance to activate glia . If so , the reception of this axon-derived signal by glia would be Draper-dependent , since we show that Draper signaling is required for activation of both the dee7-Gal4 and 10XStat92E-dGFP reporters in glia after injury . Alternatively , spreading of an injury signal throughout the brain could be accomplished by glial to glial signaling . Astrocytes are indeed heavily coupled in mammals [68] and the same could be true in Drosophila adult brain glia where signals could spread through gap junction-dependent mechanisms . In the future it will be very exciting to define the molecules that regulate the spreading of the injury signal to distant glial subtypes , and determine their functional role in brain recovery from trauma . In mammals , it is widely accepted that reactive glial responses are graded according to the severity of the brain injury . Here we show that Drosophila glia also respond in a graded way to ORN injury: axotomy of a small number of ORN axons by maxillary palp ablation led to dee7-Gal4 activity in a small subset of cells , while severing the majority of ORNs ( ∼85% ) by antennal ablation led to a more dramatic increase in the activation of two separate reporters . We propose that relatively mild injuries promote signaling through the Draper pathway in a limited number of cells close to the site of injury while more severe injuries result in widespread activation of the Draper pathway in cells throughout the entire brain , even those distant from the injury site . Presumably up-regulation of engulfment factors enhances the ability of glia to clear neuronal debris ( Figure 7 ) . Such a mechanism whereby glial transcriptional responses are activated downstream of the very pathways that drive glial phagocytic activity would allow glia to directly modulate their engulfment capacity . Since it is likely that Draper ligands are present on engulfment targets , transcriptional activation of glial engulfment genes would ultimately be regulated by extracellular levels of “eat me” cues on degenerating axons . We propose a novel injury-induced auto-regulatory loop whereby activation of Draper in glial cells responding to axonal injury leads to downstream signaling through Src42a/Shark/Rac1 , and in turn Stat92E-dependent transcriptional activation of the draper locus . We provide multiple lines of evidence that activation of glial cells after local axotomy involves Stat92E-dependent transcriptional activation of draper: RNAi knockdown of Stat92E leads to a loss of injury-induced Draper expression , mutation of a single Stat92E binding site in the dee7 enhancer element leads to ∼40%–50% reduction in dee7-Gal4 activity upon injury , the 10XStat92E-dGFP transcriptional reporter is activated in glial cells upon injury , and activation of both 10XStat92E-dGFP and the dee7-Gal4 reporter are lost in draper null animals . We provide compelling evidence that the dee7 reporter activity resembles injury induced transcriptional activation of Draper: it expresses in the subtype of glia known to respond to injury and only after axotomy; it correlates with our data showing a larger increase in overall Draper up-regulation in the brain during the more severe antennal ablation; expression of the reporter is lost in drpr mutant animals; and its expression overlaps with the 10XStatDGFP reporter activity . As with all transcriptional reporters it remains to be determined whether it fully recapitulates endogenous draper regulation after injury , nevertheless , the dee7-Gal4 element will be an extremely useful tool with which to dissect pathways mediating axon injury-induced molecular changes in glial transcription . Finally , based on the presence of STAT92E binding sites in the draper locus , and the requirement of these for maximal activation of the injury-responsive dee7-Gal4 element , we suspect the draper gene may be a direct transcriptional target of STAT92E after axotomy . Since mutation of the STAT92E binding sites in the dee7 element only led to a partial inhibition of dee7 activity after injury , it seems likely that Stat92E acts in cooperation with other factors . Evidence from mammals suggests that based on their weak transcriptional activation , STAT proteins often act in combination with other transcription factors for maximal target gene activation . Interestingly , we recently showed JNK signaling is required in glial cells for up-regulation of Draper after axotomy and that transcriptional reporters for JNK pathway signaling to dAP-1 increased in glia after axonal injury [69] . Mammalian STAT and AP-1 act together in reactive microglial cells to mediate the up-regulation of inflammatory proteins [70] , [71] . It therefore remains an interesting possibility that combinations of Stat92E binding sites , AP-1 binding sites , and binding sites for other unidentified co-factors are involved in precisely modulating levels of Draper expression in the context of injury . Our work has revealed a novel role for engulfment signaling pathways ( i . e . , Draper signaling ) in the regulation of glial transcription after local axon injury , and revealed that Stat92E acts downstream . Despite the fact that Stat92E signaling is essential for a wide range of developmental processes in Drosophila , Draper is only the second receptor implicated in positively regulating the ability of Stat92E to activate transcriptional targets in vivo [48] , [53] , [54] . At the same time our work identifies the draper gene as a novel target for Stat92E—only three other genes , eve , crb , and dome , have been shown by in vivo analyses to be directly regulated by Stat92E [72]–[74] . Our work therefore enriches our understanding of the physiological roles for engulfment receptors and Stat92E signaling in the modulation of glial responsiveness to axonal injury . We anticipate a similar role for mammalian engulfment receptors and STATs in reactive gliosis . Indeed , activation of STAT molecules in mammalian glial cells ( determined primarily by using phospho-STAT-specific antibodies ) has been widely reported in response to focal brain lesion [75] , [76] , traumatic brain injury [77] , [78] , and spinal cord injury ( SCI ) [79] . STAT3 is a critical modulator of reactive gliosis: loss of STAT3 from astrocytes in the context of SCI has been shown to lead to attenuated activation of GFAP expression , a lack of astrocyte hypertrophy , and reduced formation of the glial scar . This in turn results in increased demyelination , enhanced inflammation , and less efficient motor axon recovery [80] , [81] . IL-6 has been shown to act upstream of STAT3-dependent GFAP induction in Schwann cells during peripheral nerve regeneration , but both the source of IL-6 in vivo and how STATs are activated in glia after injury remains unclear [82] . Based on this study we speculate mammalian STATs may be activated in reactive glia by the Draper orthologs MEGF10 or Jedi .
The following fly strains were used: ( 1 ) UAS-pdk1 , VDRC 18736 , ( 2 ) UAS-raptorRNAi , VDRC 13112 , ( 3 ) UAS-pi3k92eRNAi , VDRC 38986 , ( 4 ) UAS-pi3k92ecaax , Bloomington Stock 8294 ( 5 ) UAS-mCD8::GFP [83] ( II ) , ( 6 ) UAS-mCD8::GFP [83] ( III ) , ( 7 ) OR85e-mCD8::GFP [84] , ( 8 ) UAS-stat92eRNAi , VDRC 43866 , ( 9 ) UAS-drprRNAi [24] ( 10 ) repo-Gal4 , ( 11 ) tubulin-Gal80ts , ( 12 ) repo-Gal4 , UAS-mCD8::GFP , ( 13 ) 10XStat92E-GFP [43] , ( 14 ) 10XStat92E-GFP [43] , ( 15 ) UAS-rac1V12 , Bloomington Stock 6291 , ( 16 ) UAS-Src42aCA , Bloomington Stock 6410 , ( 17 ) UAS-src42aRNAi VDRC 26019 ( 18 ) UAS-src42aRNAi VDRC 100708 , ( 19 ) UAS-rac1RNAi VDRC 49247 , ( 20 ) UAS-rac1N17 , Bloomington Stock 6292 , ( 21 ) UAS-sharkRNAi [25] ( 22 ) UAS-sharkRNAi , VDRC 105706 ( 23 ) UAS-Drpr-I [27] , ( 24 ) drprΔ5 , [24] , ( 25 ) UAS-mCherry , ( 26 ) and yw . The dee7-Gal4 construct was made by PCR amplification of the 2 , 619 bp fragment from the Draper BACR17K18 clone using the following primers: forward 5′caccagacctactcttagctctgatggagg-3′ , reverse 5′-gtttgtgtttccatggattcaggcttggg-3′ . The PCR product was purified using a Qiagen Gel Purification kit , directionally cloned into the Invitrogen pENTR/D-TOPO vector and transformed into One Shot Competent Cells using the pENTR/D-TOPO Cloning Kit ( Invitrogen catalog number K2400-20 ) . Colonies were prepped using the Qiagen miniprep kit . The dee7 fragment was then shuttled into the pBGUw destination vector [15] using the Invitrogen Gateway LR Clonase Enzyme and transformed into heat shock competent DH5α cells . The construct was sequence verified and transgenic flies were generated by Best Gene Inc . using the PhiC31 targeted integration system . To generate the dee7MUT-GAL4 construct , the dee7/TOPO construct was used as a template and PCR was carried out using Invitrogen Quick Change II Site-Directed Mutagenesis kit ( catalog number 200523 ) with the following primers ( mutation sites underlined ) : forward-5′ CTG TGC CGA ACA CGT TAA CCA TTG AAA AAT CTC GC 3′ , reverse-5′ GCG AGA TTT TTC AAT GGT TAA CGT GTT CGG CAC AG 3′ . A DpnI digestion was performed and DNA was transformed into XL-1 Blue super competent cells and plated . Colonies were prepped using the Qiagen miniprep kit and the mutations were verified by sequencing ( Genewiz ) . The dee7MUT enhancer fragment was then shuttled into the pBGUw vector using methods described above and transgenic flies were generated by Best Gene Inc . using PhiC3 targeted integration . Maxillary palp and third antennal segment ablations , adult brain dissections , and antibody stainings were performed using previously described methods [24] , [85] . Samples were mounted in Vectashield ( Vector Laboratories ) antifade reagent and viewed on a Zeiss LSM5 Pascal confocal microscope . In all experiments , laser settings were kept identical for all brains imaged as part of that experiment . For Figure 2C , single slice confocal images of approximately the same depth in the brain were identified and total intensity of GFP in the brain was measured . The minimum threshold was set at 3 to eliminate most background and the maximum threshold was set at the maximum value of 255 . Measures of GFP intensity within maxillary palp glomeruli were performed as previously described [24] . For Figure 3E , Draper expression after maxillary palp injury was measured from single confocal slices at the depth of the OR85e-innervated glomerulus . A circle was drawn around the area of the OR85e+ innervated glomerulus and total intensity of Draper was measured . Draper expression after antennal injury was measured from single confocal slices about half way through the antennal lobe . A fixed area rectangle at the edge of the antennal lobe was used to measure total intensity of Draper . All quantification of measurements was performed using Image J software . The following antibodies were used: 1∶200 mouse anti-GFP ( Invitrogen ) , 1∶500 rabbit anti-Draper [86] , 1∶500 rat anti-dCed6 [87] , 1∶200 FITC anti-mouse IgG , 1∶200 Cy3 anti-rabbit IgG , 1∶200 Cy3 anti-rat IgG ( Jackson ImmunoResearch ) . Flies were raised and maintained at 25°C unless otherwise noted . In experiments utilizing Gal80ts flies were raised at 18°C and shifted to 30°C for at least 7 days prior to injury or dissection . Drosophila brains of the indicated genotype were dissected in PBS and homogenized in SDS loading buffer ( 60 mM Tris [pH 6 . 8] , 10% glycerol , 2% SDS , 1% mercaptoethanol , 0 . 01% bromophenol blue ) . For Western analysis , samples containing approximately three brains were loaded onto 10% SDS-PAGE gels ( BioRad ) , transferred to nitrocellulose membranes ( BioRad ) , and probed with rabbit α-Draper antibody [86] at 1∶1 , 000 diluted in PBS/0 . 01% Tween-20/5% BSA . Blots were incubated overnight at 4°C , washed several times in PBS/0 . 01% Tween-20 , and probed with the appropriate HRP conjugated secondary antibody for 2 hours at room temperature . Additional washes were performed and the blot was developed using chemiluminescence ( Amersham ECL Plus ) , and detected with a Fujifilm Luminescent imager . The protein blot was stripped with mild stripping buffer ( 0 . 2M glycine , 0 . 1% sodium dodecyl sulfate , 1% Tween [pH 2 . 2] ) at room temperature followed by washes in 1× PBS and 1× PBS+0 . 01% Tween-20 and then reprobed with mouse-tubulin ( Sigma ) , 1∶1 , 000 . Drosophila central brain regions were dissected in Jan's saline ( 1 . 8 mM Ca2+ ) and immediately frozen on dry ice . Total RNA was extracted in Trizol and the aqueous phase was passed over an Omega Bio-Tek E . Z . N . A MicroElute RNA Clean-Up Column with an on-column DNAase I treatment ( Omega ) . RNA concentration was determined on a Nanodrop 2000c spectrometer ( Thermo Scientific ) . RNA was diluted to equal concentration and 125 ng of total RNA was reverse-transcribed with the SuperScript VILO cDNA synthesis Kit for 2 h at 42°C . Relative quantification of gene expression was carried out on an ABI 7000 Real-Time PCR machine . The following Taqman assays ( Applied Biosystems ) were used: ( i ) Ribosomal protein L32 ( ABI pre-made assay Dm02151827_g1 ) ( ii ) Draper-I custom assay , F-primer , TGTGATCATGGTTACGGAGGAC; R-primer , CAGCCGGGTGGGCAA; probe , CGCCTGCGATATAA [27] . Assay efficiencies were experimentally determined ( RpL32 , 102%; Draper-I , 103%; using a 5-point dilution series of cDNA spanning a 20-fold range in concentration . The raw threshold cycle ( Ct ) of the normalization control ( RpL32 ) did not vary by more than 0 . 5 cycles across all time points analyzed . Statistical analysis ( ANOVA , with Dunnett's Multiple Comparisons Test ) was performed on 2−ΔCt values . Draper Ct values were normalized to ribosomal protein L32 and results are presented as fold induction relative to uninjured levels .
|
Acute injuries of the central nervous system ( CNS ) trigger a robust reaction from glial cells—a non-neuronal population of cells that regulate and support neural development and physiology . Although this process occurs after all types of CNS trauma in mammals , how it is activated and its precise role in recovery remain poorly understood . Using the fruit fly Drosophila melanogaster as a model , we previously identified a cell surface receptor called Draper , which is required for the activation of glia after local axon injury ( “axotomy” ) and for the removal of degenerating axonal debris by phagocytosis . Here , we show that regulation of Draper protein levels and glial activation through the Draper signaling pathway are mediated by the well-conserved PI3K and signal transducer and activator of transcription ( STAT ) signaling cascades . We find that STAT transcriptional activity is activated in glia in response to axotomy , and identify an injury-responsive regulatory element within the draper gene that appears to be directly modulated by STAT . Interestingly , the intensity of STAT activity in glial cells after axotomy correlates tightly with the number of local severed axons , indicating that Drosophila glia are able to fine-tune their response to neuronal injury according to its severity . In summary , we propose that the initial phagocytic competence of glia is regulated by setting Draper baseline levels ( via PI3K ) , whereas injury-activated glial phagocytic activity is modulated through a positive feedback loop that requires STAT-dependent activation of draper . We speculate that the level of activation of this cascade is determined by glial cell recognition of Draper ligands present on degenerating axon material , thereby matching the levels of glial reactivity to the amount of injured axonal material .
|
[
"Abstract",
"Introduction",
"Results",
"Discussion",
"Materials",
"and",
"Methods"
] |
[
"neuroglial",
"development",
"molecular",
"neuroscience",
"developmental",
"neuroscience",
"cellular",
"neuroscience",
"neural",
"homeostasis",
"biology",
"and",
"life",
"sciences",
"neuroscience"
] |
2014
|
PI3K Signaling and Stat92E Converge to Modulate Glial Responsiveness to Axonal Injury
|
Many problems of cooperation involve repeated interactions among the same groups of individuals . When collective action is at stake , groups often engage in Public Goods Games ( PGG ) , where individuals contribute ( or not ) to a common pool , subsequently sharing the resources . Such scenarios of repeated group interactions materialize situations in which direct reciprocation to groups may be at work . Here we study direct group reciprocity considering the complete set of reactive strategies , where individuals behave conditionally on what they observed in the previous round . We study both analytically and by computer simulations the evolutionary dynamics encompassing this extensive strategy space , witnessing the emergence of a surprisingly simple strategy that we call All-Or-None ( AoN ) . AoN consists in cooperating only after a round of unanimous group behavior ( cooperation or defection ) , and proves robust in the presence of errors , thus fostering cooperation in a wide range of group sizes . The principles encapsulated in this strategy share a level of complexity reminiscent of that found already in 2-person games under direct and indirect reciprocity , reducing , in fact , to the well-known Win-Stay-Lose-Shift strategy in the limit of the repeated 2-person Prisoner's Dilemma .
The emergence and sustainability of cooperation constitutes one of the most important problems in social and biological sciences [1] . It revolves around the clash between individual and collective interest , which becomes particularly clear when one considers the evolution of collective action involving Public Goods Games ( PGG ) , such as the stereotypical N-person Prisoner's Dilemma ( NPD ) [2] , [3] . In the absence of additional mechanisms , such as the presence of thresholds [4] , [5] , risk [6] , an embedding network of interactions [7]–[12] , institutions [13]–[15] , punishment or voluntary participation [16]–[19] , evolutionary game theory predicts a population fated to fall into a tragedy of the commons [20] . Collective action problems , however , often involve repeated interactions between members of the same group [21]–[23] , as exemplified by the repeated attempts from country leaders to cooperate in reducing emissions of greenhouse gases [6] , [24]–[29] or in finding a solution to the Euro monetary crisis [30]–[32] . In such scenarios , where collective action is more difficult to achieve in larger groups [6] , one is naturally led to question whether a generalization of the direct reciprocity [33] mechanism to problems of collective action may provide an escape hatch to the aforementioned tragedy of the commons . Moreover , N-player interactions pose many additional difficulties , in particular in what concerns the emergence of reciprocation: If one interacts repeatedly in a group of N-players it is hard to identify towards whom should one reciprocate [3] . In fact , only recently direct reciprocity has been generalized to PGGs [22] , [23] , studying the co-evolution of unconditional defectors with generalized reciprocators , that is , individuals who , in a group of size N , only cooperate if there were at least M ( 0≤M≤N ) individuals who cooperated in the previous round . Results show [22] , [23] that generalized reciprocators are very successful in promoting cooperation . Moreover , for a given group size N , there is a critical threshold level of fairness , M* , at which reciprocation optimizes the emergence of cooperation [22] . Generalized reciprocators [22] provide an intuitive generalization of the TFT strategy to repeated N-player games . However , and despite the underlying intuition , they constitute but a small subset of all possible individual ( reactive ) strategies one can envisage in a group of size N . Here we explore the complete set of reactive strategies that individuals may adopt when engaging in repeated Public Goods Games with N-1 other individuals , assuming that the decision to cooperate or not is based on the behavioral decisions of the group in the previous round ( see below ) . We find that , in the context of Public Goods Games , a reactive strategy not belonging to the set of generalized reciprocators emerges as ubiquitous , ensuring the emergence and sustainability of cooperation .
Let us consider a finite and well-mixed population of Z individuals , who assemble in groups of size N randomly formed , and play a repeated version of the NPD [34] . In each round individuals either cooperate ( C ) by contributing an amount c to a public good or defect ( D ) by not doing so . The aggregated contributions of the group are multiplied by an enhancement factor F and equally divided among the N individuals of the group . Hence , in each round , Ds achieve a payoff of , while Cs attain where k is the number of contributions in that round . We consider a repeated PGG with an undetermined number of rounds , such that at the end of each round , another round will take place with probability w [3] , leading to an average number of rounds — m — given by m = ( 1−w ) −1 . At the beginning of each round ( with the exception of the first ) , each individual decides to contribute ( i . e . to play C ) or not ( i . e . to play D ) , depending on the total number of contributions that took place in the previous round . Each strategy Si defines how an individual behaves in each round ( i . e . if she/he decides to cooperate or defect ) and is encoded in a string with N+2 bits ( b−1b0b1…bN−1bN ) . The first bit ( b−1 ) dictates the behavior in the initial round , while the remaining N+1 bits ( b0b1…bN−1bN ) correspond in sequence to the player's behavior depending on the number of Cs in the previous round . In this definition a bit 1 corresponds to a cooperative act and a bit 0 to a defective one . Hence , one obtains a maximum of 2N+2 strategies , corresponding to all possible combinations of 0 s and 1 s in a string of size N+2 . We consider groups of N individuals , randomly sampled from a finite population of size Z , playing a repeated NPD . Individuals revise their strategies through the Fermi update rule [35]–[38] , a stochastic birth-death process with mutations . At each time step a randomly selected individual A ( with strategy SA and fitness ) may adopt a different strategy i ) by mutation with probability μ or ii ) by imitating a random member B of the population ( with strategy SB and fitness ) with probability , where β is the intensity of selection that regulates the randomness of the decision process . The fitness of each strategy is the average payoff attained over all rounds and possible groups by individuals adopting strategy Si . It is well known that execution errors profoundly affect the evolutionary dynamics of repeated 2-person games [39]–[45] . Consequently , we shall also consider that , in each round , and after deciding to contribute or not according to bq , an individual may act with the opposite behavior ( 1−bq ) with a probability ε , thus making an execution error .
Let us start by investigating the evolutionary dynamics of the population in the small mutation limit approximation [46] . This allows us to compute analytically the relative pervasiveness of each strategy in time . It is noteworthy , however , that the results obtained through this approximation remain valid for a wide range of mutation probabilities , as we show explicitly in the Supporting Information ( SI ) via comparison with results from computer simulations . In a nutshell , and whenever mutations are rare , a new mutant that appears in the population will either get extinct or invade the entire population before the occurrence of the next mutation . Hence , in each time-step there will be , at most , 2 strategies present in the population , which allows one to describe the evolutionary dynamics of the population in terms of an embedded ( and reduced ) Markov Chain with a size equal to the number of strategies available . Each state represents a monomorphic population adopting a given strategy , whereas transitions are defined by the fixation probabilities of a single mutant [47] . The resulting stationary distribution τi will then indicate the fraction of time the population spends in each of the 2N+2 states ( or strategies Si ) . We shall also make use of τi to compute the fraction of time the population spends in a configuration/strategy with biq = 1 , a quantity we call stationary bit strategy , defined as , where corresponds to the bit q of strategy i . The stationary bit strategy allows us to easily quantify the relative dominance of each behavior and extract the most pervasive strategic profiles . Figure 1 shows the stationary bit distribution , , for different group sizes . Colored cells highlight those bits ( bq ) that retain the same value more than 75% of the time , with ≥0 . 75 ( blue ) and ≤0 . 25 ( red ) . For simplicity , we associate this feature with what we call dominant bit . Analysis of the stationary bit distributions for different group sizes under small error probabilities puts into evidence the overall evolutionary success of strategies that conform with a particular profile: b0 = bN = 1 and bq = 0 for 0<q<N . A similar trend is obtained if instead we analyze the stationary distribution τi for all possible strategies Si: This strategy — or minor variations on this profile ( see below ) — shows the highest prevalence for a wide range of parameters even in the absence of errors of execution ( see SI ) . The philosophy encapsulated in this strategy is a simple yet efficient one: cooperating only after a round of unanimous group behavior ( cooperation or defection ) . Hence we refer to this strategy as All-Or-None ( AoN ) , highlighting the two situations in which these individuals are prone to cooperate . As group size increases , so does the number of expected errors per round , which leads to an overall reduction of the number of dominant bits found in the intermediate sector ( i . e . bq for 0<q<N ) without affecting the “edge bits” , which again reveals the prevalence of AoN behaviour in the population . A monomorphic population of AoN players can easily sustain unanimous group cooperation , even in the presence of errors . Indeed , after an occasional individual defection , a round of full defection ensues , resuming back to unanimous cooperation in the following round . Therefore , AoN allows a prompt recovery from errors of execution , which constitutes a key feature that allows cooperation to thrive . To investigate the robustness of AoN we show , in Figure 2 , the effect of execution errors on the stationary bit distribution ( ) for a fixed group size ( here N = 5 ) : Clearly , both b0 and bN remain associated with cooperation for a wide range of error probabilities ( ε≤0 . 2 ) . The internal bits , in turn , remain qualitatively close to the AoN profile ( i . e . bq = 0 for 0<q<N ) , undergoing changes as the error rate increases , allowing an efficient resume into full cooperation , after ( at least ) one behavioral error . In particular , for 0 . 01<ε<0 . 1 , evolution selects for defection in bits b1 to bN−1 , with particular incidence to adjacent bits of b0 and bN , allowing a fast error recovery . This feature gets enhanced with increasing ε . For larger values of ε ( ε>0 . 1 ) , unanimity becomes less likely and we witness an adaptation of the predominant strategy that acts to reduce the interval of bits in which defection prevails . In other words , it is as if execution errors redefine the notion of unanimity itself or , alternatively , individuals become more tolerant as execution errors become more likely . It is also noteworthy that the non-monotonous response to errors shown in Figure 2 has been previously observed in other evolutionary models of cooperation [48] where intermediate degrees of stochasticity emerge as maximizers of cooperation . We confirmed that the results remain qualitatively equivalent for different group sizes . In the following we investigate the relevant issue of asserting whether the introduction of this strategy can efficiently promote the average fraction of cooperative actions . The level of cooperation , η , may be defined as the average number of contributions per round divided by the maximum number of contributions possible . Denoting by Ci the average number of contributions per round associated with strategy Si , η reads , where τi is the fraction of time the population spends in the configuration Si and N is the group size . As shown in Figure 3 , the overall levels of cooperation remain high as long as the average number of rounds is sizeable ( left panel , for different values of the PGG enhancement factor F ) . The success of AoN can also be inferred by assessing its evolutionary chances when interacting with unconditional defectors ( AllD ) . To do so , we compute the gradient of selection [5] — G ( k ) — which provide information on the most likely direction of change of the population configuration with time . This is given by the difference between the probabilities of increasing and decreasing the number of AoN players in a population of k AoNs and Z-k AllDs . The result is depicted in the right panel of Figure 3 , a profile characteristic of a coordination game , in which case the AoN strategy dominates whenever the population accumulates a critical fraction of AoN players . Moreover , the size of coordination barrier decreases with increasing values of the enhancement factor F . In the SI we further show that the location of the coordination point is rather insensitive to other game parameters , in particular when the number of rounds is large . Notably , the evolutionary chances of the AoN strategy remain qualitatively independent from alterations on the first bit ( b−1 ) . Similarly , we have checked the robustness of the AoN strategy when interacting with random strategists ( RS ) , i . e . , individuals that cooperate or defect with equal probability . It can be shown that both AoN and AllD are advantageous with respect to RS strategists ( regardless of their prevalence in the population ) , while these should drive AllC to extinction . Finally , contrary to the generalized versions of TFT strategies , in the presence of errors , the AoN strategy is robust to invasion of unconditional cooperators ( AllC ) by random drift , as the former can efficiently exploit the latter . To sum up , we have shown that the strategy AoN emerges as the most viable strategy that leads to the emergence of cooperation under repeated PGGs . This strategy , despite its remarkable simplicity , cannot be encoded within the subspace of generalized reciprocators studied before in this context [22] . When we consider individuals capable of making behavioral errors , AoN is dominant as suggested by analyzing both the stationary bit strategy ( Figures 1 and 2 ) and the stationary distribution in the monomorphic configuration space ( SI ) . More importantly , our results suggest that AoN dominates independently of the group size and over a wide range of error rates . Previous works have identified similar strategy principles in different contexts . For instance , the Win-Stay-Lose-Shift [39]–[41] , [49] strategy discovered in the context of the repeated 2-person Prisoner's Dilemma constitutes the N = 2 limit of AoN . In the context of repeated N-Person games on the multiverse [34] , the strategy entitled generic Pavlov [50] encapsulates a behavioral principle which is similar to that underlying AoN . In fact , one may argue that the principles underlying AoN may very well be ubiquitous: The simplicity of this strategy can be seen as equivalent — in the context of problems of collective action [5] , [6] , [14] involving Public Goods Games — to the simplicity of tit-for-tat or Win-Stay-Lose-Shift strategies discovered in the context of 2-person direct reciprocity , or the stern-judging social norm of indirect reciprocity [51] . In these cases , we observe a fine balance between strict replies towards defective actions and prompt forgiving moves , allowing the emergence of unambiguous decision rules ( or norms ) that may efficiently recover from past mistakes . Thus , despite the inherent complexity of N-person interactions and the individual capacity to develop complex strategies , it is remarkable how evolution still selects simple key principles that lead to widespread cooperative behaviors .
|
The problem of cooperation has been a target of many studies , and some of the most complex dilemmas arise when we deal with groups repeatedly interacting by means of a Public Goods Game ( PGG ) , where individuals may contribute to a common pool , subsequently sharing the resources . Here we study generalized direct group reciprocity by incorporating the complete set of reactive strategies , where action is dictated by what happened in the previous round . We compute the pervasiveness in time of each possible reactive strategy , and find a ubiquitous strategy profile that prevails throughout evolution , independently of group size and specific PGG parameters , proving also robust in the presence of errors . This strategy , that we call All-Or-None ( AoN ) , consists in cooperating only after a round of unanimous group behavior ( cooperation or defection ) ; not only is it conceptually very simple , it also ensures that cooperation can self-sustain in a population . AoN contains core principles found , e . g . , in the repeated 2-person Prisoner's Dilemma , in which case it reduces to the famous Win-Stay-Lose-Shift strategy .
|
[
"Abstract",
"Introduction",
"Models",
"Results/Discussion"
] |
[
"ecology",
"and",
"environmental",
"sciences",
"statistical",
"mechanics",
"applied",
"mathematics",
"simulation",
"and",
"modeling",
"systems",
"science",
"mathematics",
"adaptive",
"systems",
"population",
"modeling",
"altruistic",
"behavior",
"stochastic",
"processes",
"evolutionary",
"emergence",
"research",
"and",
"analysis",
"methods",
"complex",
"systems",
"computer",
"and",
"information",
"sciences",
"behavior",
"agent-based",
"modeling",
"population",
"ecology",
"dynamical",
"systems",
"nonlinear",
"dynamics",
"probability",
"theory",
"behavioral",
"ecology",
"game",
"theory",
"physics",
"psychology",
"computer",
"modeling",
"ecology",
"interdisciplinary",
"physics",
"biology",
"and",
"life",
"sciences",
"physical",
"sciences",
"computational",
"biology",
"evolutionary",
"biology",
"evolutionary",
"processes",
"collective",
"human",
"behavior"
] |
2014
|
Evolution of All-or-None Strategies in Repeated Public Goods Dilemmas
|
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